# HG changeset patch # User laurenmarazzi # Date 1640188834 0 # Node ID 7e5c71b2e71f0e420992be9ae1bcd83c19f31e5c # Parent f24d4892aaed5ad8adb35088b21b058ed72e859b Uploaded diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/._.DS_Store Binary file tools/myTools/._.DS_Store has changed diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/10_internal_control_node_analysis.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/10_internal_control_node_analysis.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,38 @@ + +Extracts the steady-state values of internal-marker nodes in the attractors from perturbations whose attractors passed filtering criterion 1 + + + pandas + sys + + + python3 '$__tool_directory__/bin/get_RONs_getperts.py' '$pert_attrs' '$icn' '$crit_perts' + + + + + + + + + + + + + + + + + + + + + + This tool extracts the steady-state values of internal-marker nodes in the attractors from perturbations whose attractors passed filtering criterion 1. + Required Inputs: + 1. Perturbation Attractors + 2. Perturbation Attractors IDs that shifted to a different state/cluster + 3. Internal Control Nodes + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/11_filtering_by_icn.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/11_filtering_by_icn.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,58 @@ + +Filters perutrbations on FVS control nodes whose steady-state values of internal-marker nodes are within the gene expression range of the desired attractor. + + + plyr + dplyr + reshape2 + readr + + + Rscript '$__tool_directory__/bin/crit2.R' '$exp_attrs_icn' '$samples' '$desired_state' '$undesired_state' '$filter_type' '$crit_perts' crit2_perturbations.txt + + + + + + + + + + + + + + + + + + + + + + + + + + + + + This tool Filters perutrbations on FVS control nodes whose steady-state values of internal-marker nodes are within the gene expression range of the desired attractor. + Required Inputs: + 1. Experimental Attractors of Internal Control Markers + 2. Experimental Samples of Interest + 3. Perturbation Attractors that shifted to a different state/cluster + 4. Sample Name of Desired State + 5. Sample Name of Undesired State + 6. Strict or Relaxed Criterion + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/12_extract_perts.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/12_extract_perts.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,39 @@ + +Extracts the combinations of perturbations on FVS control nodes that pass filtering criterion 2 + + + pandas + sys + + + python3 '$__tool_directory__/bin/get_perts.py' '$fvs_init' '$fvs' '$filtered_perts' + + + + + + + + + + + + + + + + + + + + + + + This tool extracts the combinations of perturbations on FVS control nodes that pass filtering criterion 2. + Required Inputs: + 1. Random Perturbation States for Feedback Vertex Set + 2. Feedback Vertex Set + 3. Perturbation Attractors that successfully shifted the network from undesired to desired state + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/13_translate_perts.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/13_translate_perts.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,32 @@ + +Translates combinations of perturbations on FVS control nodes into readable format. + + + dplyr + ggplot2 + + + Rscript '$__tool_directory__/bin/pertanalysis.R' '$perts' successful_controlnode_perturbations.txt + + + + + + + + + + + + + + + + + + This tool translates combinations of perturbations on FVS control nodes into readable format + Required Inputs: + 1. Successful Perturbations that shifted network from undesired to desired state + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/1_sfa_exp.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/1_sfa_exp.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,45 @@ + + + Estimates attractors from experimental data using Signal Flow Analysis. + + + os + numpy + pandas + networkx + random + sfa + csv + sys + + + python3 '$__tool_directory__/bin/SFA_exp_attr.py' '$network' '$expression_data' exp_attrs.csv + + + + + + + + + + + + + + + + + + + + This tool estimates attractors for each experimental sample from normalized expression data using Signal Flow Analysis. + Required Inputs: + 1. network in sif format (activating edges as "activates", inhibitory edges as "inhibits") + 2. normalized expression values for data sample + + + + 10.1038/s41598-019-50790-0 + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/2_get_exp_internal_control_nodes.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/2_get_exp_internal_control_nodes.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,36 @@ + + + Extracts Internal-marker node values from attractors generated from experimental data + + + pandas + sys + + + python3 '$__tool_directory__/bin/get_RONs.py' '$attrs_exp' '$icn' + + + + + + + + + + + + + + + + + + + + This tool extracts Internal-marker node values from attractors generated from experimental data. + Required Inputs: + 1. output from netisce step 1 + 2. internal control nodes in txt format + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/3_insilico_attrs.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/3_insilico_attrs.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,52 @@ + +Estimates the attractors for randomly generated initial states using signal flow analysis + + + pandas + numpy + os + networkx + random + sys + csv + sfa + statistics + + + python3 '$__tool_directory__/bin/SFA_insilico.py' '$network' '$expression_data' '$samples' '$num_states' + + + + + + + + + + + + + + + + + + + + + + + + This tool estimates the attractors for randomly generated initial states using signal flow analysis. + Required Inputs: + 1. network in sif format (activating edges as "activates", inhibitory edges as "inhibits") + 2. normalized expression values for data sample + 3. experimental sample names of interest + 4. number of insilico Attractors to generate (default = 10,000) + + + + 10.1038/s41598-019-50790-0 + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/4_getFVS.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/4_getFVS.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,36 @@ + +Computes the minimal Feedback Vertex Set of the network + + + networkx + random + FVS_python3 + re + csv + sys + + + python3 '$__tool_directory__/bin/FVS_run.py' '$network' fvs.txt + + + + + + + + + + + + + + + + + + This tool computes the minimal Feedback Vertex Set using Control Systems theory and the topological features of the network. + Required Inputs: + 1. network in sif format (activating edges as "activates", inhibitory edges as "inhibits") + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/5_perturbation_inits.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/5_perturbation_inits.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,40 @@ + +Generates combinations of perturbations for FVS control nodes + + + numpy + pandas + os + itertools + csv + sys + + + python3 '$__tool_directory__/bin/generate_perts.py' '$fvs' 'pert' '-1,0,1' + + + + + + + + + + + + + + + + + + + + + This tool Generates combinations of perturbations for FVS control nodes. + Required Inputs: + 1. minimal Feedbfack Vertex Set in txt format (from 4_getFVS) + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/6_sfa_perts.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/6_sfa_perts.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,58 @@ + +Computes the attractors from perturbations on FVS control nodes + + + numpy + pandas + os + networkx + random + sfa + sklearn.preprocessing + csv + sys + + + python3 '$__tool_directory__/bin/SFA_virtscreen.py' '$network' '$expression_data' '$init' '$samples' '$fvs' '$fvs_init' + + + + + + + + + + + + + + + + + + + + + + + + + + + + This tool computes Computes the attractors from perturbations on FVS control nodes. + Required Inputs: + 1. network in sif format (activating edges as "activates", inhibitory edges as "inhibits") + 2. normalized expression values for data sample (rows are expressed genes, columns are samples) + 3. name of undesired initial state/experimental sample + 4. name of all experimental samples of interest + 5. minimal Feedback Vertex set of the network + 6. randomly generated perturbations of FVS Set + + + + 10.1038/s41598-019-50790-0 + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/7_check_icns.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/7_check_icns.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,38 @@ + +Checks the values of internal-marker nodes in attractors generated from experimental data + + + plyr + dplyr + ggplot2 + reshape2 + readr + + + Rscript '$__tool_directory__/bin/icn_check1.R' '$attrs_exp' '$samples' + + + + + + + + + + + + + + + + + + + + This tool checks the values of internal-marker nodes in attractors generated from experimental data + Required Inputs: + 1. name of all experimental samples of interest + 2. experimental attractors + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/8_kmeans.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/8_kmeans.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,46 @@ + +This tool computes the optimal k value using Elbow and Silhouette Metric and clusters the attractors generated from experimental data and random initial states to the k clusters + + + pandas + scipy + matplotlib + matplotlib.pyplot + sklearn.decomposition + numpy + sklearn.datasets + sklearn.cluster + yellowbrick.cluster.elbow + sys + os + + + python3 '$__tool_directory__/bin/kmeans_full.py' '$experimental_attractors','$random_attractors' + + + + + + + + + + + + + + + + + + + + + + + This tool computes the optimal k value using Elbow and Silhouette Metric and clusters the attractors generated from experimental data and random initial states to the k clusters. + Required Inputs: + 1. Experimental Attractors of all samples of interest, Insilico Attractors of all samples of interest + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/9_class_and_consensus.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/9_class_and_consensus.xml Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,45 @@ + +classifies perturbation attractors to k clusters with SVM, Naive Bayes and Random Forest + and then extracts the perturbation attractors that shifted to the cluster associated to the desired phenotype by to 2/3 classification methods. + + + pandas + collections + sys + sklearn.naive_bayes + sklearn.svm + sklearn.ensemble + + + python3 '$__tool_directory__/bin/class_and_consensus.py' '$experimental_attractors','$random_attractors' '$pert_attrs' '$kmeans' + + + + + + + + + + + + + + + + + + + + + + + Classifies perturbation attractors to k clusters with SVM, Naive Bayes and Random Forest + and then extracts the perturbation attractors that shifted to the cluster associated to the desired phenotype by to 2/3 classification methods. + Required Inputs: + 1. Training Set: Experimental and Insilico Attractors + 2. Test Set: Perturbation Attractors + 3. K-mean classifications of training set attractors + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/.Rhistory --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/.Rhistory Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,512 @@ +} +}else { #filter=="relaxed" +for (node in nodes){ +if (disc[node]==0){ +val=min(dfundes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]% select(-ID,-perturbation,-replicate) +isdisc<-is +if (filter=="strict"){ +for (node in nodes){ +if (disc[node]==0){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=min(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=min(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]%filter(hammingdist<1) +}else{ +hams1_1<-hammings %>%filter(hammingdist<=length(nodes)-round(.9*length(nodes))) +} +exp<-read.delim('exp_internalmarkers.txt',sep=" ",row.names = 1) +samples<-read.delim("test-data/samples.txt",sep="\t",row.names = 1) +exp<-exp %>% dplyr::filter(row.names(exp) %in% row.names(samples)) +desired<-"sensitive" +undesired<-"resistant" +filter<-"strict" +dfdes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% desired,,drop=FALSE])) +meandes<- apply(dfdes,2,mean) +dfundes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% undesired,,drop=FALSE])) +meandundes<-apply(dfundes,2,mean) +test<-meandes-meandundes +disc<-test +disc[disc<0]<-0 +disc[disc>0]<-1 +nodes<-colnames(dfdes) +is<-read.delim("internal_markers.txt",sep=" ") +is$ID<-paste(is$replicate,is$perturbation,sep="_") +row.names(is)<-is$ID +is<- is %>% select(-ID,-perturbation,-replicate) +isdisc<-is +if (filter=="strict"){ +for (node in nodes){ +if (disc[node]==0){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=min(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]%filter(hammingdist<1) +}else{ +hams1_1<-hammings %>%filter(hammingdist<=length(nodes)-round(.9*length(nodes))) +} +poi<-Reduce(intersect,list(hams1_1$perturbations)) +write.table(poi,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE,col.names = c('pert')) +write.table(poi,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +exp<-read.delim('exp_internalmarkers.txt',sep=" ",row.names = 1) +samples<-read.delim("test-data/samples.txt",sep="\t",row.names = 1) +exp<-exp %>% dplyr::filter(row.names(exp) %in% row.names(samples)) +desired<-"sensitive" +undesired<-"resistant" +filter<-"strict" +dfdes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% desired,,drop=FALSE])) +meandes<- apply(dfdes,2,mean) +dfundes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% undesired,,drop=FALSE])) +meandundes<-apply(dfundes,2,mean) +test<-meandes-meandundes +disc<-test +disc[disc<0]<-0 +disc[disc>0]<-1 +nodes<-colnames(dfdes) +is<-read.delim("internal_markers.txt",sep=" ") +is$ID<-paste(is$replicate,is$perturbation,sep="_") +row.names(is)<-is$ID +is<- is %>% select(-ID,-perturbation,-replicate) +isdisc<-is +if (filter=="strict"){ +for (node in nodes){ +if (disc[node]==0){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=min(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]% dplyr::filter(row.names(exp) %in% row.names(samples)) +desired<-"sensitive" +undesired<-"resistant" +filter<-"strict" +dfdes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% desired,,drop=FALSE])) +meandes<- apply(dfdes,2,mean) +dfundes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% undesired,,drop=FALSE])) +meandundes<-apply(dfundes,2,mean) +test<-meandes-meandundes +disc<-test +disc[disc<0]<-0 +disc[disc>0]<-1 +nodes<-colnames(dfdes) +is<-read.delim("internal_markers.txt",sep=" ") +isdisc<-is +if (filter=="strict"){ +for (node in nodes){ +if (disc[node]==0){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=min(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]%filter(hammingdist<1) +}else{ +hams1_1<-hammings %>%filter(hammingdist<=length(nodes)-round(.9*length(nodes))) +} +poi<-Reduce(intersect,list(hams1_1$perturbations)) +write.table(poi,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +View(hammings) +if (length(nodes)<10){ +hams1_1<-hammings %>%filter(hammingdist<1) +}else{ +hams1_1<-hammings %>%filter(hammingdist<=length(nodes)-round(.9*length(nodes))) +} +View(hams1_1) +poi<-Reduce(intersect,list(hams1_1$perturbations)) +write.table(poi,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +source('~/Dropbox/NETISCE_galaxy/new_bin/crit2-update.R') +exp<-read.delim('exp_internalmarkers.txt',sep=" ",row.names = 1) +samples<-read.delim("test-data/samples.txt",sep="\t",row.names = 1) +exp<-exp %>% dplyr::filter(row.names(exp) %in% row.names(samples)) +desired<-"sensitive" +undesired<-"resistant" +filter<-"strict" +dfdes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% desired,,drop=FALSE])) +meandes<- apply(dfdes,2,mean) +dfundes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% undesired,,drop=FALSE])) +meandundes<-apply(dfundes,2,mean) +test<-meandes-meandundes +disc<-test +disc[disc<0]<-0 +disc[disc>0]<-1 +nodes<-colnames(dfdes) +is<-read.delim("internal_markers.txt",sep=" ") +isdisc<-is +if (filter=="strict"){ +for (node in nodes){ +if (disc[node]==0){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=min(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]%filter(hammingdist<1) +}else{ +hams1_1<-hammings %>%filter(hammingdist<=length(nodes)-round(.9*length(nodes))) +} +poi<-Reduce(intersect,list(hams1_1$perturbations)) +write.table(poi,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +View(hams1_1) +View(hams1_1) +poi<-Reduce(intersect,list(hams1_1$perturbations)) +poi<-hams1_1 +View(poi) +write.table(poi,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +write.table(hams1_1,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +View(hams1_1) +write.table(hams1_1,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +write.table(as.dataframe(hams1_1),'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +View(hams1_1) +View(hams1_1) +write.table(hams1_1,file='crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +class(hams1_1) +.libPaths('~/R_libs_36') +args = commandArgs(trailingOnly=TRUE) +library(plyr) +library(dplyr) +library(ggplot2) +library(reshape2) +library(readr) +getwd() +exp<-read.delim('exp_internalmarkers.txt',sep=" ",row.names = 1) +samples<-read.delim("test-data/samples.txt",sep="\t",row.names = 1) +exp<-exp %>% dplyr::filter(row.names(exp) %in% row.names(samples)) +desired<-"sensitive" +undesired<-"resistant" +filter<-"strict" +dfdes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% desired,,drop=FALSE])) +meandes<- apply(dfdes,2,mean) +dfundes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% undesired,,drop=FALSE])) +meandundes<-apply(dfundes,2,mean) +test<-meandes-meandundes +disc<-test +disc[disc<0]<-0 +disc[disc>0]<-1 +nodes<-colnames(dfdes) +is<-read.delim("internal_markers.txt",sep=" ") +isdisc<-is +if (filter=="strict"){ +for (node in nodes){ +if (disc[node]==0){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=min(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]%filter(hammingdist<1) +}else{ +hams1_1<-hammings %>%filter(hammingdist<=length(nodes)-round(.9*length(nodes))) +} +poi<-Reduce(intersect,list(hams1_1$perturbations)) +write.table(poi,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +poi<-Reduce(intersect,list(hams1_1$perturbations)) +View(hams1_1) +write.table(hams1_1,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE) +write.delim(hams1_1,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE,sep=" ") +write.table(hams1_1,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE,sep=" ") +#!/usr/bin/env Rscript +.libPaths('~/R_libs_36') +args = commandArgs(trailingOnly=TRUE) +library(plyr) +library(dplyr) +library(reshape2) +library(readr) +getwd() +exp<-read.delim('exp_internalmarkers.txt',sep=" ",row.names = 1) +samples<-read.delim("test-data/samples.txt",sep="\t",row.names = 1) +exp<-exp %>% dplyr::filter(row.names(exp) %in% row.names(samples)) +desired<-"sensitive" +undesired<-"resistant" +filter<-"strict" +dfdes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% desired,,drop=FALSE])) +meandes<- apply(dfdes,2,mean) +dfundes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% undesired,,drop=FALSE])) +meandundes<-apply(dfundes,2,mean) +test<-meandes-meandundes +disc<-test +disc[disc<0]<-0 +disc[disc>0]<-1 +nodes<-colnames(dfdes) +is<-read.delim("internal_markers.txt",sep=" ") +isdisc<-is +if (filter=="strict"){ +for (node in nodes){ +if (disc[node]==0){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=min(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) +} +if (disc[node]==1){ +val=max(dfdes[,node]) +isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) +isdisc[,node]<-replace(isdisc[,node],is[,node]%filter(hammingdist<1) +}else{ +hams1_1<-hammings %>%filter(hammingdist<=length(nodes)-round(.9*length(nodes))) +} +View(hams1_1) +class(hams1_1) +write.table(hams1_1,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE,sep=" ") +typeof(hams1_1$replicate) +typeof(hams1_1$perturbation) +?lapply +hammings$hammingdist<-lapply(simplify=T,isdisc[,3],function(x) sum(disc != x)) +hammings$hammingdist<-sapply(isdisc[,3],function(x) sum(disc != x)) +if (length(nodes)<10){ +hams1_1<-hammings %>%filter(hammingdist<1) +}else{ +hams1_1<-hammings %>%filter(hammingdist<=length(nodes)-round(.9*length(nodes))) +} +write.table(hams1_1,'crit2_perturbations.txt',quote=FALSE,row.names = FALSE,sep=" ") +write.table(hams1_1[c(1,2),],'crit2_perturbations.txt',quote=FALSE,row.names = FALSE,sep=" ") +View(hams1_1) +View(hams1_1) +write.table(hams1_1[c(1,2),],'crit2_perturbations.txt',quote=FALSE,row.names = FALSE,sep=" ") +write.table(hams1_1[,c(1,2)],'crit2_perturbations.txt',quote=FALSE,row.names = FALSE,sep=" ") diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_python3/FVS.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/FVS_python3/FVS.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,189 @@ +''' +This file contains functions used to calculate feedback vertex set for graph/directed graph. +''' + +import networkx as nx +#import FVS_localsearch_10_cython as FVS10 +import FVS_python3.FVS_localsearch_10_python as FVS10 + +def python_format(Ginput): + ''' + The FVS_local_search function are written based on the fact that the node are named as + sequential integer number from 0 to N-1. This function convert the input graph into a + graph that follows this criteria and can be used by FVS_local_search. The function returns + the formatted graph, this mapping from original name to new name and its inverse mapping. + ''' + G=nx.DiGraph() + N=len(G.nodes()) + i=0 + mapping={} + inverse_mapping={} + for node in Ginput.nodes(): + mapping[node]=i + inverse_mapping[i]=node + i+=1 + new_edgelist=[(mapping[edge[0]],mapping[edge[1]]) for edge in Ginput.edges()] + G.add_edges_from(new_edgelist) + return G,mapping,inverse_mapping + + +def FVS(G, T_0 = 0.6, alpha = 0.99, maxMvt_factor = 5, maxFail = 50, randomseed=None): + ''' + Returns an approximation of minimum Feedback Vertex Set (FVS) for a DiGraph G. + An undirected graph will be converted to a directed graph. + If the input graph is weakly connected, FVS_weakly_connected() will be called. + If the input graph is not weakly connected, i.e. have disconnected components, + FVS_disconnected() will be called. + + + From Wikipedia: + A Feedback Vertex Set of a graph is a set of vertices whose removal leaves a graph without cycles. + In other words, each Feedback Vertex Set contains at least one vertex of any cycle in the graph. + The Feedback Vertex Set problem for directed graph is an NP-complete problem in computational complexity theory. + + This algorithm is a fast approximation based on simulating annealing(SA) of a noval local search strategy [1]_. + The function mainly called a function named FVS_local_search. + FVS_local_search calcualte the longest sub topological ordering of G_input (directed graph) + based on simulated annealing(SA) of local search of topological ordering. + FVS is then G \ the topological ordering. + The code follows the pseudocode given in Page 805 in that paper. + This algorithm is claimed to outperform the existing heuristic algorithm, + greedy adaptive search procedure (GRASP) by Pardalos et al. + + Parameters + ---------- + G : NetworkX Graph/DiGraph, result for MultiGraph is not tested + T_0 : the initial temperature in SA + alpha : the cooling multiplier for temperatue in the geometric cooling regime + maxMvt_factor : maxMvt_factor times network size is the number of iterations for the inner loop given a fixed temperatue + maxFail : FVS_local_search stops when maxFail number of outloops (temperatue) fail to improve the result + randomseed: random seed for generating random numbers + + Returns + ------- + An approximation of the minimum FVS of the given graph as a list. + + + Examples + -------- + #calculate a FVS for a random graph + >>>G1 = nx.gnm_random_graph(6, 12, seed=None, directed=True) + >>>G1_FVS = FVS.FVS(G1) + #calculate a FVS for a toy example + >>>G2 = nx.DiGraph() + >>>G2.add_edges_from([('A','B'),('B','C'),('C','A'),('A','D'),('D','A')]) + >>>G2_FVS = FVS.FVS(G2) + #result should be ['A'] + + Notes + ----- + The code is written by Gang Yang, Department of Physics, Penn State University + + References + ---------- + ..[1] Galinier, P., Lemamou, E. & Bouzidi, M.W. J Heuristics (2013) 19: 797. doi:10.1007/s10732-013-9224-z + + ''' + #make sure input is a directed graph, undirected graph will be converted into a directed graph + if not nx.is_directed(G): + print("Warning: undirected graph is converted to directed graph!") + Gtemp=G.to_directed() + else: + Gtemp=G.copy() + if nx.is_weakly_connected(Gtemp): + result = FVS_weakly_connected(Gtemp,T_0,alpha,maxMvt_factor,maxFail,randomseed) + else: + result = FVS_disconnected(Gtemp,T_0,alpha,maxMvt_factor,maxFail,randomseed) + return result + + + + +def FVS_weakly_connected(G, T_0, alpha, maxMvt_factor, maxFail,randomseed): + ''' + Returns an approximation of minimum Feedback Vertex Set (FVS) for a weakly connected DiGraph G. + This function is part of FVS(). Do not call this function directly. + See more information at FVS(). + + Parameters + ---------- + G : NetworkX DiGraph (required to be weakly connected) + T_0 : the initial temperature in SA + alpha : the cooling multiplier for temperatue in the geometric cooling regime + maxMvt_factor : maxMvt_factor times network size is the number of iterations for the inner loop given a fixed temperatue + maxFail : FVS_local_search stops when maxFail number of outloops (temperatue) fail to improve the result + randomseed: random seed for generating random numbers + + Returns + ------- + An approximation of the minimum FVS of the given graph as a list. + + + Notes + ----- + The code is written by Gang Yang, Department of Physics, Penn State University + + ''' + #test whether it is already a Directed Acyclic Graph + self_loops = [edge[0] for edge in G.edges() if edge[0]==edge[1] ] #all the nodes that are self_loops + Gtemp = G.copy() + Gtemp.remove_nodes_from(self_loops) + if nx.is_directed_acyclic_graph(Gtemp): + return self_loops + #map the network into a format required by FVS_local search + G_formatted, mapping, inverse_mapping = python_format(G) + N = len(G.nodes()) + #maximum iteration times for inner loop + maxMvt = maxMvt_factor*N + #calculate the longest topological order inside the given graph + S_optimal_formatted = FVS10.FVS_local_search(G_formatted, T_0, alpha, maxMvt, maxFail, randomseed) + #map the topological order back to the names in the original graph + S_optimal = [inverse_mapping[node] for node in S_optimal_formatted] + #minimum FVS is all the nodes minus nodes in the topological order + FVS_set = set(G.nodes())-set(S_optimal) + FVS = [node for node in FVS_set] + #check that we do obtain a FVS through checking G\FVS is a Directed Acyclic Graph + Gprime=G.copy() + Gprime.remove_nodes_from(FVS) + assert nx.is_directed_acyclic_graph(Gprime) + return FVS + + +def FVS_disconnected(G, T_0, alpha, maxMvt_factor, maxFail, randomseed): + ''' + Returns an approximation of minimum Feedback Vertex Set (FVS) for a disconnected DiGraph G. + This function is part of FVS(). Do not call this function directly. + See more information at FVS(). + + For each weakly connnected component of the original graph, + call FVS_weakly_connected to calculate FVS of each component. + The result is the union of the FVS of each component. + + + Parameters + ---------- + G : A disconnected NetworkX DiGraph + T_0 : the initial temperature in SA + alpha : the cooling multiplier for temperatue in the geometric cooling regime + maxMvt_factor : maxMvt_factor times network size is the number of iterations for the inner loop given a fixed temperatue + maxFail : FVS_local_search stops when maxFail number of outloops (temperatue) fail to improve the result + randomseed: random seed for generating random numbers + + Returns + ------- + An approximation of the minimum FVS of the given graph as a list. + + + Notes + ----- + The code is written by Gang Yang, Department of Physics, Penn State University + ''' + + wccs = nx.weakly_connected_component_subgraphs(G) + FVS=[] + for wcc in wccs: + FVS_temp = FVS_weakly_connected(wcc, T_0, alpha, maxMvt_factor, maxFail, randomseed) + FVS.extend(FVS_temp) + return FVS + + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_python3/FVS_localsearch_10_python.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/FVS_python3/FVS_localsearch_10_python.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,193 @@ +''' +This file contains functions used to calculate feedback vertex set for directed graph + +The major function is the FVS_local_search(), which calculate maximum sub topological ordering of a graph +The algorithm is given by "Applying local search to feedback vertex set problem" +Author of the paper Philippe Galinier, Eunice Lemamou, Mohamed Wassim Bouzidi +The code mimic the pseudocode given in Page 805 in that paper +The code is written by Gang Yang, Penn State University +''' + + +import networkx as nx +import random +import math +import array + + + +#the following two function calculate the position for the candidate given the existing topological ordering S +def get_position_minus(candidate_incoming_neighbour, S): + ''' + get_position_minus return position as just after its numbered in-coming neighbours + As in the paper, the function return i_minus(v) + ''' + position = 1 + for x in range(len(S)-1,-1,-1): #we loop through index from the end as we try to find the largest index of numbered incoming neighbour + if S[x] in candidate_incoming_neighbour: + position = x+2 #2 comes from we need to put candidate after the incoming neighbour and also the position count from 1 instead of 0 + return position + return position #put the candidate in the first position if there is no numbered incoming neighbour + + + +def get_position_plus(candidate_outgoing_neighbour, S): + ''' + get_position_plus return position as just before its numbered out-going neighbours + As in the paper, the function return i_plus(v) + ''' + position = 1+len(S) + for x in range(len(S)): #we loop through index from the beginning as we try to find the smallest index of numbered outgoing neighbour + if S[x] in candidate_outgoing_neighbour: + position = x+1 #1 comes from the fact position count from 1 instead of 0 + return position + return position #put the candidate in the first position if there is no numbered outgoing neighbour + + + +def FVS_local_search(G_input, T_0, alpha, maxMvt, maxFail, randomseed=None): + ''' + Returns an maximum sub topological ordering of a DiGraph G. + FVS is G_input \ the topological ordering + A topological ordering of a graph is an ordering of vertices such that + the starting point of every arc occurs earlier in the ordering than + the endpoint of the art. + + This algorithm is a fast approximation based on simulating annealing(SA) of a noval local search strategy [1]_. + The code follows the pseudocode given in Page 805 in that paper. + + + Parameters + ---------- + G : NetworkX Graph/DiGraph, result for MultiGraph is not tested + T_0 : the initial temperature in SA + alpha : the cooling multiplier for temperatue in the geometric cooling regime + maxMvt_factor : maxMvt_factor times network size is the number of iterations for the inner loop given a fixed temperatue + maxFail : FVS_local_search stops when maxFail number of outloops (temperatue) fail to improve the result + randomseed: random seed for generating random numbers + + Returns + ------- + An approximation of the maximum ordering of the given graph as a list. + + + Notes + ----- + The code is written by Gang Yang, Department of Physics, Penn State University + + + References + ---------- + ..[1] Galinier, P., Lemamou, E. & Bouzidi, M.W. J Heuristics (2013) 19: 797. doi:10.1007/s10732-013-9224-z + + ''' + #set a random seed + random.seed(randomseed) + G=G_input.copy() + N= len(G.nodes()) #number of nodes in the graph + edges=G.edges() + + + #Initialization + T = T_0 #set initial temperatue + nbFail = 0 #Outer loop counter to record failure times + S = [] #list to record the ascending ordering + S_optimal = [] #list to record the optimal ordering + + + #calculate parent and child node for each node + parent = [{} for i in range(N)] + child = [{} for i in range(N)] + + for edge in edges: + child[int(edge[0])][int(edge[1])]=None + parent[int(edge[1])][int(edge[0])]=None + + #all the nodes that are self_loops + self_loops = [edge[0] for edge in edges if edge[0]==edge[1]] + print(self_loops) + #all the nodes that is not in S + unnumbered=[x for x in range(N) if x not in self_loops] + N_unnumbered = len(unnumbered) + while nbFail< maxFail: + nbMvt = 0 #Inner loop counter to record movement times + failure = True #if cardinal of S increase after one inner loop, failure will be set to false + while nbMvt < maxMvt: + candidate_index = random.randint(0, N_unnumbered-1) + candidate = unnumbered[candidate_index] #random pick a node from all unnumbered node + position_type = random.randint(0,1) #random choose a position type + #calculate incoming/outgoing neighbour for the candidate node, store as keys in the dict + candidate_incoming_neighbour = parent[candidate] + candidate_outgoing_neighbour = child[candidate] + #see how to find the position on Page 803 of the paper + #position_type=1 means just after incoming neighbours + if position_type==1: + position = get_position_minus(candidate_incoming_neighbour,S) + #position_type=0 means just before outgoint neighbours + elif position_type==0: + position = get_position_plus(candidate_outgoing_neighbour,S) + + #first, insert the candidate to the given position + S_trail = S[:] #copy the list + S_trail.insert(position-1,candidate) + + #second remove all the conflict + #break the sequence into two parts: before and after the candidate node and + S_trail_head= S_trail[:position-1] + S_trail_tail= S_trail[position:] + #determine conflict node See page 801 + if position_type==1: + CV_pos=[] #conflict before the newly inserted node in the topological ordering + for x in range(len(S_trail_head)): + nodetemp=S_trail_head[x] + #print nodetemp,candidate_outgoing_neighbour,nodetemp in candidate_outgoing_neighbour + if nodetemp in candidate_outgoing_neighbour: + CV_pos.append(nodetemp) + conflict=CV_pos #there won't be conflict after the inserted node as the node inserted after its incoming neighbour + elif position_type==0: + CV_neg=[] #conflict after the newly inserted node in the topological ordering + for x in range(len(S_trail_tail)): + nodetemp=S_trail_tail[x] + #print nodetemp,candidate_incoming_neighbour,nodetemp in candidate_incoming_neighbour + if nodetemp in candidate_incoming_neighbour: + CV_neg.append(nodetemp) + conflict=CV_neg #there won't be conflict before the inserted node as the node inserted before its outgoing neighbour + #finally remove the conflict node + N_conflict=len(conflict) + if N_conflict>0: + for i in range(N_conflict): + S_trail.remove(conflict[i]) + + #third, evaluate the move + #delta_move=-len(S_trail)+len(S) + delta_move=N_conflict-1 + #accept all the move that is not harmful, otherwise use metrospolis algorithm + if delta_move<=0 or math.exp(-delta_move/float(T))>random.random(): + S = S_trail[:] + #update unnumbered nodes + unnumbered.remove(candidate) #remove the node just inserted + if N_conflict>0: #add all the conflict node just removed + for i in range(N_conflict): + unnumbered.append(conflict[i]) + N_unnumbered+=delta_move + nbMvt = nbMvt+1 + #update S_optimal only when there is increase in cardinal of the sequence + if len(S)>len(S_optimal): + S_optimal = S[:] + failure = False + if N_unnumbered==0: + return S_optimal + + #Increment the failure times if no progress in size of the sequence + if failure==True: + nbFail+=1 + else: #otherwise reset the num of failure times + nbFail=0 + #shrink the temperatue by factor alpha + T=T*alpha + #print T + #print nbFail + return S_optimal + + + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_python3/FVS_localsearch_10_python.py.bak --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/FVS_python3/FVS_localsearch_10_python.py.bak Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,193 @@ +''' +This file contains functions used to calculate feedback vertex set for directed graph + +The major function is the FVS_local_search(), which calculate maximum sub topological ordering of a graph +The algorithm is given by "Applying local search to feedback vertex set problem" +Author of the paper Philippe Galinier, Eunice Lemamou, Mohamed Wassim Bouzidi +The code mimic the pseudocode given in Page 805 in that paper +The code is written by Gang Yang, Penn State University +''' + + +import networkx as nx +import random +import math +import array + + + +#the following two function calculate the position for the candidate given the existing topological ordering S +def get_position_minus(candidate_incoming_neighbour, S): + ''' + get_position_minus return position as just after its numbered in-coming neighbours + As in the paper, the function return i_minus(v) + ''' + position = 1 + for x in range(len(S)-1,-1,-1): #we loop through index from the end as we try to find the largest index of numbered incoming neighbour + if S[x] in candidate_incoming_neighbour: + position = x+2 #2 comes from we need to put candidate after the incoming neighbour and also the position count from 1 instead of 0 + return position + return position #put the candidate in the first position if there is no numbered incoming neighbour + + + +def get_position_plus(candidate_outgoing_neighbour, S): + ''' + get_position_plus return position as just before its numbered out-going neighbours + As in the paper, the function return i_plus(v) + ''' + position = 1+len(S) + for x in range(len(S)): #we loop through index from the beginning as we try to find the smallest index of numbered outgoing neighbour + if S[x] in candidate_outgoing_neighbour: + position = x+1 #1 comes from the fact position count from 1 instead of 0 + return position + return position #put the candidate in the first position if there is no numbered outgoing neighbour + + + +def FVS_local_search(G_input, T_0, alpha, maxMvt, maxFail, randomseed=None): + ''' + Returns an maximum sub topological ordering of a DiGraph G. + FVS is G_input \ the topological ordering + A topological ordering of a graph is an ordering of vertices such that + the starting point of every arc occurs earlier in the ordering than + the endpoint of the art. + + This algorithm is a fast approximation based on simulating annealing(SA) of a noval local search strategy [1]_. + The code follows the pseudocode given in Page 805 in that paper. + + + Parameters + ---------- + G : NetworkX Graph/DiGraph, result for MultiGraph is not tested + T_0 : the initial temperature in SA + alpha : the cooling multiplier for temperatue in the geometric cooling regime + maxMvt_factor : maxMvt_factor times network size is the number of iterations for the inner loop given a fixed temperatue + maxFail : FVS_local_search stops when maxFail number of outloops (temperatue) fail to improve the result + randomseed: random seed for generating random numbers + + Returns + ------- + An approximation of the maximum ordering of the given graph as a list. + + + Notes + ----- + The code is written by Gang Yang, Department of Physics, Penn State University + + + References + ---------- + ..[1] Galinier, P., Lemamou, E. & Bouzidi, M.W. J Heuristics (2013) 19: 797. doi:10.1007/s10732-013-9224-z + + ''' + #set a random seed + random.seed(randomseed) + G=G_input.copy() + N= len(G.nodes()) #number of nodes in the graph + edges=G.edges() + + + #Initialization + T = T_0 #set initial temperatue + nbFail = 0 #Outer loop counter to record failure times + S = [] #list to record the ascending ordering + S_optimal = [] #list to record the optimal ordering + + + #calculate parent and child node for each node + parent = [{} for i in range(N)] + child = [{} for i in range(N)] + + for i in range(len(edges)): + edege = edges[i] + child[int(edge[0])][int(edge[1])]=None + parent[int(edge[1])][int(edge[0])]=None + + #all the nodes that are self_loops + self_loops = [edges[i][0] for i in range(len(edges)) if edges[i][0]==edges[i][1]] + #all the nodes that is not in S + unnumbered=[x for x in range(N) if x not in self_loops] + N_unnumbered = len(unnumbered) + while nbFail< maxFail: + nbMvt = 0 #Inner loop counter to record movement times + failure = True #if cardinal of S increase after one inner loop, failure will be set to false + while nbMvt < maxMvt: + candidate_index = random.randint(0, N_unnumbered-1) + candidate = unnumbered[candidate_index] #random pick a node from all unnumbered node + position_type = random.randint(0,1) #random choose a position type + #calculate incoming/outgoing neighbour for the candidate node, store as keys in the dict + candidate_incoming_neighbour = parent[candidate] + candidate_outgoing_neighbour = child[candidate] + #see how to find the position on Page 803 of the paper + #position_type=1 means just after incoming neighbours + if position_type==1: + position = get_position_minus(candidate_incoming_neighbour,S) + #position_type=0 means just before outgoint neighbours + elif position_type==0: + position = get_position_plus(candidate_outgoing_neighbour,S) + + #first, insert the candidate to the given position + S_trail = S[:] #copy the list + S_trail.insert(position-1,candidate) + + #second remove all the conflict + #break the sequence into two parts: before and after the candidate node and + S_trail_head= S_trail[:position-1] + S_trail_tail= S_trail[position:] + #determine conflict node See page 801 + if position_type==1: + CV_pos=[] #conflict before the newly inserted node in the topological ordering + for x in range(len(S_trail_head)): + nodetemp=S_trail_head[x] + #print nodetemp,candidate_outgoing_neighbour,nodetemp in candidate_outgoing_neighbour + if nodetemp in candidate_outgoing_neighbour: + CV_pos.append(nodetemp) + conflict=CV_pos #there won't be conflict after the inserted node as the node inserted after its incoming neighbour + elif position_type==0: + CV_neg=[] #conflict after the newly inserted node in the topological ordering + for x in range(len(S_trail_tail)): + nodetemp=S_trail_tail[x] + #print nodetemp,candidate_incoming_neighbour,nodetemp in candidate_incoming_neighbour + if nodetemp in candidate_incoming_neighbour: + CV_neg.append(nodetemp) + conflict=CV_neg #there won't be conflict before the inserted node as the node inserted before its outgoing neighbour + #finally remove the conflict node + N_conflict=len(conflict) + if N_conflict>0: + for i in range(N_conflict): + S_trail.remove(conflict[i]) + + #third, evaluate the move + #delta_move=-len(S_trail)+len(S) + delta_move=N_conflict-1 + #accept all the move that is not harmful, otherwise use metrospolis algorithm + if delta_move<=0 or math.exp(-delta_move/float(T))>random.random(): + S = S_trail[:] + #update unnumbered nodes + unnumbered.remove(candidate) #remove the node just inserted + if N_conflict>0: #add all the conflict node just removed + for i in range(N_conflict): + unnumbered.append(conflict[i]) + N_unnumbered+=delta_move + nbMvt = nbMvt+1 + #update S_optimal only when there is increase in cardinal of the sequence + if len(S)>len(S_optimal): + S_optimal = S[:] + failure = False + if N_unnumbered==0: + return S_optimal + + #Increment the failure times if no progress in size of the sequence + if failure==True: + nbFail+=1 + else: #otherwise reset the num of failure times + nbFail=0 + #shrink the temperatue by factor alpha + T=T*alpha + #print T + #print nbFail + return S_optimal + + + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_python3/FVS_test.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/FVS_python3/FVS_test.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,34 @@ +''' +This file contains test cases to test the FVS function. +''' + +import networkx as nx +import random +from FVS_python3 import FVS as FVS + + +#A random graph example +N=6 +M=12 +G1=nx.gnm_random_graph(N, M, seed=None, directed=True) +#calculate FVS +G1_FVS=FVS.FVS(G1) +print(G1.edges()) +print(G1_FVS) + +#A fixed graph example, the solution should be ['A'] +G2=nx.DiGraph() +G2.add_edges_from([('A','B'),('B','C'),('C','A'),('A','D'),('D','A')]) +#calculate FVS +G2_FVS=FVS.FVS(G2) +print(G2.edges()) +print(G2_FVS) + +#A three node feedback loops. The solution could be any node. +#With fixed randomseed, one should get the same answer. +G3=nx.DiGraph() +G3.add_edges_from([('A','B'),('B','C'),('C','A')]) +#calculate FVS +G3_FVS=FVS.FVS(G3, T_0=0.6, alpha=0.99, maxMvt_factor=5, maxFail=50, randomseed=1) +print(G3.edges()) +print(G3_FVS) diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_python3/README.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/FVS_python3/README.md Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,102 @@ +# README + +# I) THE METHOD +This repository contains functions used to approximate the minimum feedback vertex set (FVS) of a directed graph, a problem whose exact solution is known to be NP-hard. This algorithms was used to estimate the size of the minimum FVS in a recent paper "Zañudo, J. G. T., Yang, G., & Albert, R. (2017). PNAS 114: 28, 7234–7239, doi: 10.1073/pnas.1617387114". + +This algorithm identifies a near-minimum feedback vertex set using simulated annealing (SA) and a local search of topological ordering. The algorithm is describe in the paper "Galinier, P., Lemamou, E. & Bouzidi, M.W. J Heuristics (2013) 19: 797. doi:10.1007/s10732-013-9224-z". The code follows the pseudocode given in Page 805 in that paper. + +The originally code was written in Python 2.7, and this updated version seems to work for Python 3 (although it is still not fully tested). The module requires NetworkX 1.11. + +Another version written in Cython is available at https://github.com/yanggangthu/FVS_cython. The cython version is typically 5-10 times faster than the python version. It has not been updated for use in Python 3. + + +# II) STRUCTURE OF MODULE +Related functions are stored in FVS.py and FVS_localsearch_10_python.py. + +Core functions of finding a maximum sub topological ordering of a given graph (which is equivalent to identifying a minimum FVS) is written in FVS_localsearch_10_python.py. + +FVS.py is a wrapper, that deals with graph input and identifies a near-minimum FVS by subtracting the nodes in the topological ordering from all the nodes. + +FVS_test.py contains three examples illustrating how to use the code. + +# III) INSTRUCTIONS + +To use this module, import FVS and call FVS() just as a regular function. The function can take 6 paramters listed below and only the first (the graph) is neccessary. + +This was tested in anaconda3 creating an environment "py37_FVS" with Python=3.7 and networkx=1.1: + +``` +conda create -n py3_FVS python=3.7 networkx=1.11 +``` + +From there, the FVS_python3 folder with the contents of the repository was moved to *anaconda3/envs/py37_FVS/lib/python3.7/site-packages*. With that, the *FVS_test.py* runs as it should: + +``` +python FVS_test.py + +[(0, 4), (0, 1), (1, 0), (2, 1), (2, 4), (2, 5), (2, 3), (3, 5), (3, 4), (4, 1), (5, 2), (5, 0)] +[0, 2] +[('A', 'B'), ('A', 'D'), ('B', 'C'), ('C', 'A'), ('D', 'A')] +['A'] +[('A', 'B'), ('B', 'C'), ('C', 'A')] +['B'] +``` + +Parameters +---------- +G : NetworkX Graph/DiGraph, result for MultiGraph is not tested +T_0 : the initial temperature in SA +alpha : the cooling multiplier for temperatue in the geometric cooling regime +maxMvt_factor : maxMvt_factor times network size is the number of iterations for the inner loop given a fixed temperatue +maxFail : FVS_local_search stops when maxFail number of outloops (temperatue) fail to improve the result +randomseed: random seed for generating random numbers + +Default Parameter Values +----------------------- +T_0 = 0.6, alpha = 0.99, maxMvt_factor = 5, maxFail = 50, randomseed=None +The default values are suggested by the author of the paper. +T_0 and maxFail are chosen after a limited number of preliminary experiments +alpha is chosen more arbitrarily, however alpha should be a positive number slightly smaller than 1. +Increase alpha or maxMvt_factor or maxFail will increase the time of finding FVS. + +Returns +------- +An approximation of the minimum FVS of the given graph as a list. + +# IV) EXAMPLES + +``` +import networkx as nx +from FVS_python3 import FVS as FVS +``` +Here we construct an example with an optimal solution. G2_FVS shoule be ['A'] as a list. + +``` +G2=nx.DiGraph() +G2.add_edges_from([('A','B'),('B','C'),('C','A'),('A','D'),('D','A')]) +G2_FVS=FVS.FVS(G2) +``` + +Here we construct an example of three-node feedback loops. +We show how you change all the parameters and set a random seed. +Your result should be the same with the same randomseed. +``` +G3=nx.DiGraph() +G3.add_edges_from([('A','B'),('B','C'),('C','A')]) +G3_FVS=FVS.FVS(G3, T_0=0.6, alpha=0.99, maxMvt_factor=5, maxFail=50, randomseed=1) +``` + + + +# V) COPYRIGHT + + +The MIT License (MIT) + +Copyright (c) 2017 Gang Yang and Reka Albert. + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_python3/__init__.py diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_python3/__pycache__/FVS.cpython-36.pyc Binary file tools/myTools/bin/FVS_python3/__pycache__/FVS.cpython-36.pyc has changed diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_python3/__pycache__/FVS_localsearch_10_python.cpython-36.pyc Binary file tools/myTools/bin/FVS_python3/__pycache__/FVS_localsearch_10_python.cpython-36.pyc has changed diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_python3/__pycache__/__init__.cpython-36.pyc Binary file tools/myTools/bin/FVS_python3/__pycache__/__init__.cpython-36.pyc has changed diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/FVS_run.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/FVS_run.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 + +import networkx as nx +import random +from FVS_python3 import FVS as FVS +import re +import csv +import sys + +outputfile=open('fvs.txt','w+') +edges=open(sys.argv[1]).readlines() #open network file +data1=[(line.strip().split()[0],line.strip().split()[1],line.strip().split()[2]) for line in edges] +for elem in data1: + if elem[0]==elem[2]: + if elem[1]=="inhibits": + data1.remove(elem) +data=[tuple([line[0],line[2]]) for line in data1] #creates tuple from file +G3=nx.DiGraph() +G3.add_edges_from(data) + +G3_FVS1=FVS.FVS(G3, T_0=0.6, alpha=0.99, maxMvt_factor=5, maxFail=10,randomseed=1) +G3_FVS1.sort() + +outputfile.write('name'+'\n') +outputfile.write("\n".join(map(str, G3_FVS1))) + +outputfile.close() diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/SFA_exp_attr.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/SFA_exp_attr.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +# ----------------------------------------------------------- +# SimulateAttractors.py +## estimate experimental attractors +## calculate the median of the resistant and vehicle attractors +#----------------------------------------------------------- + + +import os +import numpy as np +import pandas as pd +import networkx as nx +import random +import sfa +import csv +import sys +########INPUTS############ + +fpath = os.path.join(sys.argv[1]) #location of networkfile + +samples=pd.read_csv(sys.argv[2],index_col = 0) ##expression values for data + +class ThreeNodeCascade(sfa.base.Data): + def __init__(self): + super().__init__() + self._abbr = "TNC" + self._name = "A simple three node cascade" + + signs = {'activates':1, 'inhibits':-1} + A, n2i, dg = sfa.read_sif(fpath, signs=signs, as_nx=True) + self._A = A + self._n2i = n2i + self._dg = dg + self._i2n = {idx: name for name, idx in n2i.items()} + + # end of def __init__ +# end of def class + +if __name__ == "__main__": +## initalize parameters from SFA + data = ThreeNodeCascade() + algs = sfa.AlgorithmSet() + alg = algs.create('SP') + alg.data = data + alg.params.apply_weight_norm = True + alg.initialize() + alg.params.exsol_forbidden=True + alg.params.alpha=.9 + + netnodes= list(data.dg.nodes) #get network node names + samples=samples.loc[list(set(netnodes) & set(samples.index))] + n = data.dg.number_of_nodes() #the number of nodes + b = np.zeros((n,)) + + + + logss=pd.DataFrame(index=samples.columns,columns=netnodes,copy=True) + pi=[] + for name, item in samples.iteritems(): #for each experimental sample + enodes=item.index.tolist() + for node in enodes: # set initial state to sample norm exp value + b[data.n2i[node]]=float(str(item.loc[node])) + + x = alg.compute(b,pi) #Run SFA calculation + logss.loc[name,netnodes]=x[0] + logss=logss.astype(float).round(3) +#write out nondisc tables + logss.to_csv('attrs_exp.tsv', sep=' ',float_format='%.3f',index_label="name",chunksize=10000) diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/SFA_insilico.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/SFA_insilico.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,107 @@ +#!/usr/bin/env python3 + + +import os +import numpy as np +import pandas as pd +import networkx as nx +import random +import sfa +import csv +import sys +import statistics + + + +fpath = os.path.join(sys.argv[1]) #location of networkfile +qdata_all=pd.read_csv(sys.argv[2],index_col = 0) +all_samples=pd.read_csv(sys.argv[3],delim_whitespace=True, index_col = False) +npert=int(sys.argv[4]) +phenotypes = all_samples.phenotype.unique() +class ThreeNodeCascade(sfa.base.Data): + def __init__(self): + super().__init__() + self._abbr = "TNC" + self._name = "A simple three node cascade" + + signs = {'activates':1, 'inhibits':-1} + A, n2i, dg = sfa.read_sif(fpath, signs=signs, as_nx=True) + self._A = A + self._n2i = n2i + self._dg = dg + self._i2n = {idx: name for name, idx in n2i.items()} + + # end of def __init__ +# end of def class + + +def gen_basal_states(npert,nnode,pre,opts): + numpert=int(npert) # number of perturbations to generate + numnodes=int(nnode) # number of nodes to generate them for + prefix = pre # prefix for column labels + options=opts.split(',') + df1=pd.DataFrame() + if numpert > len(options)**numnodes: + numpert = len(options)**numnodes + while len(df1.index)numpert: + df1=df1.iloc[0:numpert,] + l1=[] + for i in range(0,numpert): + l1.append(prefix+'_' + str(i+1).rjust(len(str(numpert)), '0')) + df1.index=l1 + + return df1 + + +if __name__ == "__main__": +## initalize parameters from SFA + data = ThreeNodeCascade() + algs = sfa.AlgorithmSet() + alg = algs.create('SP') + alg.data = data + alg.params.apply_weight_norm = True + alg.initialize() + alg.params.exsol_forbidden=True + alg.params.alpha=0.9 + + + + netnodes=list(data.dg.nodes) + expnodes=list(set(netnodes) & set(qdata_all.index)) + samples=gen_basal_states(npert,len(expnodes),'attr','0,-1,1') + samples.columns=expnodes + n = data.dg.number_of_nodes() #the number of nodes + b = np.zeros((n,)) + switch_num = int(len(samples)/len(phenotypes)) + (len(phenotypes) % len(samples) > 0) + l=0 + logss=pd.DataFrame(index=samples.index,columns=netnodes,copy=True) + for i in range(len(phenotypes)): + m=switch_num*i + qs = all_samples[all_samples.isin([phenotypes[i]]).any(axis=1)]['name'].tolist() + qdata=qdata_all.loc[:,qs] + pi=[] + samples2=samples.iloc[l:switch_num+m] + minv=pd.Series(index = qdata.index, data = [np.amin(qdata.loc[node,]) for node in qdata.index]) + maxv=pd.Series(index = qdata.index, data = [np.amax(qdata.loc[node,]) for node in qdata.index]) + q1=pd.Series(index = qdata.index, data = [np.quantile(qdata.loc[node,],.33) for node in qdata.index]) + q2=pd.Series(index = qdata.index, data = [np.quantile(qdata.loc[node,],.66) for node in qdata.index]) + + for name, item in samples2.iterrows(): #for each simulated initial condition + enodes=item.index.tolist() + for node in enodes: # set initial state to simulated value + if item.loc[node]==1: # if 1 + number=np.random.uniform(low=q2[node], high=maxv[node]) #generate a random value for the node in the upper quartile + elif item.loc[node]==-1: # if -1 + number=np.random.uniform(low=minv[node], high=q1[node]) #generate a random value for the node in the lower quartile + else: #item.loc[node]==0 + number=np.random.uniform(low=q1[node], high=q2[node]) #generate a random value for the node in the middle + b[data.n2i[node]]=number + x = alg.compute(b,pi) # Run SFA calculation + logss.loc[name,netnodes]=x[0] + l=switch_num+m + logss=logss.astype(float).round(3) + logss.to_csv('attrs_insilico.tsv', sep=' ',float_format='%.3f',index_label="name",chunksize=10000) \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/SFA_virtscreen.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/SFA_virtscreen.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +import os +import numpy as np +import pandas as pd +import networkx as nx +import random +import sfa +import csv +from sys import argv +from sklearn.preprocessing import StandardScaler +import itertools +########INPUTS############ +fpath = os.path.join( argv[1]) # location of networkfile +samples=pd.read_csv(argv[2],index_col = 0) # input samples (normexp initial states) +inits=argv[3] #the initial states of interest + +phenotypes = pd.read_csv(argv[4],delim_whitespace=True, index_col = False) +initz = phenotypes[phenotypes.isin([inits]).any(axis=1)]['name'].tolist() +samples = samples[initz] + +FC_nodes=open(argv[5]).read().strip().split('\n') # FC set +FC_perts=pd.read_csv(argv[6],delim_whitespace=True,index_col=0,header=0,names=FC_nodes) + + +class ThreeNodeCascade(sfa.base.Data): + def __init__(self): + super().__init__() + self._abbr = "TNC" + self._name = "A simple three node cascade" + + signs = {'activates':1, 'inhibits':-1} + A, n2i, dg = sfa.read_sif(fpath, signs=signs, as_nx=True) + self._A = A + self._n2i = n2i + self._dg = dg + self._i2n = {idx: name for name, idx in n2i.items()} + + # end of def __init__ +# end of def class + +if __name__ == "__main__": +## initalize parameters from SFA + data = ThreeNodeCascade() + algs = sfa.AlgorithmSet() + alg = algs.create('SP') + alg.data = data + alg.params.apply_weight_norm = True + alg.initialize() + alg.params.exsol_forbidden=True + alg.params.alpha=0.9 + + + netnodes= list(data.dg.nodes) + + n = data.dg.number_of_nodes() #the number of nodes + b = np.zeros((n,)) + + alllogss=pd.DataFrame() + for name, item in samples.iteritems(): #for each simulated initial condition + logss=pd.DataFrame(index=FC_perts.index,columns=netnodes,copy=True) # create dataframe for the FC perturbations + enodes=item.index.tolist() # get expressed nodes + for node in enodes: # set initial state to simulated value + b[data.n2i[node]]=float(str(item.loc[node])) + for name2,item2 in FC_perts.iterrows(): # for each FCnode perturbation + pnode=item2.index.tolist() + pi = [] + for node in pnode: #if logFC.loc[node].at['logFC']>0: # if the logfc is postive (res > veh) + if int(item2.loc[node])==-1: #if the perturbation is 0 + if node in enodes: + b[data.n2i[node]]=float(str(item.loc[node]))-2.5*(float(str(item.loc[node]))) #downregulate the expression. fix to 0 + else: + b[data.n2i[node]]=-2.5 #downregulate the expression. fix to 0 + pi.append(data.n2i[node]) + if int(item2.loc[node])==1: #if the perturbation is 1 + if node in enodes: + b[data.n2i[node]]=float(str(item.loc[node]))+2.5*(float(str(item.loc[node]))) #upregulate the expression + else: + b[data.n2i[node]]=2.5 #upregulate the expression + pi.append(data.n2i[node]) + x = alg.compute(b,pi) # Run SFA calculation + logss.loc[name2,netnodes]=x[0] + logss=logss.astype(float).round(3) + alllogss=pd.concat([alllogss,logss],axis=0) + multi=pd.MultiIndex.from_product([samples.columns.tolist(),FC_perts.index.tolist()],names=['replicate', 'perturbation']) + alllogss.set_index(multi,inplace=True) + alllogss.to_csv('pert_logss.txt', sep=' ',float_format='%.3f',chunksize=10000) \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/class_and_consensus.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/class_and_consensus.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +import pandas as pd +from sklearn.naive_bayes import GaussianNB +from sklearn.ensemble import RandomForestClassifier +from sklearn.svm import SVC +from collections import Counter +import sys +def main(): + train=sys.argv[1].split(',') #train attractors + df_attr=pd.DataFrame() + for j in train: + dfj=pd.read_csv(j, delim_whitespace=True,index_col = ["name"]) + df_attr=pd.concat([df_attr,dfj],axis=0) + #df_attr=df_attr.drop_duplicates() + df_perturb=pd.read_csv(sys.argv[2], delim_whitespace=True,index_col=[0,1]) + + df_labels=pd.read_csv(sys.argv[3], delim_whitespace=True,index_col = ["name"]) # kmeans results + labels=df_labels['clusters'].tolist() + + gnb = GaussianNB() + gnb.fit(df_attr,labels) #do knn with attractor landscape + perturb_lab=gnb.predict(df_perturb) # predict clusters for perturbations + #create dataframe of perturabtion and clusters + NB_label=pd.DataFrame(index=df_perturb.index) + NB_label['clusters']=perturb_lab + + regressor = RandomForestClassifier(n_estimators=100, random_state=1) + regressor.fit(df_attr,labels) #do knn with attractor landscape + perturb_lab=regressor.predict(df_perturb) # predict clusters for perturbations + #create dataframe of perturabtion and clusters + RF_label=pd.DataFrame(index=df_perturb.index) + RF_label['clusters']=perturb_lab + + + svm=SVC(gamma='auto',random_state=4) #intialize knn + svm.fit(df_attr,labels) #do knn with attractor landscape + perturb_lab=svm.predict(df_perturb) # predict clusters for perturbations + #create dataframe of perturabtion and clusters + SVM_label=pd.DataFrame(index=df_perturb.index) + SVM_label['clusters']=perturb_lab + + consensus=[] + + for df in (SVM_label,NB_label,RF_label): + df=df.where(df==0, None) + df['count']=df.apply(lambda x: x.count(), axis=1) + if df.index.nlevels>1: + consensus.append(df.loc[df['count']>=1].index.get_level_values('perturbation').to_list()) + else: + consensus.append(df.loc[df['count']>1].index.to_list()) + cencount=Counter(x for sublist in consensus for x in sublist) + + if df.index.nlevels>1: + keys = [k for k, v in cencount.items() if v >=len(df.index.unique('replicate').tolist())*2] + else: + keys = [k for k, v in cencount.items()] + + with open('crit1perts.txt','w') as f: + for item in keys: + f.write("%s\n" % item) + +main() \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/crit2.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/crit2.R Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,74 @@ +#!/usr/bin/env Rscript +.libPaths('~/R_libs_36') +args = commandArgs(trailingOnly=TRUE) + +library(plyr) +library("dplyr", warn.conflicts = FALSE) +library(reshape2) +library(readr) +getwd() + +exp<-read.delim(args[1],sep=" ",row.names = 1) +samples<-read.delim(args[2],sep="\t",row.names = 1) +exp<-exp %>% dplyr::filter(row.names(exp) %in% row.names(samples)) +desired<-args[3] +undesired<-args[4] +filter<-args[5] + + +dfdes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% desired,,drop=FALSE])) +meandes<- apply(dfdes,2,mean) +dfundes<- exp %>% filter(rownames(exp) %in% rownames(samples[samples$phenotype %in% undesired,,drop=FALSE])) +meandundes<-apply(dfundes,2,mean) +test<-meandes-meandundes +disc<-test +disc[disc<0]<-0 +disc[disc>0]<-1 +nodes<-colnames(dfdes) + +is<-read.delim(args[6],sep=" ") #internal_markers.txt +isdisc<-is +if (filter=="strict"){ + for (node in nodes){ + if (disc[node]==0){ + val=max(dfdes[,node]) + isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) + } + if (disc[node]==1){ + val=min(dfdes[,node]) + isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) + isdisc[,node]<-replace(isdisc[,node],is[,node]val, 1) + } + if (disc[node]==1){ + val=max(dfdes[,node]) + isdisc[,node]<-replace(isdisc[,node],is[,node]>val, 1) + isdisc[,node]<-replace(isdisc[,node],is[,node]%filter(hammingdist<1) +}else{ + hams1_1<-hammings %>%filter(hammingdist<=length(nodes)-round(.9*length(nodes))) +} + +write.table(hams1_1[,c(1,2)],'crit2_perturbations.txt',quote=FALSE,row.names = FALSE,sep=" ") + + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/generate_perts.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/generate_perts.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,18 @@ +#!/usr/bin/env python3 +import os +import numpy as np +import pandas as pd +import itertools +import csv +import sys + +def main(): + numnodes=len(open(sys.argv[1]).read().strip().split('\n')[1:])# number of perturbations to generate + prefix = sys.argv[2] # prefix for column labels + options=sys.argv[3].split(',') + test=itertools.product(options, repeat=numnodes) + df1=pd.DataFrame(list(test)) + df1=df1.set_index(prefix+'_'+df1.index.astype(str)) + + df1.to_csv('init.txt',sep=' ', index=True,na_rep='NA') +main() diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/get_RONs.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/get_RONs.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,14 @@ +#!/usr/bin/env python3 +import pandas as pd +import sys + +def main(): + +# read in sfa attractors + df=pd.read_csv(sys.argv[1], delim_whitespace=True,index_col = ["name"]) +# read in rons + rons = open(sys.argv[2],"r").read().strip().split('\n') +#get the rons and their values + df1=df.loc[:,rons] + df1.to_csv("exp_internalmarkers.tsv",sep=' ',index_label="name",na_rep='NA') +main() \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/get_RONs_getperts.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/get_RONs_getperts.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,17 @@ +#!/usr/bin/env python3 +import pandas as pd +import sys + +def main(): + +# read in sfa attractors + df=pd.read_csv(sys.argv[1], delim_whitespace=True,index_col=[0,1]) + perts=open(sys.argv[3],"r").read().split('\n') + del perts[-1] + #print(perts) +# read in rons + rons = open(sys.argv[2],"r").read().strip().split('\n') +#get the rons and their values + df1=df.loc[pd.IndexSlice[:,perts],rons] + df1.to_csv("internal_markers.txt",sep=' ',na_rep='NA') +main() \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/get_perts.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/get_perts.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 + +import pandas as pd +import sys + +def main(): + + FC_perts=pd.read_csv(sys.argv[1], delim_whitespace=True) + FC_nodes=open(sys.argv[2]).read().strip().split('\n')[1:] # FC set + extracts_file=pd.read_csv(sys.argv[3], delim_whitespace=True,index_col=0) + extracts=extracts_file.perturbation.unique() + FC_perts.columns=FC_nodes #set index for FC nodes + df1=FC_perts.loc[extracts,:] + df1.to_csv("extracted_perts.txt",sep=' ',index_label="name",na_rep='NA') +main() \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/icn_check1.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/icn_check1.R Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,30 @@ +#!/usr/bin/env Rscript + + +args = commandArgs(trailingOnly=TRUE) + +library("plyr") +library("dplyr", warn.conflicts = FALSE) +library("ggplot2") +library("reshape2") +library("readr") + +png(file="experimental_internalmarkers.png") +exp<-read.delim(args[1],sep=" ",row.names = 1) +samples<-read.delim(args[2],sep="\t",row.names = 1) +exp<-exp %>% dplyr::filter(row.names(exp) %in% row.names(samples)) + + +d1exp<-exp +d1exp$phenotype<-samples$phenotype +suppressWarnings({ +suppressMessages({ +mu <- ddply(melt(d1exp), .(variable,phenotype)) + +g1<- ggplot(melt(d1exp),aes(x = value)) + + facet_wrap(~variable,scales = "free_x",shrink=FALSE) + + geom_vline(data=mu,aes(xintercept=value,color=phenotype),linetype="dashed") + +g1 +}) +}) \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/kmeans_full.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/kmeans_full.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +import pandas as pd +from scipy import stats +import matplotlib as mpl +mpl.use('Agg') +import matplotlib.pyplot as plt +from sklearn.decomposition import PCA +import numpy as np +from sklearn.datasets import make_blobs +from sklearn.cluster import MiniBatchKMeans +from yellowbrick.cluster.elbow import kelbow_visualizer +import sys +import os + +def main(): +#####Input files and user specifications########### + datasets = sys.argv[1].split(',') #the input data file (logss, DAC, both, or discrete versions) + df=pd.DataFrame() + for i in datasets: + dfi=pd.read_csv(i, delim_whitespace=True,index_col = ["name"]) + df=pd.concat([df,dfi],axis=0) +# Use the quick method and immediately show the figure + df=df.loc[:, (df != 0).any(axis=0)] + + visualizer1=kelbow_visualizer(MiniBatchKMeans(random_state=0,n_init=10),df, k=(2,10),title="optimal k via elbow method") + elbow=visualizer1.elbow_value_ + visualizer1.show('elbow.png',clear_figure=True) + visualizer2=kelbow_visualizer(MiniBatchKMeans(random_state=0,n_init=10),df, k=(2,10),metric='silhouette',title="optimal k via silhouette method") + silhouette=visualizer2.elbow_value_ + visualizer2.show("silhouette.png") + + if elbow==silhouette: + k=elbow + if elbowsilhouette: + k=silhouette +#report out final k-means selected + kmeans = MiniBatchKMeans(n_clusters=k,random_state=0,n_init=100).fit(df) + labels = kmeans.labels_ + df['clusters'] = labels + df2 = pd.DataFrame(index=df.index) + df2['clusters'] = labels + df2.index.name = 'name' + df2.to_csv('kmeans.txt', sep=' ') +main() diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/pertanalysis.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/pertanalysis.R Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,24 @@ +#!/usr/bin/env Rscript +.libPaths('~/R_libs_36') +args = commandArgs(trailingOnly=TRUE) + +library("dplyr", warn.conflicts = FALSE) +library(ggplot2) + + +perts_lab<-read.delim(args[1],sep=" ",row.names = 1) + +perts_lab<-replace(perts_lab,perts_lab==-1,"down") +perts_lab<-replace(perts_lab,perts_lab==1,"up") +perts_lab<-replace(perts_lab,perts_lab==0,"nochange") + +df1<-data.frame(perts_lab) +df1$up <- apply(df1, 1, function(x) length(which(x=="up"))) +df1$down<-apply(df1, 1, function(x) length(which(x=="down"))) +df1$total<-df1$up+df1$down +df1<-df1[order(df1$total),] +write.table(df1,"successful_controlnode_perturbations.txt",quote=F) + +# oh<-t(perts_lab) %>% reshape2::melt() +# pdf(file="pertrubation_trends.pdf") +# ggplot(oh, aes(x=value)) + facet_wrap(~Var1,scales = "free_x",shrink=FALSE) + geom_bar(aes(y = (..count..)/nrow(perts_lab))) + scale_y_continuous(labels=scales::percent) \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,19 @@ + + + +from .base import * +from .containers import AlgorithmSet +from .containers import DataSet + +from .stats import * +from .utils import * +from .topology import * +from .fileio import * + +__all__ = [] +__all__ += base.__all__ +__all__ += containers.__all__ +__all__ += stats.__all__ +__all__ += utils.__all__ +__all__ += topology.__all__ +__all__ += fileio.__all__ diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/__pycache__/__init__.cpython-36.pyc Binary file 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tools/myTools/bin/sfa/algorithms/np.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/algorithms/np.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,425 @@ +# -*- coding: utf-8 -*- + +import sys + +if sys.version_info <= (2, 8): + from builtins import super + + +import numpy as np +import pandas as pd + +import sfa.base +import sfa.utils + + +class NetworkPropagationParameterSet(sfa.base.ParameterSet): + """ + An object that deals with the parameters + of ``sfa.algorithms.np.NetworkPropagation`` base algorithm. + + Attributes + ---------- + alpha : float + lim_iter : int + apply_weight_norm : bool + use_rel_change : bool + exsol_forbidden : bool + no_inputs : bool + """ + + def __init__(self): + self.initialize() + self._freeze() + + def initialize(self): + """Initialize the parameters with default values. + """ + self._alpha = 0.5 # float value in (0, 1). The default value is 0.5. + self._lim_iter = 1000 + self._apply_weight_norm = False + self._use_rel_change = False + self._exsol_forbidden = False + self._no_inputs = False + + @property + def alpha(self): + r"""Hyperparameter, :math:`\alpha` ~ (0, 1). + It controls the portion of signal flow + in determining the activity. + The default value is 0.5. + """ + return self._alpha + + @alpha.setter + def alpha(self, val): + if not isinstance(val, float): + raise TypeError("alpha should be a float type value in (0,1).") + elif (val <= 0.0) or (val >= 1.0): + raise ValueError("alpha should be within (0,1).") + else: + self._alpha = val + + @property + def lim_iter(self): + """Number of maximum iterations in the iterative method. + """ + return self._lim_iter + + @lim_iter.setter + def lim_iter(self, val): + if not isinstance(val, int): + raise TypeError("lim_iter is a integer type value.") + elif val < 0: + raise ValueError("lim_iter should be greater than 0.") + else: + self._lim_iter = val + + @property + def apply_weight_norm(self): + """Apply the link weight normalization. + """ + return self._apply_weight_norm + + @apply_weight_norm.setter + def apply_weight_norm(self, val): + if not isinstance(val, bool): + raise TypeError( + "apply_weight_norm should be a bool type value.") + self._apply_weight_norm = val + + @property + def use_rel_change(self): + """Use relative change for prediction. + """ + + return self._use_rel_change + + @use_rel_change.setter + def use_rel_change(self, val): + if not isinstance(val, bool): + raise TypeError("use_rel_change should be a bool type value.") + self._use_rel_change = val + + @property + def exsol_forbidden(self): + """Forbid the propagation computation + based on the exact solution. + In other words, use the iterative method. + """ + return self._exsol_forbidden + + @exsol_forbidden.setter + def exsol_forbidden(self, val): + if not isinstance(val, bool): + raise TypeError("exsol_forbidden should be boolean type.") + + self._exsol_forbidden = val + + @property + def no_inputs(self): + """Do not apply the effects of inputs in a given network. + """ + return self._no_inputs + + @no_inputs.setter + def no_inputs(self, val): + if not isinstance(val, bool): + raise TypeError("no_inputs is bool type.") + self._no_inputs = val +# end of def class ParameterSet + + +class NetworkPropagation(sfa.base.Algorithm): + """A base class that defines the basic functionality of + network propagation algorithms. + + Attributes + ---------- + + + """ + + def __init__(self, abbr): + """Constructor of NetworkPropagation. + """ + super().__init__(abbr) + self._name = "Abstract base class for network propagation algorithms" + self._params = NetworkPropagationParameterSet() + + # The following members are assigned the instances in initialize() + self._W = None + self._b = None + self._pi=None + + self._exsol_avail = False # The exact solution is available. + self._M = None # A matrix for getting the exact solution. + self._weight_matrix_invalidated = True + + self._result = sfa.base.Result() + + # end of def __init__ + + @property + def b(self): + return self._b + + @b.setter + def b(self, vec): + self._b = vec + + @property + def pi(self): + return self._pi + + @pi.setter + def pi(self,vec): + self._pi=vec + @property + def W(self): + return self._W + + @W.setter + def W(self, mat): + self._W = mat + self._weight_matrix_invalidated = True + + # end of _W.setter + + def initialize_network(self): + + # Matrix normalization for getting transition matrix + if self._params.apply_weight_norm: + self.W = sfa.utils.normalize(self.data.A) + else: + self.W = np.array(self.data.A, dtype=np.float) + + self._check_dimension(self.W, "transition matrix") + + if not self.params.exsol_forbidden: + # Try to prepare the exact solution + try: + self.prepare_exact_solution() + self._check_dimension(self._M, "exact solution matrix") + self._exsol_avail = True + except np.linalg.LinAlgError: + pass + + if not self._exsol_avail: + self.prepare_iterative_solution() + self._exsol_avail = False + + # end of def _initialize_network + + def _check_dimension(self, mat, mat_name): + """Check whether a given matrix is a square matrix. + """ + if mat.shape[0] != mat.shape[1]: + raise ValueError("The %s should be square matrix." % (mat_name)) + # end of def _check_dimension + + def initialize_basal_activity(self): + N = self.data.A.shape[0] # Number of state variables + self._b = np.zeros(N) + self._pi=[] + # end of def + + def apply_inputs(self, inds, vals): + """ + + Parameters + ---------- + + """ + if self._params.no_inputs: + return + + # Input condition + if hasattr(self.data, 'inputs') and self.data.inputs: + inds_inputs = [self.data.n2i[inp] for inp in self.data.inputs] + vals_inputs = [val for val in self.data.inputs.values()] + inds.extend(inds_inputs) + vals.extend(vals_inputs) + # end of if + + # end of def apply_inputs + + def apply_perturbations(self, targets, inds, vals, W_ptb=None): + if self.data.has_link_perturb and W_ptb is None: + raise ValueError("Weight matrix for perturbation is necessary for " + "the data including link type perturbations.") + + for target in targets: + if self.data.df_ptb is not None: + type_ptb = self.data.df_ptb.ix[target, "Type"] + val_ptb = self.data.df_ptb.ix[target, "Value"] + else: + type_ptb = 'node' + val_ptb = -1 + + if type_ptb == 'node': + inds.append(self.data.n2i[target]) + vals.append(val_ptb) + elif type_ptb == 'link': + idx = self.data.n2i[target] + W_ptb[:, idx] *= val_ptb + elif type_ptb == 'isolation': + idx = self.data.n2i[target] + W_ptb[:, idx] *= val_ptb + W_ptb[idx, :] *= val_ptb + else: + raise ValueError("Undefined perturbation type: %s" % (type_ptb)) + + # end of def apply_perturbations + + def compute_batch(self): + + df_exp = self.data.df_exp # Result of experiment + + # Simulation result + sim_result = np.zeros(df_exp.shape, dtype=np.float) + + b = self._b + pi=self._pi + + if self._params.use_rel_change: + inds_ba = [] # Indices of nodes to be perturbed + vals_ba = [] # Basal activity + self.apply_inputs(inds_ba, vals_ba) + b[inds_ba] = vals_ba + x_cnt = self.compute(b) + + W_cnt = self.W + + # Main loop of the simulation + for i, targets_ptb in enumerate(self.data.names_ptb): + inds_ba = [] # Indices of nodes to be perturbed + vals_ba = [] # Basal activity + self.apply_inputs(inds_ba, vals_ba) # Apply the input condition + + if self.data.has_link_perturb: + W_ptb = W_cnt.copy() + self.apply_perturbations(targets_ptb, inds_ba, vals_ba, W_ptb) + self.W = W_ptb + else: + self.apply_perturbations(targets_ptb, inds_ba, vals_ba) + + b_store = b[inds_ba] + b[inds_ba] = vals_ba + x_exp = self.compute(b) + + # Result of a single condition + if self._params.use_rel_change: # Use relative change + x_diff = (x_exp - x_cnt) + rel_change = x_diff + res_single = rel_change[self.data.iadj_to_idf] + else: + res_single = x_exp[self.data.iadj_to_idf] + + sim_result[i, :] = res_single + b[inds_ba] = b_store + # end of for + + self.W = W_cnt + + df_sim = pd.DataFrame(sim_result, + index=df_exp.index, + columns=df_exp.columns) + + # Get the result of elements in the columns of df_exp. + self._result.df_sim = df_sim[df_exp.columns] + + # end of def compute_batch + + def prepare_exact_solution(self): + """Prepare to get the matrix for the exact solution. + """ + # end of def _prepare_exact_solution + + def prepare_iterative_solution(self): + """Prepare to get the solution from the iterative method. + """ + # end of def _prepare_iterative_solution + + def compute(self, b, pi): + if self.params.exsol_forbidden is True \ + or self._exsol_avail is False: + alpha = self._params.alpha + W = self.W + lim_iter = self._params.lim_iter + x_ss, _ = self.propagate_iterative(W, b, b,pi, a=alpha, + lim_iter=lim_iter) + return x_ss, _ # x at steady-state (i.e., stationary state) + else: + return self.propagate_exact(b) + + # end of def compute + + def propagate_exact(self, b): + """Obtain the activity at steady-state + based on the exact solution of network propagation. + + Parameters + ---------- + b : numpy.ndarray + 1D array of basal activity. + + Returns + ------- + x : numpy.ndarray + The exact solution of the activity at steady-state + in 1D array. + """ + raise NotImplementedError("propagate_exact is not implemented") + + def propagate_iterative(self, + W, + xi, + b, + pi, + a=0.5, + lim_iter=1000, + tol=1e-5, + get_trj=False): + + r"""Compute network propagation based on the iterative method. + This method should be used if we want to obtain the trajectory. + + Parameters + ---------- + W: numpy.ndarray + 2D array for weight matrix. + xi: numpy.ndarray + 1D array for initial state. + b: numpy.ndarray + 1D array for basal activity. + a: real number, optional + Hyperparameter, :math:`\alpha`, ~ (0, 1). + The default value is 0.5. + lim_iter: int, optional + Number of maximum iterations in the iterative method. + Computation terminates when the iteration reaches ``lim_iter``. + The default value is 1000. + tol: float, optional + Tolerance for terminating iteration. + Iteration continues, if Frobenius norm of + :math:`x(t+1)-x(t)` is greater than ``tol``. + The default value is 1e-5. + get_trj: bool, optional + Determine whether the trajectory of the state is returned. + If get_trj is true, the trajectory is returned. + + Returns + ------- + x : numpy.ndarray + 1D array of the activity after the computation. + trj : numpy.ndarray + 2D array where the row represents a state of the activity. + + See also + -------- + """ + raise NotImplementedError("propagate_iterative is not implemented") + # end of def propagate_iterative + +# end of def class NetworkPropagation diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/algorithms/sp.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/algorithms/sp.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,119 @@ +# -*- coding: utf-8 -*- + +import sys +if sys.version_info <= (2, 8): + from builtins import super + +import numpy as np + +from .np import NetworkPropagation +from .np import NetworkPropagationParameterSet + + +def create_algorithm(abbr): + return SignalPropagation(abbr) +# end of def + + +class SignalPropagationParameterSet(NetworkPropagationParameterSet): + def initialize(self): + super().initialize() +# end of class ParameterSet + + +class SignalPropagation(NetworkPropagation): + + def __init__(self, abbr): + super().__init__(abbr) + self._name = "Signal propagation algorithm" + # end of def __init__ + + def prepare_exact_solution(self): + """ + Prepare to get the matrix for the exact solution: + + .. :math + x(t+1) = a*_W.dot(x(t)) + (1-a)*b, where $a$ is alpha. + + When $t -> inf$, both $x(t+1)$ and $x(t)$ + converges to the stationary state. + + + Then, s = aW*s + (1-a)b + (I-aW)*s = (1-a)b + s = (I-aW)^-1 * (1-a)b + s = M*b, where M is (1-a)(I-aW)^-1. + + This method is to get the matrix, M for preparing the exact solution + """ + W = self._W + a = self._params.alpha + M0 = np.eye(W.shape[0]) - a*W + self._M = (1-a)*np.linalg.inv(M0) + # end of def _prepare_exact_solution + + def prepare_iterative_solution(self): + pass # Nothing... + # end of def prepare_iterative_solution + + def propagate_exact(self, b): + if self._weight_matrix_invalidated: + self.prepare_exact_solution() + + return self._M.dot(b) + # end of def propagate_exact + + def propagate_iterative(self, + W, + xi, + b, + pi, + a=0.5, + lim_iter=1000, + tol=1e-5, + get_trj=False): + + + n = W.shape[0] + # Initial values + + #x0 = np.zeros((n,), dtype=np.float) + #x0[:] = xi + x0 = np.array(xi, dtype=np.float64) + #print(x0) + + x_t1 = x0.copy() + if get_trj: + # Record the initial states + trj_x = [] + trj_x.append(x_t1.copy()) + + # Main loop + num_iter = 0 + for i in range(lim_iter): + # Main formula + x_t2 = a*W.dot(x_t1) + (1-a)*b + for j in pi: + x_t2[j] = x0[j] + + num_iter += 1 + # Check termination condition + if np.linalg.norm(x_t2 - x_t1) <= tol: + break + + # Add the current state to the trajectory + if get_trj: + trj_x.append(x_t2) + + # Update the state + x_t1 = x_t2.copy() +## print("***") + # end of for + if get_trj is False: + return x_t2, num_iter + else: +## print(np.array(trj_x)) + return x_t2, np.array(trj_x) + + # end of def propagate_iterative +# end of def class SignalPropagation diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/analysis/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/analysis/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- + + +from .perturb import analyze_perturb + +from .random.weight import RandomWeightBatchSimulator +from .random.structure import RandomStructureBatchSimulator + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/analysis/perturb.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/analysis/perturb.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,122 @@ +# -*- coding: utf-8 -*- + +import numpy as np + + +def analyze_perturb(alg, data, targets, b=None, get_trj=False): + """Perform signal flow analysis under perturbations. + + Parameters + ---------- + alg : sfa.Algorithm + Algorithm object. + + data : sfa.Data + Data object which has perturbation data. + + targets : list + List of node names, which are the keys of data.n2i. + + b : numpy.ndarray + Basic vector for signaling sources or basal activities. + + get_trj : bool (optional) + Decide to get the trajectory of activity change. + + Returns + ------- + act : numpy.ndarray + Change in the activities. It is usually calculated + as x2 - x1, where x is + the a vector of activities at steady-state. + + F : numpy.ndarray + A matrix of signal flows. + It is usually calculated as W2*x1 - W1*x1, + where W is weight matrix and + x is a vector of activities at steady-state. + + trj : numpy.ndarray (optional) + Trajectory of activity change, which is returned + if get_trj is True. + """ + N = data.A.shape[0] + + if b is None: + b = np.zeros((N,), dtype=np.float) + elif b.size != N: + raise TypeError("The size of b should be equal to %d"%(N)) + + inds = [] + vals = [] + alg.apply_inputs(inds, vals) + b[inds] = vals + + W_ctrl = alg.W.copy() + x_ctrl, trj_ctrl = alg.propagate_iterative( + W_ctrl, + b, + b, + alg.params.alpha, + get_trj=get_trj) + + if data.has_link_perturb: + W_pert = W_ctrl.copy() + alg.apply_perturbations(targets, inds, vals, W_pert) + alg.W = W_pert + else: + W_pert = W_ctrl + alg.apply_perturbations(targets, inds, vals) + + b[inds] = vals + x_pert, trj_pert = alg.propagate_iterative( + W_pert, + b, + b, + alg.params.alpha, + get_trj=get_trj) + + act_change = x_pert - x_ctrl + + if data.has_link_perturb: + F = W_pert*x_pert - W_ctrl*x_ctrl + else: + F = W_ctrl*act_change + + ret = [act_change, F] # return objects + if get_trj: + if trj_pert.shape[0] != trj_ctrl.shape[0]: + trj_ctrl, trj_pert = resize_trj(trj_ctrl, trj_pert) + + trj_change = trj_pert - trj_ctrl + ret.append(trj_change) + + return tuple(ret) + + +def resize_trj(trj_ctrl, trj_pert): + # Prepare the comparison + trjs = [trj_pert, trj_ctrl] + ind_trjs = [0, 1] + func_key = lambda x: trjs[x].shape[0] + + # Find smaller and bigger arrays. + ind_smaller = min(ind_trjs, key=func_key) + ind_bigger = max(ind_trjs, key=func_key) + smaller = trjs[ind_smaller] + bigger = trjs[ind_bigger] + + # Resize the smaller one. + smaller_resized = np.zeros_like(bigger) + smaller_resized[:smaller.shape[0], :] = smaller + smaller_resized[smaller.shape[0]:, :] = smaller[-1, :] + + if ind_smaller == 0: + trj_pert = smaller_resized + elif ind_smaller == 1: + trj_ctrl = smaller_resized + else: + err_msg = "Invalid index for trajectories: %d" % (ind_smaller) + raise IndexError(err_msg) + + return trj_ctrl, trj_pert \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/analysis/random/__init__.py diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/analysis/random/base.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/analysis/random/base.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,260 @@ +# -*- coding: utf-8 -*- + +from multiprocessing import Pool + +import numpy as np +import pandas as pd + +import sfa + + +class BaseRandomSimulator(object): + + def __init__(self): + # Consider RuntimeWarning from NumPy as an error + np.seterr(all='raise') + + def _initialize(self, alg): + self._S = np.sign(alg.data.A) # Sign matrix + self._ir, self._ic = alg.data.A.nonzero() + self._num_links = self._ir.size + self._A = np.array(alg.data.A) + self._W = np.zeros_like(self._A, dtype=np.float) + + def _randomize(self): + raise NotImplementedError() + + def _apply_norm(self, alg, use_norm): + """ + Apply normalization + """ + if use_norm: + alg.W = sfa.normalize(self._W) + else: + alg.W = self._W + + # end of def + + def _need_to_print(self, use_print, freq_print, cnt): + return use_print and (cnt % freq_print == 0) + + def simulate_single(*args, **kwargs): + raise NotImplementedError() + + def simulate_multiple(*args, **kwargs): + raise NotImplementedError() + + """ + def _simulate_single(self, args): + num_samp = args[0] + alg = args[1] + data = args[2] + use_norm = args[3] + use_print = args[4] + freq_print = args[5] + + alg.data = data + alg.initialize(network=False) + + results = np.zeros((num_samp,), dtype=np.float) + cnt = 0 + + if self._need_to_print(use_print, freq_print, cnt): + print("%s simulation for %s starts..." % (alg.abbr, data.abbr)) + + while cnt < num_samp: + self._randomize() + self._apply_norm(alg, use_norm) + try: + alg.compute_batch() + acc = sfa.calc_accuracy(self._alg.result.df_sim, + self._alg.data.df_exp) + except FloatingPointError as pe: + # Skip this condition + if self._need_to_print(use_print, freq_print, cnt): + print("%s: skipped..." % (pe)) + continue + except RuntimeWarning as rw: + # Skip these weights + if self._need_to_print(use_print, freq_print, cnt): + print("%s: skipped..." % (rw)) + continue + + # Skip these weights assuming acc cannot be exactly 0. + if acc == 0: + if self._need_to_print(use_print, freq_print, cnt): + print("Zero accuracy: skipped...") + continue + + results[cnt] = acc + cnt += 1 + if self._need_to_print(use_print, freq_print, cnt): + print("[Iteration #%d] acc: %f" % (cnt, acc)) + # end of loop + df = pd.DataFrame(results) + df.index = range(1, num_samp + 1) + df.columns = [data.abbr] + return df + # end of def + + def simulate_single(self, num_samp, alg, data, use_norm=False, + use_print=False, freq_print=100): + self._alg = alg + self._alg.data = data + self._alg.initialize() + self._initialize(alg) + + df = self._simulate_single((num_samp, alg, data, use_norm, + use_print, freq_print)) + return df + + # end of def + + def simulate_multiple(self, num_samp, alg, mdata, use_norm=False, + use_print=False, freq_print=100, + max_workers=1): + self._alg = alg + + # Initialize network information only + self._alg.data = sfa.get_avalue(mdata) + self._alg.initialize() + self._initialize(alg) + + if isinstance(mdata, list): + list_data = [(data.abbr, data) for data in mdata] + elif isinstance(mdata, dict): + list_data = [(abbr, mdata[abbr]) for abbr in mdata] + + dfs = [] + if max_workers == 1: + for (abbr, data) in list_data: + df = self._simulate_single(num_samp, alg, data, use_norm, + use_print, freq_print) + dfs.append(df) + # end of for + elif max_workers > 1: + args = ((num_samp, alg, data, use_norm, use_print, freq_print) + for (abbr, data) in list_data) + pool = Pool(processes=max_workers) + dfs = list(pool.map(self._simulate_single, args)) + pool.close() + pool.join() + else: + raise ValueError("max_workers should be a positive integer.") + + df_res = pd.concat(dfs, axis=1) + return df_res + # end of def + """ + +# end of class + + +class BaseRandomBatchSimulator(BaseRandomSimulator): + def __init__(self): + super().__init__() + + def _randomize(self): + raise NotImplementedError() + + def _simulate_single(self, args): + num_samp = args[0] + alg = args[1] + data = args[2] + use_norm = args[3] + use_print = args[4] + freq_print = args[5] + + alg.data = data + alg.initialize(network=False) + + results = np.zeros((num_samp,), dtype=np.float) + cnt = 0 + + if self._need_to_print(use_print, freq_print, cnt): + print("%s simulation for %s starts..." % (alg.abbr, data.abbr)) + + while cnt < num_samp: + self._randomize() + self._apply_norm(alg, use_norm) + try: + alg.compute_batch() + acc = sfa.calc_accuracy(self._alg.result.df_sim, + self._alg.data.df_exp) + except FloatingPointError as pe: + # Skip this condition + if self._need_to_print(use_print, freq_print, cnt): + print("%s: skipped..." % (pe)) + continue + except RuntimeWarning as rw: + # Skip these weights + if self._need_to_print(use_print, freq_print, cnt): + print("%s: skipped..." % (rw)) + continue + + # Skip these weights assuming acc cannot be exactly 0. + if acc == 0: + if self._need_to_print(use_print, freq_print, cnt): + print("Zero accuracy: skipped...") + continue + + results[cnt] = acc + cnt += 1 + if self._need_to_print(use_print, freq_print, cnt): + print("[Iteration #%d] acc: %f" % (cnt, acc)) + # end of loop + df = pd.DataFrame(results) + df.index = range(1, num_samp + 1) + df.columns = [data.abbr] + return df + # end of def + + def simulate_single(self, num_samp, alg, data, use_norm=False, + use_print=False, freq_print=100): + self._alg = alg + self._alg.data = data + self._alg.initialize() + self._initialize(alg) + + df = self._simulate_single((num_samp, alg, data, use_norm, + use_print, freq_print)) + return df + + # end of def + + def simulate_multiple(self, num_samp, alg, mdata, use_norm=False, + use_print=False, freq_print=100, + max_workers=1): + self._alg = alg + + # Initialize network information only + self._alg.data = sfa.get_avalue(mdata) + self._alg.initialize() + self._initialize(alg) + + if isinstance(mdata, list): + list_data = [(data.abbr, data) for data in mdata] + elif isinstance(mdata, dict): + list_data = [(abbr, mdata[abbr]) for abbr in mdata] + + dfs = [] + if max_workers == 1: + for (abbr, data) in list_data: + df = self._simulate_single(num_samp, alg, data, use_norm, + use_print, freq_print) + dfs.append(df) + # end of for + elif max_workers > 1: + args = ((num_samp, alg, data, use_norm, use_print, freq_print) + for (abbr, data) in list_data) + pool = Pool(processes=max_workers) + dfs = list(pool.map(self._simulate_single, args)) + pool.close() + pool.join() + else: + raise ValueError("max_workers should be a positive integer.") + + df_res = pd.concat(dfs, axis=1) + return df_res + # end of def +# end of class diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/analysis/random/structure.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/analysis/random/structure.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- + +import sfa +from .base import BaseRandomBatchSimulator + + +class RandomStructureBatchSimulator(BaseRandomBatchSimulator): + def __init__(self, *args, nswap=10, nflip=10, noself=True, **kwargs): + super().__init__(*args, **kwargs) + self._nswap = nswap + self._nflip = nflip + self._noself = noself + + def _randomize(self): + B = sfa.rand_flip(self._A, self._nflip) + B = sfa.rand_swap(B, self._nswap, self._noself) + ir, ic = B.nonzero() + self._W[ir, ic] = B[ir, ic] +# end of class diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/analysis/random/weight.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/analysis/random/weight.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- + +import sfa +from .base import BaseRandomBatchSimulator + + +class RandomWeightBatchSimulator(BaseRandomBatchSimulator): + def __init__(self, *args, bounds=(-3,0), **kwargs): + super().__init__(*args, **kwargs) + self._lb = bounds[0] + self._ub = bounds[1] + + def _randomize(self): + sfa.rand_weights(self._W, self._lb, self._ub, inplace=True) +# end of class + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/base.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/base.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,344 @@ +import os +import sys +if sys.version_info <= (2, 8): + from builtins import super + +# from abc import ABC, abstractmethod +import abc +import copy + +import pandas as pd +import six +import sfa.utils + +__all__ = ['Algorithm', 'Data', 'Result'] + + +@six.add_metaclass(abc.ABCMeta) +class ContainerItem(): + """ + The base class that defines the item object of + ``sfa.containers.Container``. + + """ + def __init__(self, abbr=None, name=None): + self._abbr = abbr + self._name = name + + def __str__(self): + return self._abbr + + def __repr__(self): + class_name = self.__class__.__name__ + return "%s object" % (class_name) + + @property + def abbr(self): + """Abbreviation or symbol representing this item. + """ + return self._abbr + + @abbr.setter + def abbr(self, val): + self._abbr =val + + @property + def name(self): + """Full name or description of this item. + """ + return self._name + + @name.setter + def name(self, val): + self._name = val + + +class ParameterSet(sfa.utils.FrozenClass): + """The base class of ParameterSet objects. + """ + + def __init__(self, abbr): + """ + """ + super().__init__(abbr) + + +class Algorithm(ContainerItem): + """The base class of Algorithm classes. + + Attributes + ---------- + abbr : str + name : str + data : sfa.base.Data + params : sfa.base.ParameterSet + result : sfa.base.Result + + Examples + -------- + >>> class AnAlgorithm(sfa.base.Algorithm): + # Definition of algorithm ... + ... + + >>> alg = AnAlgorithm() + >>> alg.params = params_obj # Parameters of the algorithm + >>> alg.data = data_obj # Data to be analyzed by the algorithm + >>> alg.initialize() + >>> res = alg.compute() + + """ + def __init__(self, abbr): + super().__init__(abbr) + self._data = None + self._params = None + self._result = None + + def copy(self, is_deep=False): + """ + + """ + if is_deep: + copy.deepcopy(self) + else: + return copy.copy(self) + + # Read-only properties + @property + def result(self): + """The object of ``sfa.base.Result``. + The result of computing the batch. + """ + return self._result + + # Read & write properties + @property + def params(self): + """The object of ``sfa.base.ParameterSet``. + Parameters of the algorithm can accessed + through this member. + """ + return self._params + + @params.setter + def params(self, obj): + self._params = obj + + @property + def data(self): + """The object of ``sfa.base.Data``. + Data to be processed based on the algorithm + can accessed through this member. + """ + return self._data + + @data.setter + def data(self, obj): + self._data = obj + + def initialize(self, network=True, ba=True): + """ + """ + if network: + self.initialize_network() + + if ba: + self.initialize_basal_activity() + + def initialize_network(self): + """Initialize the data structures related to network. + """ + pass + + def initialize_basal_activity(self): + """Initialize the basal activity, :math:`b`. + """ + pass + + @abc.abstractmethod + def compute(self, b,pi): + r"""Process the assigned data + with the given basal activity, :math:`b`. + + Parameters + ---------- + b : numpy.ndarray + 1D array of basal activity. + + pi: list + list of fixed node perturbations + Returns + ------- + x : numpy.ndarray + 1D-array object of activity at steady-state. + """ + raise NotImplementedError("compute() should be implemented") + + @abc.abstractmethod + def compute_batch(self): + """Process the assigned data that contains a batch data. + The result is stored in ``result`` member. + """ + raise NotImplementedError("compute_batch() should be implemented") + +# end of class Algorithm + + +class Data(ContainerItem): + def __init__(self): + super().__init__() + self._A = None + self._n2i = None + self._i2n = None + self._dg = None + self._inputs= None + self._df_conds = None + self._df_exp = None + self._df_ptb = None + self._names_ptb = None + self._iadj_to_idf = None + self._has_link_perturb = None + + def initialize(self, + fpath, + fname_network="network.sif", + fname_ptb="ptb.tsv", + fname_conds="conds.tsv", + fname_exp="exp.tsv", + inputs={}): + + dpath = os.path.dirname(fpath) + fpath_network = os.path.join(dpath, fname_network) + fpath_ptb = os.path.join(dpath, fname_ptb) + + A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True) + self._A = A + self._n2i = n2i + self._dg = dg + self._df_conds = pd.read_table(os.path.join(dpath, fname_conds), + header=0, index_col=0) + self._df_exp = pd.read_table(os.path.join(dpath, fname_exp), + header=0, index_col=0) + + self._inputs = inputs + self._df_ptb = pd.read_table(fpath_ptb, index_col=0) + if any(self._df_ptb.Type == 'link'): + self._has_link_perturb = True + else: + self._has_link_perturb = False + + self._names_ptb = [] + for i, row in enumerate(self._df_conds.iterrows()): + row = row[1] + list_name = [] # Target names + for target in self._df_conds.columns[row.nonzero()]: + list_name.append(target) + # end of for + self._names_ptb.append(list_name) + # end of for + + # For mapping from the indices of adj. matrix to those of DataFrame + # (arrange the indices of adj. matrix according to df_exp.columns) + self._iadj_to_idf = [n2i[x] for x in self._df_exp.columns] + + self._i2n = {idx: name for name, idx in n2i.items()} + + # end of def + + # Read-only members + @property + def A(self): # Adjacency matrix (numpy.ndarray) + return self._A + + @property + def n2i(self): # Name to index mapping (hashable) + return self._n2i + + @property + def i2n(self): # Index to name mapping (hashable) + return self._i2n + + @property + def dg(self): # Directed graph object of NetworkX + return self._dg + + @property # List of perturbation targets + def names_ptb(self): + return self._names_ptb + + @property # List of values for perturbation + def vals_ptb(self): + return self._vals_ptb + +# @property # List of perturbation types +# def types_ptb(self): +# return self._types_ptb + + @property + def iadj_to_idf(self): + return self._iadj_to_idf + + @iadj_to_idf.setter + def iadj_to_idf(self, arr): + self._iadj_to_idf = arr + + # Replaceable (assignable) members + @property + def inputs(self): # Input conditions + return self._inputs + + @inputs.setter + def inputs(self, obj_dict): + self._inputs = obj_dict + + @property # DataFrame of experimental conditions + def df_conds(self): + return self._df_conds + + @df_conds.setter + def df_conds(self, df): + self._df_conds = df + + @property # DataFrame of experimental results + def df_exp(self): + return self._df_exp + + @df_exp.setter + def df_exp(self, df): + self._df_exp = df + + @property # DataFrame of perturbation information + def df_ptb(self): + return self._df_ptb + + @df_ptb.setter + def df_ptb(self, df): + self._df_ptb = df + + @property + def has_link_perturb(self): + return self._has_link_perturb + + @has_link_perturb.setter + def has_link_perturb(self, val): + if not isinstance(val, bool): + raise TypeError("has_link_perturb should be boolean.") + self._has_link_perturb = val + +# end of class Data + + +class Result(sfa.utils.FrozenClass): + + def __init__(self): + self._df_sim = None + self._freeze() + + @property + def df_sim(self): + return self._df_sim + + @df_sim.setter + def df_sim(self, val): + self._df_sim = val + +# end of def class Result diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/containers.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/containers.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,267 @@ +# -*- coding: utf-8 -*- + +import sys +if sys.version_info <= (2, 8): + from builtins import super + +import os +import re +import importlib +import collections + +import abc +import six + +import sfa.base +import sfa.algorithms +import sfa.data + +from sfa.utils import Singleton + +__all__ = ["AlgorithmSet", "DataSet"] + +@six.add_metaclass(abc.ABCMeta) +class Container(collections.MutableMapping): + """ + A simple container class, which handles multiple objects with + its hashable functionality (using dictionary). + """ + def __init__(self, *args, **kwargs): + """ + _dict: internal data structure, which is hashable. + _dpath: Path of the directory containing algorithms, data, etc. + """ + self._map = dict() + self._dpath = None + self._all_keys = None + self.update(dict(*args, **kwargs)) + + def __getitem__(self, key): + return self._map[key] + + def __setitem__(self, key, value): + if isinstance(value, sfa.base.ContainerItem): + self._map[key] = value + else: + raise TypeError("Container.__setitem__ only accepts " + "sfa.base.ContainerItem type object.") + + def __delitem__(self, key): + del self._map[key] + + def __iter__(self): + return iter(self._map) + + def __len__(self): + return len(self._map) + + def __str__(self): + return str(self._map) + + def keys(self): + return self._map.keys() + + def values(self): + return self._map.values() + + def create(self, keys=None): + """ + Create a single or multiple objects according to keys. + keys: a single string or multiple strings in an iterable object. + All related objects are created if 'keys' is None. + """ + if keys is not None: + if type(keys) is str: + return self._create_single(keys) + elif hasattr(keys, '__iter__'): + mult = [] # To return multiple elements + # An iterable object contains multiple keys. + for elem in keys: + mult.append(self._create_single(elem)) + return mult + else: + self._create_all() + return self + # end of def create + + @abc.abstractmethod + def get_all_keys(self): + """""" + # end of def + + @abc.abstractmethod + def _create_single(self, key): + """Create a single object""" + # end of def + + @abc.abstractmethod + def _create_all(self): + """Create all objects""" + # end of def + + + +""" + + +[Dictionary functionality] +http://stackoverflow.com/questions/3387691/python-how-to-perfectly-override-a-dict +""" +# end of def class + +excluded = ['np.py'] + +@Singleton +class AlgorithmSet(Container): + _instance = None + def __new__(cls, *args, **kwargs): + return super().__new__(cls, *args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + """ + sfa.algorithms.__file__ represents a directory path + containing sfa.algorithms's init module (__init__.py). + """ + self._dpath = os.path.dirname(sfa.algorithms.__file__) + + # end of def __init__ + + def get_all_keys(self): + if not self._all_keys: + self._all_keys = [] + for entity in os.listdir(self._dpath): + if re.match(r"[^_]\w+\.py$", entity) \ + and entity not in excluded: + mod_name = entity.split('.')[0] # Module name + print(mod_name, entity) + key = mod_name.upper() + self._all_keys.append(key) + yield key + # end of for + else: + return iter(self._all_keys) + + def _create_single(self, key): + if key in self._map: + return self._map[key] + + + key_low = key.lower() + fstr_module_path = "%s.%s" % (sfa.algorithms.__name__, + key_low) + + _key = key.upper() # We use captital characters for the key. + if _key in self._map: # Avoid redundant importing + return + + mod = importlib.import_module(fstr_module_path) + # The following is of test purpose (removable in the future). + if "this_should_be_imported" in mod.__name__: + return + + alg = mod.create_algorithm(_key) + self._map[_key] = alg + + # For testing purpose + print("%s algorithm has been created." % (_key)) + return alg + + def _create_all(self): + """ + Import all algorithms, based on file names + """ + for entity in os.listdir(self._dpath): + if re.match(r"[^_]\w+\.py", entity) \ + and entity not in excluded: + mod_name = entity.split('.')[0] # Module name + self._create_single(mod_name) + # end of for + # end of def _create_all + +# end of class Algorithms + +@Singleton +class DataSet(Container): + """ + The name of this class is similar to that of 'DataSet' in C#. + The instance of this class handles multiple sfa.base.Data objects. + """ + _instance = None + def __new__(cls, *args, **kwargs): + return super().__new__(cls, *args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + """ + sfa.data.__file__ represents a directory path + containing sfa.data's init module (__init__.py). + """ + self._dpath = os.path.dirname(sfa.data.__file__) + + # end of def __init__ + + def get_all_keys(self): + if not self._all_keys: + self._all_keys = [] + for entity in os.listdir(self._dpath): + dpath = os.path.join(self._dpath, entity) + if not entity.startswith('_') and os.path.isdir(dpath): + key = entity.upper() + self._all_keys.append(key) + yield key + else: + return iter(self._all_keys) + + def _create_single(self, key): + if key in self._map: + return self._map[key] + + key_items = key.split("_") + key_1st, key_2nd = key_items[:2] + mod_name = "%s_%s"%(key_1st.lower(), key_2nd.lower()) + fstr_module_path = "%s.%s" % (sfa.data.__name__, mod_name) + + _key = key.upper() + if _key in self._map: # Avoid redundant importing + return + elif mod_name.upper() in self._map: + # All data object in this directory has been created before. + return + + mod = importlib.import_module(fstr_module_path) + if len(key_items) > 2: # Create the specified single data object + data = mod.create_data(key) + else: # Create all data objects from this directory + data = mod.create_data() + + if isinstance(data, dict): + self._map[_key] = data + elif isinstance(data, list): + self._map[_key] = {obj.abbr.upper(): obj for obj in data} + elif isinstance(data, sfa.base.Data): + _key = data.abbr.upper() + self._map[_key] = data + else: + err_msg = "%s.create_data() returns unsupported type."\ + % (fstr_module_path) + raise TypeError(err_msg) + # end of if + + # For testing purpose + print("%s data has been created." % (_key)) + return self._map[_key] + # end of def _create_single + + def _create_all(self): + """ + Import all data, based on the directory names of data modules + """ + for entity in os.listdir(self._dpath): + dpath = os.path.join(self._dpath, entity) + if not entity.startswith('_') and os.path.isdir(dpath): + self._create_single(entity) + # end of for + # end of def _create_all + +# end of def class DataSet diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/control/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/control/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,4 @@ + +from .influence import compute_influence +from .influence import arrange_si +from .influence import prioritize \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/control/influence.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/control/influence.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,347 @@ +from collections import Counter + +import numpy as np +import scipy as sp +import pandas as pd + + +def compute_influence(W, + alpha=0.9, + beta=0.1, + S=None, + rtype='df', + outputs=None, + n2i=None, + max_iter=1000, + tol=1e-7, + get_iter=False, + device="cpu", + sparse=False): + r"""Compute the influence. + It estimates the effects of a node to the other nodes, + by calculating partial derivative with respect to source nodes, + based on a simple iterative method. + + Based on the below difference equation, + + x(t+1) = alpha*W.dot(x(t)) + (1-alpha)*b + + The influence matrix, S, is computed using chain rule of + partial derivative as follows. + + \begin{align} + S_{ij} &= \frac{\partial{x_i}}{\partial{x_j}} \\ + &= (I + \alpha W + \alpha^2 W^2 + ... + \alpha^{\infty}W^{\infty})_{ij} \\ + &\approx (I + \alpha W + \alpha^2 W^2 + ... + \alpha^{l}W^{l})_{ij} \\ + \end{align} + + This is the summation of the weight multiplications along all paths + including cycles. $S_{ij}$ denotes the influence of node (j) on node (i). + + An iterative method for an approximated solution is as follows. + + S(t+1) = \alpha WS(t) + I, + + where $S(0) = \beta I$ and $S(1) = \beta(I + \alpha W)$ $(t>1)$. + + The iteration continues until $||S(t+1) - S(t)|| \leq tol$. + + + Parameters + ---------- + W : numpy.ndarray + Weight matrix. + alpha : float, optional + Hyperparameter for adjusting the effect of signal flow. + beta : float, optional + Hyperparameter for adjusting the effect of basal activity. + S : numpy.ndarray, optional + Initial influence matrix. + rtype: str (optional) + Return object type: 'df' or 'array'. + outputs: list (or iterable) of str, optional + Names of output nodes, which is necessary for 'df' rtype. + n2i: dict, optional + Name to index dict, which is necessary for 'df' rtype. + max_iter : int, optional + The maximum iteration number for the estimation. + tol : float, optional + Tolerance for terminating the iteration. + get_iter : bool, optional + Determine whether the actual iteration number is returned. + device : str, optional, {'CPU', 'GPU:0', 'GPU:1', ...} + Select which device to use. 'CPU' is default. + sparse : bool, optional + Use sparse matrices for the computation. + + Returns + ------- + S : numpy.ndarray, optional + 2D array of influence. + df : pd.DataFrame, optional + Influences for each output in DataFrame. + num_iter : int, optional + The actual number of iteration. + """ + # TODO: Test rendering the above mathematical expressions in LaTeX form. + + if max_iter < 2: + raise ValueError("max_iter should be greater than 2.") + + device = device.lower() + + if 'cpu' in device: + if sparse: + ret = _compute_influence_cpu_sparse(W, alpha, beta, S, + max_iter, tol, get_iter) + else: + ret = _compute_influence_cpu(W, alpha, beta, S, + max_iter, tol, get_iter) + elif 'gpu'in device: + _, id_device = device.split(':') + ret = _compute_influence_gpu(W, alpha, beta, S, + max_iter, tol, get_iter, id_device) + + if rtype == 'df': + import cupy as cp + if isinstance(ret, cp.core.core.ndarray): + ret = cp.asnumpy(ret) + + if get_iter: + S_ret, num_iter = ret + else: + S_ret = ret + + if rtype == 'array': + return ret + elif rtype == 'df': + if not outputs: + err_msg = "outputs should be designated for 'df' return type." + raise ValueError(err_msg) + + df = pd.DataFrame(columns=outputs) + + for trg in outputs: + for src in n2i: + if src == trg: + df.loc[src, trg] = np.inf + + idx_src = n2i[src] + idx_trg = n2i[trg] + df.loc[src, trg] = S_ret[idx_trg, idx_src] + + if get_iter: + return df, num_iter + else: + return df + else: + raise ValueError("Unknown return type: %s"%(rtype)) + + +def _compute_influence_cpu(W, alpha=0.5, beta=0.5, S=None, + max_iter=1000, tol=1e-6, get_iter=False): + N = W.shape[0] + if S is not None: + S1 = S + else: + S1 = np.eye(N, dtype=np.float) + + I = np.eye(N, dtype=np.float) + S2 = np.zeros_like(W) + aW = alpha * W + for cnt in range(max_iter): + S2[:, :] = S1.dot(aW) + I + norm = np.linalg.norm(S2 - S1) + if norm < tol: + break + # end of if + S1[:, :] = S2 + # end of for + + S_fin = beta * S2 + if get_iter: + return S_fin, cnt + else: + return S_fin + + +def _compute_influence_cpu_sparse(W, alpha, beta, S, + max_iter, tol, get_iter): + N = W.shape[0] + if S is not None: + S1 = S + else: + S1 = sp.sparse.lil_matrix(sp.sparse.eye(N, dtype=np.float)) + + + I = sp.sparse.eye(N, dtype=np.float) + S2 = sp.sparse.lil_matrix((N,N), dtype=np.float) + aW = sp.sparse.csc_matrix(alpha * W) + for cnt in range(max_iter): + S2[:, :] = S1.dot(aW) + I + norm = sp.sparse.linalg.norm(S2 - S1) + if norm < tol: + break + # end of if + S1[:, :] = S2 + # end of for + + S_fin = beta * S2 + if get_iter: + return S_fin, cnt + else: + return S_fin + + +def _compute_influence_gpu(W, alpha=0.5, beta=0.5, S=None, + max_iter=1000, tol=1e-6, get_iter=False, + id_device=0): + import cupy as cp + cp.cuda.Device(id_device).use() + N = W.shape[0] + I = cp.eye(N, dtype=cp.float32) #np.eye(N, N, dtype=np.float) + if S is not None: + S1 = cp.array(S, dtype=cp.float32) + else: + S1 = cp.eye(N, dtype=cp.float32) + + S2 = cp.zeros((N,N), dtype=cp.float32) + aW = alpha * cp.array(W, dtype=cp.float32) + + tol_gpu = cp.array(tol) + + for cnt in range(max_iter): + S2[:, :] = cp.dot(S1, aW) + I + mat_norm = cp.linalg.norm(S2 - S1) + if mat_norm < tol_gpu: + break + # end of if + S1[:, :] = S2 + # end of for + + S_fin = beta*S2 + if get_iter: + return S_fin, cnt + else: + return S_fin + + + +def arrange_si( + df_splo, + df_inf, + output, + min_splo=None, + max_splo=None, + thr_inf=1e-10, + ascending=True): + + # SPLO-Influence data + if not min_splo: + min_splo = df_splo.min() + + if not max_splo: + max_splo = df_splo.max() + + mask_splo = (min_splo <= df_splo) & (df_splo <= max_splo) + df_splo = df_splo[mask_splo] + + df_splo = pd.DataFrame(df_splo) + df_splo.columns = ['SPLO'] + + if output in df_splo.index: + df_splo.drop(output, inplace=True) + + index_common = df_splo.index.intersection(df_inf.index) + df_inf = pd.DataFrame(df_inf.loc[index_common]) + + mark_drop = df_inf[output].abs() <= thr_inf + df_inf.drop(df_inf.loc[mark_drop, output].index, + inplace=True) + + + df_si = df_inf.join(df_splo.loc[index_common]) + df_si.index.name = 'Source' + df_si.reset_index(inplace=True) + + cnt_splo = Counter(df_si['SPLO']) + splos = sorted(cnt_splo.keys()) + + si = {} + for i, splo in enumerate(splos): + df_sub = df_si[df_si['SPLO'] == splo] + df_sub = df_sub.sort_values(by=output, + ascending=ascending) + #num_items = df_sub[output].count() + #influence = np.zeros((cnt_max,)) # Influence + #num_empty = cnt_max - num_items + #influence[num_empty:] = df_sub[output] + #names = df_sub['Source'].tolist() + si[splo] = df_sub #[output] + + return si + + +def prioritize(df_splo, + df_inf, + output, + dac, + thr_rank=3, + min_group_size=0, + min_splo=None, + max_splo=None, + thr_inf=1e-10, +): + """Prioritize target candiates. + + Parameters + ---------- + df_splo : pandas.DataFrame + Dataframe for SPLO information. + df_inf : pandas.DataFrame + Dataframe for influence information. + output : str + Names of output node, which is necessary for 'df_inf'. + dac : int + Direction of activity change (DAC) of the output. + thr_rank : int or float + Rank to filter out the entities. + The entities whose ranks are greater than thr_rank survive. + min_group_size : int + Minimum group size to be satisfied. + """ + ascending = True if dac < 0 else False + + df_inf_dac = df_inf[np.sign(df_inf[output]) == dac] + si = arrange_si(df_splo, + df_inf_dac, + output, + min_splo, + max_splo, + thr_inf, + ascending) + targets = [] + for splo in si: + # Get the group of this SPLO. + df_sub = si[splo] + + if df_sub.shape[0] < min_group_size: + continue + + # Get the entities that have the designated dac. + df_sub = df_sub[np.sign(df_sub[output]) == dac] + + # Get the enetities whose rank exceeds the threshods. + if 0 < thr_rank < 1: + ix_max_rank = int(thr_rank * df_sub.shape[0]) + if ix_max_rank == 0: + ix_max_rank = df_sub.shape[0] + else: + ix_max_rank = thr_rank + + #print(ix_max_rank) + df_top = df_sub.iloc[:ix_max_rank, :] + + targets.extend(df_top['Source'].tolist()) + # end of for + return targets \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,2 @@ +# -*- coding: utf-8 -*- + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/__pycache__/__init__.cpython-36.pyc Binary file tools/myTools/bin/sfa/data/__pycache__/__init__.cpython-36.pyc has changed diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/__pycache__/__init__.cpython-39.pyc Binary file tools/myTools/bin/sfa/data/__pycache__/__init__.cpython-39.pyc has changed diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/borisov_2009/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/borisov_2009/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,193 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Borisov, N. et al. +Systems-level interactions between insulin-EGF networks amplify mitogenic +signaling. +Molecular Systems Biology (2009) 5(1), 256. +http://doi.org/10.1038/msb.2009.19 + +[Information] +- The experimental data was generated using ODE model of Borisov et al. + (It is hypothesized that the ODE model is quite well constructed enough to be + a substitute for real experimental data). +- The directed network was created by Daewon Lee (dwl@kaist.ac.kr). +- The units of EGF and insulin (I) stimulation are nM. +""" + +import sys +if sys.version_info <= (2, 8): + from builtins import super + +import os +import re +import glob + +import pandas as pd + +import sfa +import sfa.base + + +def create_data(abbr=None): + if abbr is None: # Create all data objects + data_mult = {} # Multiple data + dpath = os.path.dirname(__file__) + + fstr_file = os.path.join(dpath, 'exp_data', 'exp_*') + for abspath in glob.glob(fstr_file): + fname = os.path.basename(abspath) + data_obj = _create_single_data(abbr, fname=fname) + data_mult[data_obj.abbr] = data_obj + + # end of for + + return data_mult + else: # Create a single data object + return _create_single_data(abbr) + +# end of def + +def create_test_data(): + data_mult = {} + dpath = os.path.dirname(__file__) + + fstr_file = os.path.join(dpath, 'exp_data_test', 'exp_*') + for abspath in glob.glob(fstr_file): + fname = os.path.basename(abspath) + items = re.split("[_.]", fname) + + data_type = items[1] + stim_type = items[2] + + if 'EGF' in stim_type: + conc_EGF = 1 + else: + conc_EGF = 0.0001 + + if 'I' in stim_type: + conc_I = 1 + else: + conc_I = 0.0001 + + #data_obj = #_create_single_data(abbr, fname=fname) + #data_mult[data_obj.abbr] = data_obj + + # str_exp_file = os.path.join(dpath, 'exp_data_test', fname) + # df_exp = pd.read_table(str_exp_file, + # header=0, + # index_col=0) # Load basal activity data + # + # # Load basal activity data + # str_ba_file = os.path.join(dpath, 'exp_data_test', 'conds.tsv') + # df_conds = pd.read_table(str_ba_file, + # header=0, + # index_col=0) + # + # abbr = "BORISOV_2009_%s_%s"%(data_type, stim_type) + # data_obj = BorisovData(abbr, data_type, conc_EGF, conc_I, df_conds, df_exp) + abbr = "BORISOV_2009_%s_%s" % (data_type, stim_type) + fname_conds = os.path.join('exp_data_test', "conds.tsv") + fname_exp = os.path.join('exp_data_test', fname) + + data_obj = BorisovData(abbr, data_type, + conc_EGF, conc_I, + fname_conds, fname_exp) + data_mult[abbr] = data_obj + # end of for + + return data_mult + + + +def _create_single_data(abbr=None, fname=None): + dpath = os.path.dirname(__file__) + + if fname: + items = re.split('[._+]', fname) + + sim_duration = items[1] + data_type = items[2] + stim_EGF = items[3] + stim_I = items[4] + + m = re.search("EGF=((\d|d)+)", stim_EGF) + dconc_EGF = m.group(1) + conc_EGF = dconc_EGF.replace('d', '.') # Use '.' instead of 'd' + + # Fetch the concentration of I + m = re.search("I=((\d|d)+)", stim_I) + dconc_I = m.group(1) + conc_I = dconc_I.replace('d', '.') # Use '.' instead of 'd' + abbr = "%s_%s_EGF=%s+I=%s"%(sim_duration, data_type, conc_EGF, conc_I) + + elif abbr: # Use abbr + items = re.split('[_+]', abbr) + + sim_duration = items[1] + data_type = items[2] + stim_EGF = items[3] + stim_I = items[4] + + # Fetch the concentration of EGF + m = re.search("EGF=((\w|\.)+)", stim_EGF) + conc_EGF = m.group(1) + dconc_EGF = conc_EGF.replace('.', 'd') # Use 'd' instead of '.' + + # Fetch the concentration of I + m = re.search("I=((\w|\.)+)", stim_I) + conc_I = m.group(1) + dconc_I = conc_I.replace('.', 'd') # Use 'd' instead of '.' + + fname = "exp_%s_%s_EGF=%s+I=%s.tsv" % (sim_duration, data_type, + dconc_EGF, dconc_I) + else: + raise ValueError("One of abbr or fname should be given" + "in %s._create_single_data()"%(__name__)) + # end of if-else + + fname_exp = os.path.join('exp_data', fname) + fname_conds = "conds.tsv" + + return BorisovData(abbr, data_type, + conc_EGF, conc_I, + fname_conds, fname_exp) + + # str_exp_file = os.path.join(dpath, 'exp_data', fname) + # df_exp = pd.read_table(str_exp_file, + # header=0, index_col=0) + # + # # Load basal activity data + # str_ba_file = os.path.join(dpath, "conds.tsv") + # df_conds = pd.read_table(str_ba_file, + # header=0, + # index_col=0) + + #return BorisovData(abbr, data_type, conc_EGF, conc_I, df_conds, df_exp) +# end of def + +class BorisovData(sfa.base.Data): + def __init__(self, + abbr, + data_type, + conc_EGF, conc_I, + fname_conds, fname_exp): + + super().__init__() + self._abbr = abbr + fstr_name = "BORISOV_2009_%s[EGF=%snM,I=%snM]" + str_name = fstr_name % (data_type, conc_EGF, conc_I) + self._name = str_name + + inputs = {} + inputs['EGF'] = 1 #float(conc_EGF) + inputs['I'] = 1 #float(conc_I) + + self.initialize(__file__, + inputs=inputs, + fname_conds=fname_conds, + fname_exp=fname_exp) + + # end of def __init__ +# end of def class BorisovData \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/borisov_2009/conds.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/borisov_2009/conds.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,67 @@ + GS GAB1 IRS RasGAP RAS RAF MEK PI3K PIP3 PDK1 mTOR +1 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +2 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +3 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +4 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +5 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 +6 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 +7 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 +8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 +9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 +10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 +11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 +12 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +13 -1.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +14 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +15 -1.0 0.0 0.0 0.0 -1.0 0.0 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b/tools/myTools/bin/sfa/data/borisov_2009/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,47 @@ +I + IR +EGF + EGFR +IR + RasGAP +IR + IRS +IR + SFK +IR + PI3K +EGFR + RasGAP +EGFR + SFK +EGFR + PI3K +EGFR + GAB1 +EGFR + GAB1_pSHP2 +EGFR + SHC +EGFR + GS +SHC + GS +RasGAP - RAS +IRS + IRS_SHP2 +IRS + GS +IRS + PI3K +SFK + IRS +SFK + GAB1 +SFK + GAB1_pSHP2 +SFK + RAF +PI3K + PIP3 +RAS + RAF +IRS_SHP2 - RasGAP +GS + RAS +GAB1 + GAB1_SHP2 +GAB1 + GAB1_pSHP2 +GAB1 + PI3K +GAB1 + GS +GAB1 + RasGAP +GAB1_pSHP2 + GS +GAB1_pSHP2 - RasGAP +PIP3 + PDK1 +PIP3 + IRS +PIP3 + GAB1 +RAF + MEK +PDK1 + AKT +MEK + ERK +AKT + mTOR +AKT - RAF +ERK - GAB1 +ERK - GS +mTOR + AKT +mTOR - IRS +GAB1_SHP2 + GAB1_pSHP2 +GAB1_SHP2 - RasGAP \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/borisov_2009/ptb.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/borisov_2009/ptb.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,14 @@ +Target Type Value Comment +SFK node -1 +SHC node -1 +GS node -1 +GAB1 node -1 +IRS node -1 +RasGAP node -1 +RAS node -1 +RAF node -1 +MEK node -1 +PI3K node -1 +PIP3 node -1 +PDK1 node -1 +mTOR node -1 \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/cho_2016/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/cho_2016/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,57 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Cho et al. +Attractor landscape analysis of +colorectal tumorigenesis and its reversion. +BMC Systems Biology (2016) 10:96. +""" + +import os + +import pandas as pd + +import sfa +import sfa.base + + +def create_data(): + return ChoData() + + +class ChoData(sfa.base.Data): + + def __init__(self): + + self._abbr = "CHO_2015" + self._name = "Cho et al. BMC Systems Biology (2016) 10:96" + + inputs = {} + inputs['ECM'] = 1.0 + inputs['Tgf-b'] = 1.0 + inputs['IL1-TNF'] = 1.0 + inputs['EGF'] = 1.0 + inputs['alpha_i_lig'] = 1.0 + inputs['alpha_12_13_lig'] = 1.0 + inputs['alpha_s_lig'] = 1.0 + inputs['alpha_q_lig'] = 1.0 + inputs['Stress'] = 1.0 + inputs['WNT'] = 1.0 + inputs['Fas'] = 1.0 + inputs['ExtPump'] = 1.0 + inputs['DNA_damage'] = 1.0 + + dpath = os.path.dirname(__file__) + fpath_network = os.path.join(dpath, 'network_all_pos.sif') + A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True) + self._A = A + self._n2i = n2i + self._i2n = {idx: name for name, idx in n2i.items()} + self._dg = dg + self._inputs = inputs + + + # end of def __init__ +# end of def class + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/cho_2016/network_all_neg.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/cho_2016/network_all_neg.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,696 @@ +A20 - TAK1 +A20 - IKK +AA - RhoGDI +AA - PLC_g +AA + PKC +AC + cAMP +Actin + PLD +Actin + Vinc +Akt + bcl2 +Akt - Casp-9 +Akt - ASK1 +Akt - Rac +Akt - Raf +Akt - PAK +Akt + mdmx +Akt + IKK +Akt + mdm2 +Akt - p21 +Akt - p27 +alpha_12_13_lig + alpha_12_13_R +alpha_12_13_R + alpha_12_13_R +alpha_12_13_R + Ga_12_13 +alpha_12_13_R + Palpha_12_13_R +alpha_12_13_R + RalGDS +alpha_12_13_R + Gbg_12_13 +alpha_12_13_R + EGFR +alpha_i_lig + alpha_i_R +alpha_i_R + alpha_i_R +alpha_i_R + Palpha_i_R +alpha_i_R + RalGDS +alpha_i_R + Gbg_i +alpha_i_R + EGFR +alpha_i_R + Gai +alpha_q_lig + alpha_q_R +alpha_q_R + alpha_q_R +alpha_q_R + Palpha_q_R +alpha_q_R + Gaq +alpha_q_R + RalGDS +alpha_q_R + Gbg_q +alpha_q_R + EGFR +alpha_s_lig + alpha_s_R +alpha_s_lig + Gai +alpha_s_R + alpha_s_R +alpha_s_R + Palpha_s_R +alpha_s_R + Src +alpha_s_R + RalGDS +alpha_s_R - Gai +alpha_s_R + Gbg_s +alpha_s_R + Gas +AND_3_4 + Ral +APC - beta-catenin +ARF + PI5K +ARF + PLD +ARF + PI4K +Arp_2_3 + Actin +ASK1 + MKK3 +ASK1 + MKK6 +ASK1 + Sek1 +ASK1 + MKK7 +atm + e2f1 +atm - mdm2 +atm + rb +atm - mdmx +atm + p53 +atm + BRCA1 +B_Arrestin + Src +B_Arrestin + RalGDS +B_Arrestin + GRK +B_Arrestin + PDE4 +B_Arrestin - alpha_12_13_R +B_Arrestin - alpha_i_R +B_Arrestin - alpha_q_R +B_Arrestin - alpha_s_R +B_Parvin + Pix_Cool +bax + Smac +bax + CytochromeC +bcl2 - p53 +bcl2 - bax +beta-catenin + CyclinD +beta-catenin + Slug +beta-catenin + MMP +BRCA1 + CHK1 +Ca + PLA2 +Ca + PKC +Ca + CaM +Ca + DGK +Ca + EGFR +Ca - IP3R1 +Ca + p120RasGAP +CaM + RGS +CaM + Myosin +CaM + CaMKK +CaM - IP3R1 +CaM + MLCK +CaM + CaMK +CaM + Ral +CaM + RasGRF_GRP +CaMK + Rap1 +CaMK + Tiam +CaMK + PLA2 +CaMKK + Akt +CaMKK + CaMK +cAMP + PP2A +cAMP + MKPs +cAMP + PKA +cAMP + Rap1 +Cas + Src +Cas + DOCK180 +Cas + Nck +Cas + Crk +Cas + AND_3_4 +Casp-3 - rb +Casp-3 - bcl2 +Casp-8 + Casp-3 +Casp-9 + Casp-3 +Cbp + Csk +Cdc20 - CyclinB +Cdc20 - CyclinA +Cdc20 + Cdh1 +Cdc42 + Mekk1 +Cdc42 + Mekk4 +Cdc42 + MLK2 +Cdc42 + RasGRF_GRP +Cdc42 + MLK1 +Cdc42 + PAK +Cdc42 - Rac +Cdc42 + MLK3 +Cdc42 + WASP +Cdc42 + PLD +Cdc42 + PTEN +Cdh1 - Cdc20 +Cdh1 + CyclinA +Cdh1 - CyclinB +Crk + Sos +Crk + PI3K +Crk + DOCK180 +Crk - WASP +Csk - Src +cycg - atm +cycg + mdm2 +CyclinA - CyclinE +CyclinA - p27 +CyclinA - Cdh1 +CyclinA - e2f1 +CyclinA - rb +CyclinB + Cdc20 +CyclinB - p27 +CyclinB - Cdh1 +CyclinB - e2f1 +CyclinD - p27 +CyclinD - rb +CyclinE - mdm2 +CyclinE - rb +CytochromeC + Casp-9 +DAG + PKC +DAG + DGK +DAG + RasGRF_GRP +Daxx + ASK1 +DGK - DAG +DNA_damage + atm +DNA_damage + p14arf +DOCK180 + Rac +e2f1 + BRCA1 +e2f1 + CytochromeC +e2f1 + atm +e2f1 + p14arf +e2f1 + CyclinA +e2f1 + CyclinE +ECM - Integrins +ECM + PTPPEST +ECM + AC +ECM + Rac +EGF + EGFR +EGF - PTP1b +EGFR + Grb2 +EGFR + Shc +EGFR + VAV2 +EGFR + Src +EGFR + Nck +EGFR - PLC_g +EGFR + p120RasGAP +EGFR + Gab1 +EGFR - PP2A +EGFR + PI3K +EGFR - PTP1b +EGFR + DGK +EGFR + Mekk2 +Erk + p90RSK +Erk - Stathmin +Erk + Myc +Erk - Sos +Erk + Slug +Erk + MMP +Erk - GRK +Erk - Raf +Erk + Snail +Erk + MKPs +Erk + PLA2 +Erk - PDE4 +Erk + MLCK +Erk + CyclinD +ExtPump - Ca +FADD + Casp-8 +Fak + p190RhoGAP +Fak + Shc +Fak + PLC_g +Fak + Grb2 +Fak - Csk +Fak + Src +Fak + Trio +Fak + PI5K +Fak + p190RhoGEF +Fak + WASP +Fak - p120RasGAP +Fak + PI3K +Fak + GEFH1 +Fak + LARG +Fak + Graf +Fak - Fak +Fak + Crk +Fak + Cas +Fas + FasR +FasR + FADD +FasR + Daxx +Frizzled - APC +Fyn + LARG +Ga_12_13 + Gbg_12_13 +Ga_12_13 + p115RhoGEF +Gab1 + PI3K +Gab1 - Mekk3 +Gab1 + SHP2 +Gai + Gbg_i +Gai + PI4K +Gai - Rap1 +Gai + Src +Gaq + PI4K +Gaq + Gbg_q +Gaq + PLC_B +Gas + Gbg_s +Gas + AC +Gas + Src +Gbg_12_13 + Csk +Gbg_12_13 - Gbg_12_13 +Gbg_12_13 + GRK +Gbg_i - Gbg_i +Gbg_i - Gai +Gbg_i + Cdc42 +Gbg_i - Rac +Gbg_i + AC +Gbg_i + Csk +Gbg_i + PLC_B +Gbg_i + GRK +Gbg_i + PI3K +Gbg_i + IP3R1 +Gbg_q - Gbg_q +Gbg_q + Csk +Gbg_q + GRK +Gbg_q - Gaq +Gbg_s - Gbg_s +Gbg_s + GRK +Gbg_s - Gas +GCK + Mekk1 +Graf - Cdc42 +Graf - Rho +Grb2 + Mekk1 +Grb2 + Gab1 +Grb2 + Sos +Grb2 + Mekk2 +Grb2 + PAK +Grb2 + WASP +GRK + Palpha_12_13_R +GRK + Palpha_i_R +GRK + Palpha_q_R +GRK + Palpha_s_R +IkB - NF-kappaB +IKK - IkB +IL1_TNF + IL1_TNFR +IL1_TNFR + TAK1 +IL1_TNFR + Trafs +IL1_TNFR + FADD +IL1_TNFR + MLK3 +IL1_TNFR + Smad2 +IL1_TNFR + Mekk3 +ILK + Akt +ILK - Integrins +ILK + Myosin +ILK + B_Parvin +ILK - MLCP +Integrins + Fak +Integrins + PTPPEST +Integrins + AC +Integrins + Rac +IP3 + IP3R1 +IP3R1 + Ca +mdm2 - mdmx +mdm2 + e2f1 +mdm2 - p21 +mdm2 - rb +mdm2 - p53 +mdmx + mdm2 +mdmx - p53 +Mek + Erk +Mekk1 + Mek +Mekk1 + MKK7 +Mekk1 + Sek1 +Mekk2 + MKK3 +Mekk2 + Mek +Mekk2 + MKK7 +Mekk2 + Sek1 +Mekk3 + Mek +Mekk3 + MKK3 +Mekk3 + MKK7 +Mekk3 + Sek1 +Mekk4 + MKK7 +Mekk4 + MKK6 +Mekk4 + MKK3 +Mekk4 + Sek1 +MKK3 + p38 +MKK6 + p38 +MKK7 + SAPK +MKPs - SAPK +MKPs - Erk +MKPs - p38 +MLCK + Myosin +MLCP - Myosin +MLK1 + MKK3 +MLK1 + MKK7 +MLK1 + Sek1 +MLK2 + MKK3 +MLK2 + MKK7 +MLK2 + Sek1 +MLK3 + MKK3 +MLK3 + MKK6 +MLK3 + MKK7 +MLK3 + Sek1 +Myc + CyclinD +Myc - p21 +Myc - p27 +Myosin + Actin +Nck + NIK +Nck + Sos +Nck + WASP +Nck + PAK +NF-kappaB + CyclinD +NF-kappaB + MMP +NF-kappaB + A20 +NF-kappaB + IkB +NIK + Mekk1 +p115RhoGEF + Rho +p115RhoGEF - Ga_12_13 +p120RasGAP - p190RhoGAP +p120RasGAP - Ras +p14arf - mdmx +p14arf - mdm2 +p14arf - e2f1 +p15 - CyclinD +p190RhoGAP - Rac +p190RhoGAP - Rho +p190RhoGAP - Cdc42 +p21 - CyclinD +p21 - CyclinB +p21 - CyclinA +p21 - CyclinE +p27 - CyclinB +p27 - CyclinD +p27 - CyclinE +p27 - CyclinA +p38 + MKPs +p38 - Tab_1_2 +p53 - CyclinB +p53 - beta-catenin +p53 - cycg +p53 - bcl2 +p53 + wip1 +p53 + PTEN +p53 - p14arf +p53 + bax +p53 + p21 +p53 - IKK +p53 + mdm2 +p53 - Slug +p53 - Snail +p53 + CytochromeC +p90RSK + PDK1 +PA + DGK +PA - PLC_g +PA + PI5K +PAK + MKK3 +PAK + MKK6 +PAK - Stathmin +PAK - Rac +PAK + Cdc42 +PAK + Mek +PAK - MLCP +PAK + Raf +PAK + Myosin +PAK - MLCK +Palpha_12_13_R - alpha_12_13_R +Palpha_12_13_R + B_Arrestin +Palpha_i_R - alpha_i_R +Palpha_i_R - alpha_q_R +Palpha_i_R + B_Arrestin +Palpha_q_R + B_Arrestin +Palpha_s_R - alpha_s_R +Palpha_s_R + B_Arrestin +PDE4 - cAMP +PDK1 + p90RSK +PDK1 + PKC_primed +PDK1 - RalGDS +PDK1 - PAK +PDK1 + PKA +PDK1 + Akt +PI3K + PIP2_34 +PI3K + PIP3_345 +PI3K + Mekk2 +PI3K + VAV2 +PI3K - PIP_4 +PI3K - PIP2_45 +PI3K + Fyn +PI3K + PTEN +PI4K + PIP2_45 +PI4K + PIP_4 +PI4K + PIP2_34 +PI5K - PIP_4 +PI5K - PIP2_34 +PI5K + PIP2_45 +PI5K + PIP3_345 +PIP2_34 + Pix_Cool +PIP2_34 + p120RasGAP +PIP2_34 + Tiam +PIP2_34 + PIP_4 +PIP2_34 + PIP3_345 +PIP2_34 + Akt +PIP2_45 - Sos +PIP2_45 + GRK +PIP2_45 + p120RasGAP +PIP2_45 + PIP3_345 +PIP2_45 - Vinc +PIP2_45 + Talin +PIP2_45 + Tiam +PIP2_45 + PLD +PIP2_45 + ARF +PIP2_45 + IP3 +PIP2_45 + DAG +PIP2_45 + WASP +PIP2_45 + PLA2 +PIP2_45 - RhoGDI +PIP3_345 + Pix_Cool +PIP3_345 - RalGDS +PIP3_345 + PIP2_45 +PIP3_345 + PLD +PIP3_345 + Akt +PIP3_345 + RGS +PIP3_345 + p120RasGAP +PIP3_345 + Gab1 +PIP3_345 + PLC_g +PIP3_345 + Sos +PIP3_345 + Tiam +PIP3_345 + ILK +PIP3_345 + ARF +PIP3_345 + DOCK180 +PIP3_345 + p115RhoGEF +PIP3_345 + PLA2 +Pix_Cool + Rac +Pix_Cool + Cdc42 +Pix_Cool + PTEN +PKA - CaMKK +PKA + Src +PKA - Raf +PKA - MLCK +PKA + Rap1 +PKA + IP3R1 +PKA - PLC_B +PKA - MLCP +PKA - PAK +PKA + PDE4 +PKA - PTPPEST +PKA + Csk +PKA - Rho +PKA + Gai +PKA - Gas +PKC + PLD +PKC - Talin +PKC - IP3R1 +PKC - PTPPEST +PKC + PI4K +PKC - RalGDS +PKC - RhoGDI +PKC + RKIP +PKC + Tiam +PKC - MLCP +PKC - GRK +PKC - PKC_primed +PKC + PTPa +PKC - EGFR +PKC - AC +PKC + DGK +PKC_primed + PKC +PKC_primed - PKC_primed +PLA2 + AA +PLC_B + IP3 +PLC_B - PIP2_45 +PLC_B + DAG +PLC_B - Gaq +PLC_g + IP3 +PLC_g + Mekk2 +PLC_g - PIP2_45 +PLC_g + DAG +PLD + PA +PP2A + Raf_DeP +PP2A - Mek +PP2A - Erk +PP2A - Integrins +PP2A - SAPK +PP2A - PKA +PP2A - IP3R1 +PP2A - p38 +PP2A - Akt +PP2A - CaMK +PP2A - PKC +PTEN - Shc +PTEN - Fak +PTEN - PIP2_34 +PTEN + PIP2_45 +PTEN + PIP_4 +PTEN - PIP3_345 +PTP1b - PAK +PTP1b - EGFR +PTP1b + Src +PTPa + Src +PTPPEST - Crk +PTPPEST - Cas +PTPPEST - WASP +Rac + MLK2 +Rac + Mekk4 +Rac + Mekk3 +Rac + MLK1 +Rac + PLD +Rac - PI5K +Rac + PAK +Rac + Mekk1 +Rac - Cdc42 +Rac + MLK3 +Raf + Mek +Raf - MLCP +Raf - Raf_Loc +Raf - Raf_Rest +Raf_DeP - Raf_DeP +Raf_DeP + Raf_Loc +Raf_DeP - Raf_Rest +Raf_Loc + Raf +Raf_Loc - Raf_DeP +Raf_Loc - Raf_Loc +Raf_Rest + Raf_DeP +Raf_Rest - Raf_Rest +Ral - PLD +Ral + RalBP1 +RalBP1 - Rac +RalBP1 - Cdc42 +RalGDS + Ral +Rap1 + Tiam +Ras + PI3K +Ras - RalGDS +Ras + Tiam +Ras + Mekk1 +Ras - Sos +Ras + Raf_Loc +Ras + Raf +RasGRF_GRP + Ras +RasGRF_GRP + Rac +rb - mdm2 +rb - CyclinA +rb - CyclinE +rb - e2f1 +RGS - Gaq +RGS - Gai +RGS - Gas +Rho + PI4K +Rho + PLD +Rho - Rac +Rho + Mekk1 +Rho + PTEN +Rho - PI5K +Rho + RhoK +RhoGDI - Rac +RhoGDI - Rho +RhoGDI - Cdc42 +RhoK + ARHGAP22 +RhoK + PI5K +RhoK - MLCP +RhoK + Myosin +RKIP + Raf +RKIP - GRK +SAPK - bcl2 +SAPK + MKPs +SAPK + MLK2 +Sek1 + p38 +Sek1 + SAPK +Shc + Grb2 +Shc + Mekk1 +SHP2 + Ras +SHP2 - p120RasGAP +SHP2 - Csk +SHP2 - Gab1 +SHP2 - Cbp +Slug - E-cad +Smac - XIAP +Smad2 + smad2_4 +smad2_4 + p15 +smad2_4 + MMP +smad2_4 - E-cad +smad2_4 + p27 +smad2_4 + p21 +Snail - E-cad +Sos + Ras +Src - Cdc42 +Src + Shc +Src + PI3K +Src + DGK +Src - p120RasGAP +Src + Graf +Src + Raf +Src - Csk +Src + Mekk2 +Src + Akt +Src + Fak +Src - PP2A +Src + PI5K +Src + Tiam +Src - PTEN +Src + Cbp +Src + Rap1 +Src + PLC_g +Src - Talin +Src + WASP +Src + GRK +Src - RhoGDI +Src + p190RhoGAP +Src + PDK1 +Src + PAK +Src - Integrins +Src + Vinc +Src + Crk +Src + Grb2 +Src + Cas +Stathmin + GEFH1 +Stress + TAO_1_2 +Stress - PTP1b +Stress + Trx +Stress + PTEN +Tab_1_2 + TAK1 +TAK1 + IKK +TAK1 + Sek1 +TAK1 + MKK3 +TAK1 + NIK +TAK1 + MKK6 +Talin - Integrins +Talin - PI5K +Talin + Fak +Talin + Vinc +TAO_1_2 + MKK3 +TAO_1_2 + MKK6 +Tgf-b + TGFR +TGFR + Smad2 +Tiam + Rac +Tpl2 + Mek +Tpl2 + Sek1 +Tpl2 + MKK6 +Tpl2 + MKK3 +Tpl2 - NIK +Trafs + GCK +Trafs + Mekk1 +Trafs + Tpl2 +Trafs + Trx +Trafs + Tab_1_2 +Trafs + TAK1 +Trafs + Mekk3 +Trio + Fak +Trx + ASK1 +Trx - PKC +WASP + Arp_2_3 +wip1 - atm +wip1 + mdm2 +wip1 - p14arf +wip1 + mdmx +WNT + Frizzled +XIAP - Casp-3 +XIAP - Casp-9 +Casp-3 + Apoptosis +MMP + Metastasis +Rho + Metastasis +E-cad - Metastasis +CyclinA + Proliferation +CyclinB + Proliferation +CyclinD + Proliferation +CyclinE + Proliferation diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/cho_2016/network_all_pos.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/cho_2016/network_all_pos.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,696 @@ +A20 - TAK1 +A20 - IKK +AA - RhoGDI +AA - PLC_g +AA + PKC +AC + cAMP +Actin + PLD +Actin + Vinc +Akt + bcl2 +Akt - Casp-9 +Akt + ASK1 +Akt - Rac +Akt - Raf +Akt - PAK +Akt + mdmx +Akt + IKK +Akt + mdm2 +Akt - p21 +Akt - p27 +alpha_12_13_lig + alpha_12_13_R +alpha_12_13_R + alpha_12_13_R +alpha_12_13_R + Ga_12_13 +alpha_12_13_R + Palpha_12_13_R +alpha_12_13_R + RalGDS +alpha_12_13_R + Gbg_12_13 +alpha_12_13_R + EGFR +alpha_i_lig + alpha_i_R +alpha_i_R + alpha_i_R +alpha_i_R + Palpha_i_R +alpha_i_R + RalGDS +alpha_i_R + Gbg_i +alpha_i_R + EGFR +alpha_i_R + Gai +alpha_q_lig + alpha_q_R +alpha_q_R + alpha_q_R +alpha_q_R + Palpha_q_R +alpha_q_R + Gaq +alpha_q_R + RalGDS +alpha_q_R + Gbg_q +alpha_q_R + EGFR +alpha_s_lig + alpha_s_R +alpha_s_lig + Gai +alpha_s_R + alpha_s_R +alpha_s_R + Palpha_s_R +alpha_s_R + Src +alpha_s_R + RalGDS +alpha_s_R - Gai +alpha_s_R + Gbg_s +alpha_s_R + Gas +AND_3_4 + Ral +APC - beta-catenin +ARF + PI5K +ARF + PLD +ARF + PI4K +Arp_2_3 + Actin +ASK1 + MKK3 +ASK1 + MKK6 +ASK1 + Sek1 +ASK1 + MKK7 +atm + e2f1 +atm - mdm2 +atm + rb +atm - mdmx +atm + p53 +atm + BRCA1 +B_Arrestin + Src +B_Arrestin + RalGDS +B_Arrestin + GRK +B_Arrestin + PDE4 +B_Arrestin - alpha_12_13_R +B_Arrestin - alpha_i_R +B_Arrestin - alpha_q_R +B_Arrestin - alpha_s_R +B_Parvin + Pix_Cool +bax + Smac +bax + CytochromeC +bcl2 - p53 +bcl2 - bax +beta-catenin + CyclinD +beta-catenin + Slug +beta-catenin + MMP +BRCA1 + CHK1 +Ca + PLA2 +Ca + PKC +Ca + CaM +Ca + DGK +Ca + EGFR +Ca + IP3R1 +Ca + p120RasGAP +CaM + RGS +CaM + Myosin +CaM + CaMKK +CaM - IP3R1 +CaM + MLCK +CaM + CaMK +CaM + Ral +CaM + RasGRF_GRP +CaMK + Rap1 +CaMK + Tiam +CaMK + PLA2 +CaMKK + Akt +CaMKK + CaMK +cAMP + PP2A +cAMP + MKPs +cAMP + PKA +cAMP + Rap1 +Cas + Src +Cas + DOCK180 +Cas + Nck +Cas + Crk +Cas + AND_3_4 +Casp-3 - rb +Casp-3 - bcl2 +Casp-8 + Casp-3 +Casp-9 + Casp-3 +Cbp + Csk +Cdc20 - CyclinB +Cdc20 - CyclinA +Cdc20 + Cdh1 +Cdc42 + Mekk1 +Cdc42 + Mekk4 +Cdc42 + MLK2 +Cdc42 + RasGRF_GRP +Cdc42 + MLK1 +Cdc42 + PAK +Cdc42 + Rac +Cdc42 + MLK3 +Cdc42 + WASP +Cdc42 + PLD +Cdc42 + PTEN +Cdh1 - Cdc20 +Cdh1 + CyclinA +Cdh1 - CyclinB +Crk + Sos +Crk + PI3K +Crk + DOCK180 +Crk + WASP +Csk - Src +cycg - atm +cycg + mdm2 +CyclinA - CyclinE +CyclinA - p27 +CyclinA - Cdh1 +CyclinA - e2f1 +CyclinA - rb +CyclinB + Cdc20 +CyclinB - p27 +CyclinB - Cdh1 +CyclinB - e2f1 +CyclinD - p27 +CyclinD - rb +CyclinE - mdm2 +CyclinE - rb +CytochromeC + Casp-9 +DAG + PKC +DAG + DGK +DAG + RasGRF_GRP +Daxx + ASK1 +DGK - DAG +DNA_damage + atm +DNA_damage + p14arf +DOCK180 + Rac +e2f1 + BRCA1 +e2f1 + CytochromeC +e2f1 + atm +e2f1 + p14arf +e2f1 + CyclinA +e2f1 + CyclinE +ECM + Integrins +ECM + PTPPEST +ECM + AC +ECM + Rac +EGF + EGFR +EGF - PTP1b +EGFR + Grb2 +EGFR + Shc +EGFR + VAV2 +EGFR + Src +EGFR + Nck +EGFR + PLC_g +EGFR + p120RasGAP +EGFR + Gab1 +EGFR - PP2A +EGFR + PI3K +EGFR - PTP1b +EGFR + DGK +EGFR + Mekk2 +Erk + p90RSK +Erk - Stathmin +Erk + Myc +Erk - Sos +Erk + Slug +Erk + MMP +Erk - GRK +Erk - Raf +Erk + Snail +Erk + MKPs +Erk + PLA2 +Erk - PDE4 +Erk + MLCK +Erk + CyclinD +ExtPump - Ca +FADD + Casp-8 +Fak + p190RhoGAP +Fak + Shc +Fak + PLC_g +Fak + Grb2 +Fak + Csk +Fak + Src +Fak + Trio +Fak + PI5K +Fak + p190RhoGEF +Fak + WASP +Fak - p120RasGAP +Fak + PI3K +Fak + GEFH1 +Fak + LARG +Fak + Graf +Fak + Fak +Fak + Crk +Fak + Cas +Fas + FasR +FasR + FADD +FasR + Daxx +Frizzled - APC +Fyn + LARG +Ga_12_13 + Gbg_12_13 +Ga_12_13 + p115RhoGEF +Gab1 + PI3K +Gab1 - Mekk3 +Gab1 + SHP2 +Gai + Gbg_i +Gai + PI4K +Gai - Rap1 +Gai + Src +Gaq + PI4K +Gaq + Gbg_q +Gaq + PLC_B +Gas + Gbg_s +Gas + AC +Gas + Src +Gbg_12_13 + Csk +Gbg_12_13 - Gbg_12_13 +Gbg_12_13 + GRK +Gbg_i - Gbg_i +Gbg_i + Gai +Gbg_i + Cdc42 +Gbg_i + Rac +Gbg_i + AC +Gbg_i + Csk +Gbg_i + PLC_B +Gbg_i + GRK +Gbg_i + PI3K +Gbg_i + IP3R1 +Gbg_q - Gbg_q +Gbg_q + Csk +Gbg_q + GRK +Gbg_q + Gaq +Gbg_s - Gbg_s +Gbg_s + GRK +Gbg_s + Gas +GCK + Mekk1 +Graf + Cdc42 +Graf - Rho +Grb2 + Mekk1 +Grb2 + Gab1 +Grb2 + Sos +Grb2 + Mekk2 +Grb2 + PAK +Grb2 + WASP +GRK + Palpha_12_13_R +GRK + Palpha_i_R +GRK + Palpha_q_R +GRK + Palpha_s_R +IkB - NF-kappaB +IKK - IkB +IL1_TNF + IL1_TNFR +IL1_TNFR + TAK1 +IL1_TNFR + Trafs +IL1_TNFR + FADD +IL1_TNFR + MLK3 +IL1_TNFR + Smad2 +IL1_TNFR + Mekk3 +ILK + Akt +ILK - Integrins +ILK + Myosin +ILK + B_Parvin +ILK - MLCP +Integrins + Fak +Integrins + PTPPEST +Integrins + AC +Integrins + Rac +IP3 + IP3R1 +IP3R1 + Ca +mdm2 - mdmx +mdm2 + e2f1 +mdm2 - p21 +mdm2 - rb +mdm2 - p53 +mdmx + mdm2 +mdmx - p53 +Mek + Erk +Mekk1 + Mek +Mekk1 + MKK7 +Mekk1 + Sek1 +Mekk2 + MKK3 +Mekk2 + Mek +Mekk2 + MKK7 +Mekk2 + Sek1 +Mekk3 + Mek +Mekk3 + MKK3 +Mekk3 + MKK7 +Mekk3 + Sek1 +Mekk4 + MKK7 +Mekk4 + MKK6 +Mekk4 + MKK3 +Mekk4 + Sek1 +MKK3 + p38 +MKK6 + p38 +MKK7 + SAPK +MKPs - SAPK +MKPs - Erk +MKPs - p38 +MLCK + Myosin +MLCP - Myosin +MLK1 + MKK3 +MLK1 + MKK7 +MLK1 + Sek1 +MLK2 + MKK3 +MLK2 + MKK7 +MLK2 + Sek1 +MLK3 + MKK3 +MLK3 + MKK6 +MLK3 + MKK7 +MLK3 + Sek1 +Myc + CyclinD +Myc - p21 +Myc - p27 +Myosin + Actin +Nck + NIK +Nck + Sos +Nck + WASP +Nck + PAK +NF-kappaB + CyclinD +NF-kappaB + MMP +NF-kappaB + A20 +NF-kappaB + IkB +NIK + Mekk1 +p115RhoGEF + Rho +p115RhoGEF - Ga_12_13 +p120RasGAP - p190RhoGAP +p120RasGAP + Ras +p14arf - mdmx +p14arf - mdm2 +p14arf - e2f1 +p15 - CyclinD +p190RhoGAP - Rac +p190RhoGAP - Rho +p190RhoGAP + Cdc42 +p21 - CyclinD +p21 - CyclinB +p21 - CyclinA +p21 - CyclinE +p27 - CyclinB +p27 - CyclinD +p27 - CyclinE +p27 - CyclinA +p38 + MKPs +p38 - Tab_1_2 +p53 - CyclinB +p53 - beta-catenin +p53 - cycg +p53 - bcl2 +p53 + wip1 +p53 + PTEN +p53 - p14arf +p53 + bax +p53 + p21 +p53 - IKK +p53 + mdm2 +p53 - Slug +p53 - Snail +p53 + CytochromeC +p90RSK + PDK1 +PA + DGK +PA - PLC_g +PA + PI5K +PAK + MKK3 +PAK + MKK6 +PAK - Stathmin +PAK + Rac +PAK + Cdc42 +PAK + Mek +PAK - MLCP +PAK + Raf +PAK + Myosin +PAK - MLCK +Palpha_12_13_R + alpha_12_13_R +Palpha_12_13_R + B_Arrestin +Palpha_i_R + alpha_i_R +Palpha_i_R + alpha_q_R +Palpha_i_R + B_Arrestin +Palpha_q_R + B_Arrestin +Palpha_s_R + alpha_s_R +Palpha_s_R + B_Arrestin +PDE4 - cAMP +PDK1 + p90RSK +PDK1 + PKC_primed +PDK1 - RalGDS +PDK1 + PAK +PDK1 + PKA +PDK1 + Akt +PI3K + PIP2_34 +PI3K + PIP3_345 +PI3K + Mekk2 +PI3K + VAV2 +PI3K - PIP_4 +PI3K + PIP2_45 +PI3K + Fyn +PI3K + PTEN +PI4K + PIP2_45 +PI4K + PIP_4 +PI4K + PIP2_34 +PI5K - PIP_4 +PI5K - PIP2_34 +PI5K + PIP2_45 +PI5K + PIP3_345 +PIP2_34 + Pix_Cool +PIP2_34 + p120RasGAP +PIP2_34 + Tiam +PIP2_34 + PIP_4 +PIP2_34 + PIP3_345 +PIP2_34 + Akt +PIP2_45 + Sos +PIP2_45 + GRK +PIP2_45 + p120RasGAP +PIP2_45 + PIP3_345 +PIP2_45 - Vinc +PIP2_45 + Talin +PIP2_45 + Tiam +PIP2_45 + PLD +PIP2_45 + ARF +PIP2_45 + IP3 +PIP2_45 + DAG +PIP2_45 + WASP +PIP2_45 + PLA2 +PIP2_45 - RhoGDI +PIP3_345 + Pix_Cool +PIP3_345 - RalGDS +PIP3_345 + PIP2_45 +PIP3_345 + PLD +PIP3_345 + Akt +PIP3_345 + RGS +PIP3_345 + p120RasGAP +PIP3_345 + Gab1 +PIP3_345 + PLC_g +PIP3_345 + Sos +PIP3_345 + Tiam +PIP3_345 + ILK +PIP3_345 + ARF +PIP3_345 + DOCK180 +PIP3_345 + p115RhoGEF +PIP3_345 + PLA2 +Pix_Cool + Rac +Pix_Cool + Cdc42 +Pix_Cool + PTEN +PKA + CaMKK +PKA + Src +PKA - Raf +PKA - MLCK +PKA + Rap1 +PKA + IP3R1 +PKA - PLC_B +PKA + MLCP +PKA - PAK +PKA + PDE4 +PKA - PTPPEST +PKA + Csk +PKA - Rho +PKA + Gai +PKA - Gas +PKC + PLD +PKC + Talin +PKC + IP3R1 +PKC - PTPPEST +PKC + PI4K +PKC - RalGDS +PKC - RhoGDI +PKC + RKIP +PKC + Tiam +PKC - MLCP +PKC + GRK +PKC - PKC_primed +PKC + PTPa +PKC + EGFR +PKC + AC +PKC + DGK +PKC_primed + PKC +PKC_primed + PKC_primed +PLA2 + AA +PLC_B + IP3 +PLC_B + PIP2_45 +PLC_B + DAG +PLC_B - Gaq +PLC_g + IP3 +PLC_g + Mekk2 +PLC_g + PIP2_45 +PLC_g + DAG +PLD + PA +PP2A + Raf_DeP +PP2A - Mek +PP2A - Erk +PP2A + Integrins +PP2A - SAPK +PP2A - PKA +PP2A + IP3R1 +PP2A - p38 +PP2A - Akt +PP2A + CaMK +PP2A - PKC +PTEN - Shc +PTEN - Fak +PTEN - PIP2_34 +PTEN + PIP2_45 +PTEN + PIP_4 +PTEN - PIP3_345 +PTP1b - PAK +PTP1b + EGFR +PTP1b + Src +PTPa + Src +PTPPEST - Crk +PTPPEST - Cas +PTPPEST - WASP +Rac + MLK2 +Rac + Mekk4 +Rac + Mekk3 +Rac + MLK1 +Rac + PLD +Rac + PI5K +Rac + PAK +Rac + Mekk1 +Rac - Cdc42 +Rac + MLK3 +Raf + Mek +Raf - MLCP +Raf - Raf_Loc +Raf - Raf_Rest +Raf_DeP + Raf_DeP +Raf_DeP + Raf_Loc +Raf_DeP - Raf_Rest +Raf_Loc + Raf +Raf_Loc - Raf_DeP +Raf_Loc + Raf_Loc +Raf_Rest + Raf_DeP +Raf_Rest + Raf_Rest +Ral + PLD +Ral + RalBP1 +RalBP1 - Rac +RalBP1 + Cdc42 +RalGDS + Ral +Rap1 + Tiam +Ras + PI3K +Ras - RalGDS +Ras + Tiam +Ras + Mekk1 +Ras + Sos +Ras + Raf_Loc +Ras + Raf +RasGRF_GRP + Ras +RasGRF_GRP + Rac +rb - mdm2 +rb - CyclinA +rb - CyclinE +rb - e2f1 +RGS - Gaq +RGS - Gai +RGS - Gas +Rho + PI4K +Rho + PLD +Rho - Rac +Rho + Mekk1 +Rho + PTEN +Rho + PI5K +Rho + RhoK +RhoGDI - Rac +RhoGDI - Rho +RhoGDI - Cdc42 +RhoK + ARHGAP22 +RhoK + PI5K +RhoK + MLCP +RhoK + Myosin +RKIP + Raf +RKIP - GRK +SAPK - bcl2 +SAPK + MKPs +SAPK + MLK2 +Sek1 + p38 +Sek1 + SAPK +Shc + Grb2 +Shc + Mekk1 +SHP2 + Ras +SHP2 - p120RasGAP +SHP2 - Csk +SHP2 - Gab1 +SHP2 - Cbp +Slug - E-cad +Smac - XIAP +Smad2 + smad2_4 +smad2_4 + p15 +smad2_4 + MMP +smad2_4 - E-cad +smad2_4 + p27 +smad2_4 + p21 +Snail - E-cad +Sos + Ras +Src - Cdc42 +Src + Shc +Src + PI3K +Src + DGK +Src - p120RasGAP +Src + Graf +Src + Raf +Src + Csk +Src + Mekk2 +Src + Akt +Src + Fak +Src - PP2A +Src + PI5K +Src + Tiam +Src - PTEN +Src + Cbp +Src + Rap1 +Src + PLC_g +Src - Talin +Src + WASP +Src + GRK +Src - RhoGDI +Src + p190RhoGAP +Src + PDK1 +Src + PAK +Src + Integrins +Src + Vinc +Src + Crk +Src + Grb2 +Src + Cas +Stathmin + GEFH1 +Stress + TAO_1_2 +Stress - PTP1b +Stress + Trx +Stress + PTEN +Tab_1_2 + TAK1 +TAK1 + IKK +TAK1 + Sek1 +TAK1 + MKK3 +TAK1 + NIK +TAK1 + MKK6 +Talin + Integrins +Talin + PI5K +Talin + Fak +Talin + Vinc +TAO_1_2 + MKK3 +TAO_1_2 + MKK6 +Tgf-b + TGFR +TGFR + Smad2 +Tiam + Rac +Tpl2 + Mek +Tpl2 + Sek1 +Tpl2 + MKK6 +Tpl2 + MKK3 +Tpl2 + NIK +Trafs + GCK +Trafs + Mekk1 +Trafs + Tpl2 +Trafs + Trx +Trafs + Tab_1_2 +Trafs + TAK1 +Trafs + Mekk3 +Trio + Fak +Trx + ASK1 +Trx - PKC +WASP + Arp_2_3 +wip1 - atm +wip1 + mdm2 +wip1 - p14arf +wip1 + mdmx +WNT + Frizzled +XIAP - Casp-3 +XIAP - Casp-9 +Casp-3 + Apoptosis +MMP + Metastasis +Rho + Metastasis +E-cad - Metastasis +CyclinA + Proliferation +CyclinB + Proliferation +CyclinD + Proliferation +CyclinE + Proliferation diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/cho_2016/network_org.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/cho_2016/network_org.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,696 @@ +A20 - TAK1 +A20 - IKK +AA - RhoGDI +AA - PLC_g +AA + PKC +AC + cAMP +Actin + PLD +Actin + Vinc +Akt + bcl2 +Akt - Casp-9 +Akt +/- ASK1 +Akt - Rac +Akt - Raf +Akt - PAK +Akt + mdmx +Akt + IKK +Akt + mdm2 +Akt - p21 +Akt - p27 +alpha_12_13_lig + alpha_12_13_R +alpha_12_13_R + alpha_12_13_R +alpha_12_13_R + Ga_12_13 +alpha_12_13_R + Palpha_12_13_R +alpha_12_13_R + RalGDS +alpha_12_13_R + Gbg_12_13 +alpha_12_13_R + EGFR +alpha_i_lig + alpha_i_R +alpha_i_R + alpha_i_R +alpha_i_R + Palpha_i_R +alpha_i_R + RalGDS +alpha_i_R + Gbg_i +alpha_i_R + EGFR +alpha_i_R + Gai +alpha_q_lig + alpha_q_R +alpha_q_R + alpha_q_R +alpha_q_R + Palpha_q_R +alpha_q_R + Gaq +alpha_q_R + RalGDS +alpha_q_R + Gbg_q +alpha_q_R + EGFR +alpha_s_lig + alpha_s_R +alpha_s_lig + Gai +alpha_s_R + alpha_s_R +alpha_s_R + Palpha_s_R +alpha_s_R + Src +alpha_s_R + RalGDS +alpha_s_R - Gai +alpha_s_R + Gbg_s +alpha_s_R + Gas +AND_3_4 + Ral +APC - beta-catenin +ARF + PI5K +ARF + PLD +ARF + PI4K +Arp_2_3 + Actin +ASK1 + MKK3 +ASK1 + MKK6 +ASK1 + Sek1 +ASK1 + MKK7 +atm + e2f1 +atm - mdm2 +atm + rb +atm - mdmx +atm + p53 +atm + BRCA1 +B_Arrestin + Src +B_Arrestin + RalGDS +B_Arrestin + GRK +B_Arrestin + PDE4 +B_Arrestin - alpha_12_13_R +B_Arrestin - alpha_i_R +B_Arrestin - alpha_q_R +B_Arrestin - alpha_s_R +B_Parvin + Pix_Cool +bax + Smac +bax + CytochromeC +bcl2 - p53 +bcl2 - bax +beta-catenin + CyclinD +beta-catenin + Slug +beta-catenin + MMP +BRCA1 + CHK1 +Ca + PLA2 +Ca + PKC +Ca + CaM +Ca + DGK +Ca + EGFR +Ca +/- IP3R1 +Ca + p120RasGAP +CaM + RGS +CaM + Myosin +CaM + CaMKK +CaM - IP3R1 +CaM + MLCK +CaM + CaMK +CaM + Ral +CaM + RasGRF_GRP +CaMK + Rap1 +CaMK + Tiam +CaMK + PLA2 +CaMKK + Akt +CaMKK + CaMK +cAMP + PP2A +cAMP + MKPs +cAMP + PKA +cAMP + Rap1 +Cas + Src +Cas + DOCK180 +Cas + Nck +Cas + Crk +Cas + AND_3_4 +Casp-3 - rb +Casp-3 - bcl2 +Casp-8 + Casp-3 +Casp-9 + Casp-3 +Cbp + Csk +Cdc20 - CyclinB +Cdc20 - CyclinA +Cdc20 + Cdh1 +Cdc42 + Mekk1 +Cdc42 + Mekk4 +Cdc42 + MLK2 +Cdc42 + RasGRF_GRP +Cdc42 + MLK1 +Cdc42 + PAK +Cdc42 +/- Rac +Cdc42 + MLK3 +Cdc42 + WASP +Cdc42 + PLD +Cdc42 + PTEN +Cdh1 - Cdc20 +Cdh1 + CyclinA +Cdh1 - CyclinB +Crk + Sos +Crk + PI3K +Crk + DOCK180 +Crk +/- WASP +Csk - Src +cycg - atm +cycg + mdm2 +CyclinA - CyclinE +CyclinA - p27 +CyclinA - Cdh1 +CyclinA - e2f1 +CyclinA - rb +CyclinB + Cdc20 +CyclinB - p27 +CyclinB - Cdh1 +CyclinB - e2f1 +CyclinD - p27 +CyclinD - rb +CyclinE - mdm2 +CyclinE - rb +CytochromeC + Casp-9 +DAG + PKC +DAG + DGK +DAG + RasGRF_GRP +Daxx + ASK1 +DGK - DAG +DNA_damage + atm +DNA_damage + p14arf +DOCK180 + Rac +e2f1 + BRCA1 +e2f1 + CytochromeC +e2f1 + atm +e2f1 + p14arf +e2f1 + CyclinA +e2f1 + CyclinE +ECM +/- Integrins +ECM + PTPPEST +ECM + AC +ECM + Rac +EGF + EGFR +EGF - PTP1b +EGFR + Grb2 +EGFR + Shc +EGFR + VAV2 +EGFR + Src +EGFR + Nck +EGFR +/- PLC_g +EGFR + p120RasGAP +EGFR + Gab1 +EGFR - PP2A +EGFR + PI3K +EGFR - PTP1b +EGFR + DGK +EGFR + Mekk2 +Erk + p90RSK +Erk - Stathmin +Erk + Myc +Erk - Sos +Erk + Slug +Erk + MMP +Erk - GRK +Erk - Raf +Erk + Snail +Erk + MKPs +Erk + PLA2 +Erk - PDE4 +Erk + MLCK +Erk + CyclinD +ExtPump - Ca +FADD + Casp-8 +Fak + p190RhoGAP +Fak + Shc +Fak + PLC_g +Fak + Grb2 +Fak +/- Csk +Fak + Src +Fak + Trio +Fak + PI5K +Fak + p190RhoGEF +Fak + WASP +Fak - p120RasGAP +Fak + PI3K +Fak + GEFH1 +Fak + LARG +Fak + Graf +Fak +/- Fak +Fak + Crk +Fak + Cas +Fas + FasR +FasR + FADD +FasR + Daxx +Frizzled - APC +Fyn + LARG +Ga_12_13 + Gbg_12_13 +Ga_12_13 + p115RhoGEF +Gab1 + PI3K +Gab1 - Mekk3 +Gab1 + SHP2 +Gai + Gbg_i +Gai + PI4K +Gai - Rap1 +Gai + Src +Gaq + PI4K +Gaq + Gbg_q +Gaq + PLC_B +Gas + Gbg_s +Gas + AC +Gas + Src +Gbg_12_13 + Csk +Gbg_12_13 - Gbg_12_13 +Gbg_12_13 + GRK +Gbg_i - Gbg_i +Gbg_i +/- Gai +Gbg_i + Cdc42 +Gbg_i +/- Rac +Gbg_i + AC +Gbg_i + Csk +Gbg_i + PLC_B +Gbg_i + GRK +Gbg_i + PI3K +Gbg_i + IP3R1 +Gbg_q - Gbg_q +Gbg_q + Csk +Gbg_q + GRK +Gbg_q +/- Gaq +Gbg_s - Gbg_s +Gbg_s + GRK +Gbg_s +/- Gas +GCK + Mekk1 +Graf +/- Cdc42 +Graf - Rho +Grb2 + Mekk1 +Grb2 + Gab1 +Grb2 + Sos +Grb2 + Mekk2 +Grb2 + PAK +Grb2 + WASP +GRK + Palpha_12_13_R +GRK + Palpha_i_R +GRK + Palpha_q_R +GRK + Palpha_s_R +IkB - NF-kappaB +IKK - IkB +IL1_TNF + IL1_TNFR +IL1_TNFR + TAK1 +IL1_TNFR + Trafs +IL1_TNFR + FADD +IL1_TNFR + MLK3 +IL1_TNFR + Smad2 +IL1_TNFR + Mekk3 +ILK + Akt +ILK - Integrins +ILK + Myosin +ILK + B_Parvin +ILK - MLCP +Integrins + Fak +Integrins + PTPPEST +Integrins + AC +Integrins + Rac +IP3 + IP3R1 +IP3R1 + Ca +mdm2 - mdmx +mdm2 + e2f1 +mdm2 - p21 +mdm2 - rb +mdm2 - p53 +mdmx + mdm2 +mdmx - p53 +Mek + Erk +Mekk1 + Mek +Mekk1 + MKK7 +Mekk1 + Sek1 +Mekk2 + MKK3 +Mekk2 + Mek +Mekk2 + MKK7 +Mekk2 + Sek1 +Mekk3 + Mek +Mekk3 + MKK3 +Mekk3 + MKK7 +Mekk3 + Sek1 +Mekk4 + MKK7 +Mekk4 + MKK6 +Mekk4 + MKK3 +Mekk4 + Sek1 +MKK3 + p38 +MKK6 + p38 +MKK7 + SAPK +MKPs - SAPK +MKPs - Erk +MKPs - p38 +MLCK + Myosin +MLCP - Myosin +MLK1 + MKK3 +MLK1 + MKK7 +MLK1 + Sek1 +MLK2 + MKK3 +MLK2 + MKK7 +MLK2 + Sek1 +MLK3 + MKK3 +MLK3 + MKK6 +MLK3 + MKK7 +MLK3 + Sek1 +Myc + CyclinD +Myc - p21 +Myc - p27 +Myosin + Actin +Nck + NIK +Nck + Sos +Nck + WASP +Nck + PAK +NF-kappaB + CyclinD +NF-kappaB + MMP +NF-kappaB + A20 +NF-kappaB + IkB +NIK + Mekk1 +p115RhoGEF + Rho +p115RhoGEF - Ga_12_13 +p120RasGAP - p190RhoGAP +p120RasGAP +/- Ras +p14arf - mdmx +p14arf - mdm2 +p14arf - e2f1 +p15 - CyclinD +p190RhoGAP - Rac +p190RhoGAP - Rho +p190RhoGAP +/- Cdc42 +p21 - CyclinD +p21 - CyclinB +p21 - CyclinA +p21 - CyclinE +p27 - CyclinB +p27 - CyclinD +p27 - CyclinE +p27 - CyclinA +p38 + MKPs +p38 - Tab_1_2 +p53 - CyclinB +p53 - beta-catenin +p53 - cycg +p53 - bcl2 +p53 + wip1 +p53 + PTEN +p53 - p14arf +p53 + bax +p53 + p21 +p53 - IKK +p53 + mdm2 +p53 - Slug +p53 - Snail +p53 + CytochromeC +p90RSK + PDK1 +PA + DGK +PA - PLC_g +PA + PI5K +PAK + MKK3 +PAK + MKK6 +PAK - Stathmin +PAK +/- Rac +PAK + Cdc42 +PAK + Mek +PAK - MLCP +PAK + Raf +PAK + Myosin +PAK - MLCK +Palpha_12_13_R +/- alpha_12_13_R +Palpha_12_13_R + B_Arrestin +Palpha_i_R +/- alpha_i_R +Palpha_i_R +/- alpha_q_R +Palpha_i_R + B_Arrestin +Palpha_q_R + B_Arrestin +Palpha_s_R +/- alpha_s_R +Palpha_s_R + B_Arrestin +PDE4 - cAMP +PDK1 + p90RSK +PDK1 + PKC_primed +PDK1 - RalGDS +PDK1 +/- PAK +PDK1 + PKA +PDK1 + Akt +PI3K + PIP2_34 +PI3K + PIP3_345 +PI3K + Mekk2 +PI3K + VAV2 +PI3K - PIP_4 +PI3K +/- PIP2_45 +PI3K + Fyn +PI3K + PTEN +PI4K + PIP2_45 +PI4K + PIP_4 +PI4K + PIP2_34 +PI5K - PIP_4 +PI5K - PIP2_34 +PI5K + PIP2_45 +PI5K + PIP3_345 +PIP2_34 + Pix_Cool +PIP2_34 + p120RasGAP +PIP2_34 + Tiam +PIP2_34 + PIP_4 +PIP2_34 + PIP3_345 +PIP2_34 + Akt +PIP2_45 +/- Sos +PIP2_45 + GRK +PIP2_45 + p120RasGAP +PIP2_45 + PIP3_345 +PIP2_45 - Vinc +PIP2_45 + Talin +PIP2_45 + Tiam +PIP2_45 + PLD +PIP2_45 + ARF +PIP2_45 + IP3 +PIP2_45 + DAG +PIP2_45 + WASP +PIP2_45 + PLA2 +PIP2_45 - RhoGDI +PIP3_345 + Pix_Cool +PIP3_345 - RalGDS +PIP3_345 + PIP2_45 +PIP3_345 + PLD +PIP3_345 + Akt +PIP3_345 + RGS +PIP3_345 + p120RasGAP +PIP3_345 + Gab1 +PIP3_345 + PLC_g +PIP3_345 + Sos +PIP3_345 + Tiam +PIP3_345 + ILK +PIP3_345 + ARF +PIP3_345 + DOCK180 +PIP3_345 + p115RhoGEF +PIP3_345 + PLA2 +Pix_Cool + Rac +Pix_Cool + Cdc42 +Pix_Cool + PTEN +PKA +/- CaMKK +PKA + Src +PKA - Raf +PKA - MLCK +PKA + Rap1 +PKA + IP3R1 +PKA - PLC_B +PKA +/- MLCP +PKA - PAK +PKA + PDE4 +PKA - PTPPEST +PKA + Csk +PKA - Rho +PKA + Gai +PKA - Gas +PKC + PLD +PKC +/- Talin +PKC +/- IP3R1 +PKC - PTPPEST +PKC + PI4K +PKC - RalGDS +PKC - RhoGDI +PKC + RKIP +PKC + Tiam +PKC - MLCP +PKC +/- GRK +PKC - PKC_primed +PKC + PTPa +PKC +/- EGFR +PKC +/- AC +PKC + DGK +PKC_primed + PKC +PKC_primed +/- PKC_primed +PLA2 + AA +PLC_B + IP3 +PLC_B +/- PIP2_45 +PLC_B + DAG +PLC_B - Gaq +PLC_g + IP3 +PLC_g + Mekk2 +PLC_g +/- PIP2_45 +PLC_g + DAG +PLD + PA +PP2A + Raf_DeP +PP2A - Mek +PP2A - Erk +PP2A +/- Integrins +PP2A - SAPK +PP2A - PKA +PP2A +/- IP3R1 +PP2A - p38 +PP2A - Akt +PP2A +/- CaMK +PP2A - PKC +PTEN - Shc +PTEN - Fak +PTEN - PIP2_34 +PTEN + PIP2_45 +PTEN + PIP_4 +PTEN - PIP3_345 +PTP1b - PAK +PTP1b +/- EGFR +PTP1b + Src +PTPa + Src +PTPPEST - Crk +PTPPEST - Cas +PTPPEST - WASP +Rac + MLK2 +Rac + Mekk4 +Rac + Mekk3 +Rac + MLK1 +Rac + PLD +Rac +/- PI5K +Rac + PAK +Rac + Mekk1 +Rac - Cdc42 +Rac + MLK3 +Raf + Mek +Raf - MLCP +Raf - Raf_Loc +Raf - Raf_Rest +Raf_DeP +/- Raf_DeP +Raf_DeP + Raf_Loc +Raf_DeP - Raf_Rest +Raf_Loc + Raf +Raf_Loc - Raf_DeP +Raf_Loc +/- Raf_Loc +Raf_Rest + Raf_DeP +Raf_Rest +/- Raf_Rest +Ral +/- PLD +Ral + RalBP1 +RalBP1 - Rac +RalBP1 +/- Cdc42 +RalGDS + Ral +Rap1 + Tiam +Ras + PI3K +Ras - RalGDS +Ras + Tiam +Ras + Mekk1 +Ras +/- Sos +Ras + Raf_Loc +Ras + Raf +RasGRF_GRP + Ras +RasGRF_GRP + Rac +rb - mdm2 +rb - CyclinA +rb - CyclinE +rb - e2f1 +RGS - Gaq +RGS - Gai +RGS - Gas +Rho + PI4K +Rho + PLD +Rho - Rac +Rho + Mekk1 +Rho + PTEN +Rho +/- PI5K +Rho + RhoK +RhoGDI - Rac +RhoGDI - Rho +RhoGDI - Cdc42 +RhoK + ARHGAP22 +RhoK + PI5K +RhoK +/- MLCP +RhoK + Myosin +RKIP + Raf +RKIP - GRK +SAPK - bcl2 +SAPK + MKPs +SAPK + MLK2 +Sek1 + p38 +Sek1 + SAPK +Shc + Grb2 +Shc + Mekk1 +SHP2 + Ras +SHP2 - p120RasGAP +SHP2 - Csk +SHP2 - Gab1 +SHP2 - Cbp +Slug - E-cad +Smac - XIAP +Smad2 + smad2_4 +smad2_4 + p15 +smad2_4 + MMP +smad2_4 - E-cad +smad2_4 + p27 +smad2_4 + p21 +Snail - E-cad +Sos + Ras +Src - Cdc42 +Src + Shc +Src + PI3K +Src + DGK +Src - p120RasGAP +Src + Graf +Src + Raf +Src +/- Csk +Src + Mekk2 +Src + Akt +Src + Fak +Src - PP2A +Src + PI5K +Src + Tiam +Src - PTEN +Src + Cbp +Src + Rap1 +Src + PLC_g +Src - Talin +Src + WASP +Src + GRK +Src - RhoGDI +Src + p190RhoGAP +Src + PDK1 +Src + PAK +Src +/- Integrins +Src + Vinc +Src + Crk +Src + Grb2 +Src + Cas +Stathmin + GEFH1 +Stress + TAO_1_2 +Stress - PTP1b +Stress + Trx +Stress + PTEN +Tab_1_2 + TAK1 +TAK1 + IKK +TAK1 + Sek1 +TAK1 + MKK3 +TAK1 + NIK +TAK1 + MKK6 +Talin +/- Integrins +Talin +/- PI5K +Talin + Fak +Talin + Vinc +TAO_1_2 + MKK3 +TAO_1_2 + MKK6 +Tgf-b + TGFR +TGFR + Smad2 +Tiam + Rac +Tpl2 + Mek +Tpl2 + Sek1 +Tpl2 + MKK6 +Tpl2 + MKK3 +Tpl2 +/- NIK +Trafs + GCK +Trafs + Mekk1 +Trafs + Tpl2 +Trafs + Trx +Trafs + Tab_1_2 +Trafs + TAK1 +Trafs + Mekk3 +Trio + Fak +Trx + ASK1 +Trx - PKC +WASP + Arp_2_3 +wip1 - atm +wip1 + mdm2 +wip1 - p14arf +wip1 + mdmx +WNT + Frizzled +XIAP - Casp-3 +XIAP - Casp-9 +Casp-3 + Apoptosis +MMP + Metastasis +Rho + Metastasis +E-cad - Metastasis +CyclinA + Proliferation +CyclinB + Proliferation +CyclinD + Proliferation +CyclinE + Proliferation diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/flobak_2015/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/flobak_2015/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,48 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Flobak, Å., Baudot, A., Remy, E., Thommesen, L., Thieffry, D., Kuiper, M., & Laegreid, A. +Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling. +PLoS Comput Biol, (2015) 11(8). +""" + +import os + +import pandas as pd + +import sfa +import sfa.base + + +def create_data(): + return FlobakData() + + +class FlobakData(sfa.base.Data): + + def __init__(self): + + self._abbr = "flobak_2015" + self._name = "Flobak et al. PLoS Comput Biol, (2015) 11(8)" + inputs = {} + + dpath = os.path.dirname(__file__) + fpath_network = os.path.join(dpath, 'network.sif') + A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True) + self._A = A + self._n2i = n2i + self._i2n = {idx: name for name, idx in n2i.items()} + self._dg = dg + self._inputs = inputs + + # The following members are not defined due to the lack of data. + self._df_conds = None + self._df_exp = None + self._df_ptb = None + self._has_link_perturb = False + self._names_ptb = None + self._iadj_to_idf = None + # end of def __init__ +# end of def class + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/flobak_2015/network.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/flobak_2015/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,149 @@ +p38alpha - TAB1 +AKT + CFLAR +ITCH - CFLAR +AKT + IKKA +DKK1gene + DKK1 +AKT - pras40 +LRP - Axin +betacatenin + TCF +NLK - TCF +TCF + cMYC +FOXO + RTPKgene +GSK3 + TSC2 +IKKB - TSC2 +AKT - TSC2 +RSK - TSC2 +ERK - TSC2 +cMYC - SFRP1gene +CytochromeC + Caspase9 +p38alpha + p53 +MDM2 - p53 +TCF + DKK1gene +cMYC - DKK1gene +GRB2 + GAB +ERK - GAB +PDK1 + AKT +mTORC2 + AKT +AKT - ASK1 +ERK + RSK +PDK1 + RSK +GAB + SHP2 +SOS + Ras +SHP2 + Ras +Rac + MEKK4 +PDK1 + S6K +mTORC1 + S6K +ASK1 + MKK3 +TAK1 + MKK3 +PI3K + PDK1 +PTEN - PDK1 +Raf + MEK +MAP3K8 + MEK +ERK - MEK +p38alpha + DUSP1 +MSK + DUSP1 +AKT - BAD +RSK - BAD +p53 + BAX +TAB1 + TAK1 +RTPKgene + RTPK +MMP + RTPK +p38alpha - RTPK +MEK - RTPK +LRP - CK1 +TCF - Egr1 +GRB2 + SOS +ERK - SOS +BAD - BCL2 +TAK1 + MKK7 +GRAP2 + MKK7 +Fz + LRP +ERK + LRP +JNK1 + LRP +p38alpha + LRP +DKK1 - LRP +SHC1 + GRB2 +IKKB + MAP3K8 +CFLAR - Caspase8 +AKT - FOXO +NLK - FOXO +LRP - GSK3 +RSK - GSK3 +S6K - GSK3 +ERK - GSK3 +p38alpha - GSK3 +Dvl - GSK3 +AKT - GSK3 +Ras + Raf +Rheb - Raf +AKT - Raf +ERK - Raf +JNK1 + ITCH +Rac + MLK3 +Egr1 + PTENgene +MKK3 + p38alpha +MKK4 + p38alpha +DUSP1 - p38alpha +TAK1 + IKKB +p53 - IKKB +ERK + MSK +p38alpha + MSK +AKT + MDM2 +MDM2gene + MDM2 +S6K - MDM2 +NFkB + MDM2gene +p53 + MDM2gene +ERK + DUSP6 +mTORC1 + DUSP6 +IKKA + NFkB +IKKB + NFkB +MSK + NFkB +MKK7 + JNK1 +MKK4 + JNK1 +DUSP1 - JNK1 +MEK + ERK +DUSP6 - ERK +TSC2 - Rheb +Dvl + Rac +mTORC2 + Rac +BAX + CytochromeC +BCL2 - CytochromeC +IKKA + betacatenin +betaTrCP - betacatenin +MEKK4 + MKK4 +MLK3 + MKK4 +TAK1 + MKK4 +GRAP2 + MKK4 +TSC2 + mTORC2 +S6K - mTORC2 +RTPK + SHC1 +PTEN - SHC1 +S6K - IRS1 +ERK - IRS1 +IKKB - IRS1 +Rheb + mTORC1 +RSK + mTORC1 +pras40 - mTORC1 +TAK1 + NLK +Fz + Dvl +Axin + betaTrCP +GSK3 + betaTrCP +CK1 + betaTrCP +SFRP1gene + SFRP1 +SFRP1 - Fz +LEF + MMP +GAB + PI3K +IRS1 + PI3K +Ras + PI3K +betacatenin + LEF +PTENgene + PTEN +GSK3 - PTEN +p38alpha - GRAP2 +RSK + CCND1 +TCF + CCND1 +Caspase8 + Caspase37 +Caspase9 + Caspase37 +Caspase37 + Antisurvival +FOXO + Antisurvival +CCND1 + Prosurvival +cMYC + Prosurvival \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/fumia_2013/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/fumia_2013/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,55 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Fumiã, H. F., & Martins, M. L. +Boolean Network Model for Cancer Pathways: Predicting +Carcinogenesis and Targeted Therapy Outcomes. +PLoS ONE, (2013) 8(7), e69008 + +""" + +import os + +import pandas as pd + +import sfa +import sfa.base + + +def create_data(): + return FumiaData() + + +class FumiaData(sfa.base.Data): + + def __init__(self): + + self._abbr = "fumia_2013" + self._name = "Fumiã et al. PLoS ONE, (2013) 8(7), e69008" + inputs = {} + inputs['Mutagen'] = 1.0 + inputs['GFs'] = 1.0 + inputs['Nutrients'] = 1.0 + inputs['TNFα'] = 0.0 + inputs['Hypoxia'] = 1.0 + + dpath = os.path.dirname(__file__) + fpath_network = os.path.join(dpath, 'network.sif') + A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True) + self._A = A + self._n2i = n2i + self._i2n = {idx: name for name, idx in n2i.items()} + self._dg = dg + self._inputs = inputs + + # The following members are not defined due to the lack of data. + self._df_conds = None + self._df_exp = None + self._df_ptb = None + self._has_link_perturb = False + self._names_ptb = None + self._iadj_to_idf = None + # end of def __init__ +# end of def class + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/fumia_2013/network.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/fumia_2013/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,253 @@ +RAF - TSC1/2 +HIF1 + VEGF +AMPK + TSC1/2 +CyclinB - Rb +ATM/ATR + CHK1/2 +Hypoxia - VHL +PI3K + PIP3 +Smad/E2F - Myc/Max +HIF1 - BAX +E2F + E2F +p53 - IKK +Cytoc/APAF1 + Caspase9 +p27 - CyclinA +Mdm2 - p53 +AKT + Glut-1 +TGFβ - Myc/Max +GSK-3 - Snail +NF-κB + Snail +Gli + CyclinD +hTERT + PI3K +p53/PTEN - PTEN +p53 + p53/PTEN +Myc/Max - p27 +ATM/ATR - Mdm2 +AKT - p21 +Myc/Max + Glut-1 +Myc/Max - p21 +Hypoxia - PHDs +NF-κB + Myc +TNFα + TAK1 +cdc20 - CyclinA +TAK1 - TCF +CyclinB + cdc20 +GFs + RTK +Smad/Miz-1 + p27 +UbcH10 + cdh1/UbcH10 +p21 - CyclinA +cdh1 + cdh1/UbcH10 +p53 + p21 +mTOR - GSK-3 +β-catenin + TCF +Myc/Max + hTERT +Myc/Max + PDK1 +GSK-3/APC - β-catenin +p53 - CyclinB +WNT + PKC +NF1 + hTERT +IKK + NF-κB +Mdm2 + p53/Mdm2 +p53 - β-catenin +NF-κB + GSH +E-cadherin - NF-κB +Rb - CyclinA +ROS + DNA_Damage +HIF1 + MXI1 +HIF1 + COX412 +HIF1 + TSC1/2 +Miz-1 + Smad/Miz-1 +mTOR + p70 +p90RSK - BAD +HIF1 + p53 +TAK1 + IKK +PIP3 + PDK1 +TCF + Myc +E2F + E2F/CyclinE +ATM/ATR + DNA_Repair +PKC + IKK +mTOR + HIF1 +PKC + NF1 +Caspase8 + Cytoc/APAF1 +Nutrients + RAGS +mTOR + IKK +NF-κB + Bcl-2 +RHEB + mTOR +PDK1 + p70 +Smad/Miz-1 - hTERT +E2F/CyclinE + CyclinA +p53 - hTERT +JNK + BAX +CyclinA - cdh1 +p53 + BAX +VHL - HIF1 +Smad + Smad/E2F +CyclinB + UbcH10 +Ras + RAF +HIF1 - Myc +Gli + Myc +AKT - TSC1/2 +PDK1 + AKT +PIP3 + NF-κB +TNFα + FADD +PHDs - IKK +FosJun + Myc +RTK + Ras +COX412 - ROS +p53 - Bcl-2 +NF1 - Ras +Smad/Miz-1 + p21 +CyclinB - cdh1 +eEF2 - hTERT +p90RSK + eEF2K +Myc + Myc/Max +FADD + Caspase8 +E2F + Myc +Myc/Max + HIF1 +CyclinA + UbcH10 +TGFβ + Smad +TNFα - Smad +CyclinA - CyclinE +UbcH10 + UbcH10 +PTEN - PIP3 +BAX + Cytoc/APAF1 +WNT + Dsh +p90RSK - GSK-3 +CyclinE - Rb +cdc20 - CyclinB +Bcl-2 - p53 +Hypoxia - RAGS +MXI1 - Myc/Max +HIF1 + Glut-1 +FOXO - HIF1 +CyclinD - Rb +CyclinA - Rb +AMP/ATP + AMPK +p21 - CyclinD +Smad/Miz-1 + p15 +HIF1 + hTERT +HIF1 - BAD +Dsh - GSK-3 +NF-κB + CyclinD +PDK1 + p90RSK +p21 + GSH +TCF + CyclinD +CyclinA + CyclinA +HIF1 + LDHA +HIF1 + TGFβ +E2F + CyclinE +Rb - CyclinE +BAD - Bcl-XL +AKT + hTERT +Bcl-XL - Cytoc/APAF1 +AKT - Cytoc/APAF1 +GSK-3 + GSK-3/APC +AKT + Mdm2 +p53 - HIF1 +Snail + NF-κB +Nutrients - AMP/ATP +HIF1 + AMPK +p53 + Cytoc/APAF1 +GFs - AMPK +Myc/Max + LDHA +Hypoxia + HIF1 +ROS - PHDs +hTERT - p21 +FosJun + CyclinD +Myc/Max + CyclinD +p27 - CyclinE +Myc/Max - Miz-1 +TGFβ + JNK +TSC1/2 - RHEB +cdh1 - cdc20 +BAX - Bcl-2 +RAGS + mTOR +Ras + PI3K +cdc20 + cdh1 +RAF - BAD +Miz-1 + p15 +CyclinD - p27 +BAD - Bcl-2 +CHK1/2 + p53 +Mdm2 - Rb +AKT - p27 +cdc20 + UbcH10 +cdh1 - CyclinB +GSH - ROS +p27 - CyclinB +ERK-1/2 - TSC1/2 +Smad/Miz-1 + Snail +Rb - E2F +Ras + p14ARF +ERK-1/2 + p90RSK +p21 - CyclinE +RTK + PKC +Bcl-2 - Cytoc/APAF1 +CyclinE + E2F/CyclinE +Smad + Smad/Miz-1 +Bcl-2 - BAX +PTEN - APC +ROS - VHL +DNA_Damage + ATM/ATR +Caspase8 + Apoptosis +AKT - GSK-3 +BaK + Cytoc/APAF1 +p90RSK - TSC1/2 +Myc/Max + GSH +E2F + p14ARF +NF-κB - E-cadherin +AMPK - mTOR +CyclinA - E2F +Gli + WNT +Caspase8 + BaK +HIF1 + PDK1 +NF-κB + Slug +p53 + Mdm2 +GSK-3 - CyclinD +HIF1 + p27 +CyclinB - p27 +eEF2K - eEF2 +p14ARF - Mdm2 +APC + GSK-3/APC +p53 - Snail +HIF1 + p21 +PHDs - HIF1 +p53 - WNT +Myc/Max + VEGF +Caspase9 + Apoptosis +p70 + eEF2K +NF-κB + hTERT +PKC + RAF +p53/Mdm2 - Slug +p15 - CyclinD +ERK-1/2 + FosJun +p53 + p53/Mdm2 +Snail - E-cadherin +FOXO - CyclinD +AKT - BAD +PIP3 + AKT +Myc/Max + p14ARF +LDHA + Acid_Lactic +p21 - CyclinB +Max + Myc/Max +p53 - Bcl-XL +cdh1/UbcH10 + CyclinA +AKT + mTOR +ATM/ATR + AMPK +Mutagen + DNA_Damage +TCF + Slug +CyclinB - E2F +Slug - E-cadherin +p53 + TSC1/2 +AKT + IKK +CyclinA - p27 +cdh1 - UbcH10 +p27 - CyclinD +JNK + FosJun +AKT - FOXO +PTEN + p53/PTEN +ERK-1/2 + Myc +RAF + ERK-1/2 +CyclinA + Proliferation +CyclinB + Proliferation +CyclinD + Proliferation +CyclinE + Proliferation \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/korkut_2015a/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/korkut_2015a/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,90 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Korkut and Wang et al. +Perturbation biology nominates upstream-downstream drug combinations +in RAF inhibitor resistant melanoma cells +eLife (2015) 4:e04640 +""" + +import os + +import numpy as np +import pandas as pd + +import sfa +import sfa.base + +def create_data(): + dpath = os.path.dirname(__file__) + fpath_network = os.path.join(dpath, "model_3250.sif") + return KorkutData("KORKUT_2015A", dpath, fpath_network) + + +class KorkutData(sfa.base.Data): + + def __init__(self, abbr, dpath, fpath_network): + + self._abbr = abbr + self._name = "Korkut and Wang et al. eLife 2015;4:e04640" + + fpath_ptb = os.path.join(dpath, "ptb.tsv") + + A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True) + self._A = A + self._n2i = n2i + self._dg = dg + self._df_conds = pd.read_table(os.path.join(dpath, "conds.tsv"), + header=0, index_col=0) + self._df_exp = pd.read_table(os.path.join(dpath, "exp.tsv"), + header=0, index_col=0) + + self._inputs = {} + self._df_ptb = pd.read_table(fpath_ptb, index_col=0) + if any(self._df_ptb.Type == 'link'): + self._has_link_perturb = True + else: + self._has_link_perturb = False + + + # Remove the rows and columns of a node which is not + # included in the given network structure. + not_included = set(self._df_conds.columns) - set(self._n2i.keys()) + for target in not_included: + ind_removed = self._df_conds[self.df_conds[target] != 0].index + self._df_conds.drop(ind_removed, inplace=True) + self._df_conds.drop([target], axis=1, inplace=True) + self._df_exp.drop(ind_removed, inplace=True) + self._df_ptb.drop([target], inplace=True) + + # Re-index according to the new size. + self._df_conds.index = np.arange(1, self._df_exp.shape[0]+1) + self._df_exp.index = self._df_conds.index + + + self._names_ptb = [] + for i, row in enumerate(self._df_conds.iterrows()): + row = row[1] + list_name = [] # Target names + for target in self._df_conds.columns[row.nonzero()]: + list_name.append(target) + # end of for + self._names_ptb.append(list_name) + # end of for + + s1 = set(self._df_exp.columns) # From experimental data + s2 = set(n2i.keys()) # From network + exp_only = s1 - s2 + self._df_exp.drop(exp_only, axis=1, inplace=True) + + + # For mapping from the indices of adj. matrix to those of DataFrame + # (arrange the indices of adj. matrix according to df_exp.columns) + self._iadj_to_idf = [n2i[x] for x in self._df_exp.columns] + self._i2n = {idx: name for name, idx in n2i.items()} + # end of def __init__ + + + + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/korkut_2015a/conds.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/korkut_2015a/conds.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,90 @@ + aMEK aAKT aHDAC aMDM2 aJAK aBRAFm aPKC aSTAT3 amTOR aPI3K aCDK4 aSRC +1 -0.698 0 0 0 0 0 0 0 0 0 0 0 +2 -0.698 0 -0.89 0 0 0 0 0 0 0 0 0 +3 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/dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/korkut_2015a/model_3250.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,464 @@ +4EBP1pS65 - G1arrest +RbpS807 - ERa +RbpS807 + G1arrest +MEKpS217 + MAPKpT202 +MEKpS217 - BIM +MEKpS217 - ELK1pS383 +MEKpS217 - G2arrest +S6 - S6pS240 +PAI-1 + GATA3 +AKTpS473 + TSC2pT1462 +AMPKpT172 - RbpS807 +AMPKpT172 + S6 +b-Catenin - b-CateninpS +b-Catenin - IGF1R-beta +b-Catenin - YBIpS102 +b-Catenin - SRCpY416 +BIM - CyclinD1 +BIM + GATA3 +BIM - RAD51 +Caveolin + IGF1R-beta +Caveolin + SMAD3 +Caveolin - G2arrest +CyclinB1 + RbpS807 +CyclinB1 + PLK1 +CyclinD1 + RbpS807 +CyclinD1 - BIM +CyclinD1 - YAPpS127 +CyclinD1 - ACCpS79 +IGFBP2 - AKTpS473 +IGFBP2 + BIM +IGFBP2 + Fibronectin +IGFBP2 - mTORpS2448 +IGFBP2 + cellviab +p38pT180 - MAPKpT202 +p38pT180 + G2M +p38pT180 - G1arrest +p38pT180 - cellviab +p53 + Caveolin +p53 + BCL-XL +p53 + BCL2 +p53 + SRC +p53 + G1arrest +p70S6KpT389 - Fibronectin +p70S6KpT389 + S6pS235 +p70S6KpT389 + S6pS240 +SRCpY527 + Caspase9 +SRCpY527 + CHK1pS345 +SRCpY527 - G2M +STAT3pY705 - STAT3 +STAT3pY705 - STAT5 +STAT3pY705 - STAT5pY694 +STAT3pY705 - a-tubulin +STAT3pY705 + SRC +STAT3pY705 - G2M +STAT3pY705 - Sarrest +TSC2pT1462 - EGFRV +YAPpS127 + IRS1 +YAPpS127 - p21 +YAPpS127 + PDK1pS241 +YAPpS127 - G2arrest +ACC1 + PI3Kp85 +ACC1 + 4EBP1pT70 +ACC1 - Sarrest +AKTpT308 + AKTpS473 +AKTpT308 + GSK3abpS21 +AKTpT308 + GSKpS9 +AKTpT308 - BCL-XL +AKTpT308 + p21 +AKT - MEKpS217 +AKT + GSK3abpS21 +AKT + TSC2pT1462 +AKT + YAPpS127 +AKT - YBIpS102 +AKT - BCL-XL +AKT + mTORpS2448 +AKT - Collagenase +b-CateninpS + PKCa +b-CateninpS + p38 +b-CateninpS + G2M +b-CateninpS - Sarrest +Fibronectin - CyclinB1 +Fibronectin - SRCpY527 +Fibronectin + IGF1R-beta +Fibronectin + IRS1 +Fibronectin - PDK1pS241 +Fibronectin - IRS1pS307 +Fibronectin - Sarrest +HSP27 - 4EBP1pS65 +HSP27 - PAI-1 +HSP27 + SMAD3 +HSP27 + STAT3 +HSP27 - G2arrest +IGF1R-beta + b-Catenin +IGF1R-beta + IGFBP2 +IGF1R-beta + Fibronectin +IGF1R-beta - IRS1 +IGF1R-beta + G2M +IRS1 - p38pT180 +IRS1 + COX2 +IRS1 - mTORpS2448 +IRS1 - IRS1pS307 +p27 - MAPKpT202 +p27 - GSK3abpS21 +p27 + IGFBP2 +p27 - c-Myc +p27 - COX2 +p27 - YBIpS102 +p27 - G2M +p27 + G1arrest +p27 + cellviab +c-Myc - p27 +c-Myc - SMAD3 +c-Myc + STAT5pY694 +c-Myc + c-JUNpS73 +c-Myc - PKCa +c-Myc - SMAD3pS423 +c-Myc - G1arrest +SMAD3 + S6 +SMAD3 + Caveolin +SMAD3 + CyclinD1 +SMAD3 + Fibronectin +SMAD3 + c-Myc +SMAD3 + cellviab +STAT3 + STAT5 +STAT3 + STAT5pY694 +STAT3 + p21 +STAT3 + PDK1pS241 +STAT3 - G1arrest +STAT5 - TSC2pT1462 +STAT5 - STAT3 +STAT5 - CHK1pS345 +STAT5 - p21 +STAT5 - G2M +STAT5 + cellviab +STAT5pY694 + STAT3pY705 +STAT5pY694 + TSC2pT1462 +STAT5pY694 + AKT +STAT5pY694 - GSK3ab +STAT5pY694 + STAT6pY641 +STAT5pY694 - Sarrest +COX2 - STAT3 +COX2 - G1arrest +PAX2 - SMAD3 +PAX2 + CHK2pT68 +PAX2 + ATMpS1981 +PAX2 + BAK +PLK1 + CyclinB1 +PLK1 - CHK2pT68 +PLK1 - G1arrest +PLK1 + cellviab +YBIpS102 - HSP27 +YBIpS102 + CHK1pS345 +YBIpS102 - PDK1pS241 +YBIpS102 - PKCa +CHK2pT68 - PLK1 +CHK2pT68 - cRAFV +CHK2pT68 - SRCpY416 +CHK2pT68 + G1arrest +CHK2pT68 + Sarrest +Caspase9 + AKTpS473 +Caspase9 + b-Catenin +Caspase9 + CyclinB1 +Caspase9 + c-JUNpS73 +Caspase9 - G2arrest +Caspase9 + Sarrest +a-tubulin + COX2 +a-tubulin - BAK +a-tubulin + SRCpY416 +a-tubulin + IRS1pS307 +a-tubulin + p38 +a-tubulin + G1arrest +a-tubulin + cellviab +ATMpS1981 + PLK1 +ATMpS1981 + S6pS240 +ATMpS1981 - bRAF +ATMpS1981 + G1arrest +ATMpS1981 - Sarrest +ATMpS1981 - cellviab +ATR - c-Myc +ATR + CHK2pT68 +ATR - ERa +ATR - SRC +ATR - Sarrest +BAK - PAI-1 +BAK + PAX2 +BAK + ATR +BAK + ERa +BAK + TAZpS89 +BAK - G2arrest +BCL-XL + p53 +BCL-XL - STAT3 +BCL-XL + STAT5pY694 +BCL-XL + BCL2 +BCL-XL - 4EBP1pT70 +BCL-XL + G2arrest +BCL-XL + cellviab +BCL2 - 4EBP1pS65 +BCL2 - MEKpS217 +BCL2 - GSKpS9 +BCL2 + p53 +BCL2 - p27 +BCL2 + BCL-XL +BCL2 + GATA3 +BCL2 - GSK3ab +BCL2 + STAT6pY641 +BCL2 - G2M +BCL2 - cellviab +bRAF + MEKpS217 +bRAF - SRCpY527 +bRAF + AKTpT308 +bRAF + AKT +bRAF + a-tubulin +bRAF + p38 +bRAF + G1arrest +c-JUNpS73 - CyclinB1 +c-JUNpS73 - STAT3pY705 +c-JUNpS73 - ACC1 +c-JUNpS73 + c-Myc +c-JUNpS73 - 4EBP1pT37 +c-JUNpS73 + G2M +c-JUNpS73 - G2arrest +Casp9cAsp31 - ATR +Casp9cAsp31 + G2M +CHK1pS345 - AKTpS473 +CHK1pS345 - p27 +CHK1pS345 + YBIpS102 +CHK1pS345 - p38 +cRAFV - IRS1 +cRAFV - BCL2 +cRAFV + bRAF +cRAFV - ERa +cRAFV - Collagenase +cRAFV + p38 +cRAFV + TSC2 +cRAFV + G1arrest +cRAFV + G2arrest +EGFRV - ATR +EGFRV - bRAF +EGFRV + Sarrest +ELK1pS383 + Caveolin +ELK1pS383 - p70S6KpT389 +ELK1pS383 - GSK3ab +ELK1pS383 - G1arrest +ELK1pS383 + cellviab +ERa - p70S6KpT389 +ERa - 4EBP1pT37 +ERa + Caspase9 +ERa + BAK +ERa + GSK3ab +GATA3 - 4EBP1pS65 +GATA3 + Caveolin +GATA3 - SMAD3 +GATA3 + PAX2 +GATA3 + EGFRV +GATA3 - SMAD3pS423 +GATA3 + G2M +GATA3 - G2arrest +GATA3 - Sarrest +GSK3ab + b-Catenin +GSK3ab + GSK3abpS21 +GSK3ab + GSKpS9 +GSK3ab - SRCpY527 +GSK3ab - AKTpT308 +GSK3ab - Caspase9 +GSK3ab + mTORpS2448 +GSK3ab + G2M +mTORpS2448 - AKTpS473 +mTORpS2448 + GSKpS9 +mTORpS2448 + p70S6KpT389 +mTORpS2448 + AKT +mTORpS2448 + CyclinE1 +mTORpS2448 + p38 +mTORpS2448 - G1arrest +p21 - IRS1 +p21 - SRCpY416 +p21 + G2M +STAT6pY641 - CyclinE1 +STAT6pY641 - SMAD3pS423 +STAT6pY641 - cellviab +TAZpS89 - BCL-XL +TAZpS89 + XRCC1 +TAZpS89 + Collagenase +TAZpS89 + G1arrest +XRCC1 - GSK3abpS21 +XRCC1 + GATA3 +XRCC1 - 4EBP1pT70 +XRCC1 - G2M +XRCC1 + Sarrest +PCNA - 4EBP1pS65 +PCNA - S6 +PCNA + STAT5pY694 +PCNA - p21 +PCNA + TAZpS89 +PCNA - RAD51 +PCNA - TSC2 +PDK1pS241 - RbpS807 +PDK1pS241 - a-tubulin +PDK1pS241 - BAK +PDK1pS241 + G1arrest +PDK1pS241 + G2arrest +PDK1pS241 - cellviab +PI3Kp85 - AMPKpT172 +PI3Kp85 + GSK3abpS21 +PI3Kp85 + TSC2pT1462 +PI3Kp85 - STAT3 +PI3Kp85 + STAT5 +PI3Kp85 + COX2 +PI3Kp85 - ATR +PI3Kp85 - CHK1pS345 +PI3Kp85 - SRCpY416 +PI3Kp85 - CyclinE1 +PI3Kp85 + IRS1pS307 +PI3Kp85 + SRC +PI3Kp85 - G2M +PKCa - AKTpS473 +PKCa - GSKpS9 +PKCa - YAPpS127 +PKCa + c-Myc +PKCa + bRAF +PKCa + PDK1pS241 +PKCa + SRC +PKCa - G1arrest +PKCa - G2arrest +RAD51 - p27 +RAD51 - PCNA +RAD51 - G1arrest +RAD51 - G2arrest +SRCpY416 + MEKpS217 +SRCpY416 + AKTpS473 +SRCpY416 - b-Catenin +SRCpY416 - IGF1R-beta +SRCpY416 - GATA3 +SRCpY416 + SRC +4EBP1pT70 + S6 +4EBP1pT70 + ACC1 +4EBP1pT70 + STAT3 +4EBP1pT70 - EGFRV +4EBP1pT70 + GSK3ab +4EBP1pT70 + cellviab +ACCpS79 + XRCC1 +ACCpS79 + G2M +Collagenase + STAT6pY641 +Collagenase + PCNA +Collagenase + PKCa +CyclinE1 - RbpS807 +CyclinE1 - AMPKpT172 +CyclinE1 + CHK2pT68 +IRS1pS307 + 4EBP1pT37 +IRS1pS307 - p21 +p38 + p38pT180 +p38 - STAT3pY705 +p38 + AKT +p38 - STAT5 +p38 + a-tubulin +p38 + ATR +p38 - Casp9cAsp31 +p38 - ELK1pS383 +SMAD3pS423 + p27 +SMAD3pS423 + c-Myc +SMAD3pS423 - GATA3 +TSC2 - PAI-1 +TSC2 + Caveolin +TSC2 + TSC2pT1462 +TSC2 - Casp9cAsp31 +TSC2 + cRAFV +TSC2 - G2arrest +SRC - MEKpS217 +SRC + AKTpS473 +SRC + b-Catenin +SRC - SRCpY527 +SRC - IGF1R-beta +SRC + STAT5 +SRC + STAT5pY694 +SRC + EGFRV +SRC + PI3Kp85 +SRC - SRCpY416 +SRC + Collagenase +SRC + G1arrest +aMEK + MAPKpT202 +aMEK + MEKpS217 +aMEK - Caveolin +aMEK + STAT3pY705 +aMEK - STAT5 +aMEK + 4EBP1pT37 +aMEK + bRAF +aMEK - G2M +aAKT + AKTpS473 +aAKT - Caveolin +aAKT + AKTpT308 +aAKT + HSP27 +aAKT - p21 +aAKT + G1arrest +aHDAC + AKTpS473 +aHDAC - IGFBP2 +aHDAC + p53 +aHDAC + ACC1 +aHDAC - STAT3 +aHDAC - COX2 +aHDAC - S6pS235 +aHDAC - G2arrest +aMDM2 - p53 +aMDM2 + Fibronectin +aMDM2 + SRC +aMDM2 + cellviab +aJAK + STAT5pY694 +aJAK - c-JUNpS73 +aJAK + cRAFV +aJAK - PDK1pS241 +aJAK - cellviab +aBRAFm + MEKpS217 +aBRAFm + ACC1 +aBRAFm - AKTpT308 +aBRAFm + AKT +aBRAFm + SRCpY416 +aBRAFm - G1arrest +aBRAFm + G2arrest +aBRAFm + cellviab +aPKC - PAI-1 +aPKC + AKTpS473 +aPKC - b-Catenin +aPKC + STAT3pY705 +aPKC - IRS1 +aPKC + SMAD3 +aPKC - COX2 +aPKC + p21 +aPKC - SRCpY416 +aPKC - CyclinE1 +aPKC + SMAD3pS423 +aPKC - SRC +aPKC + G1arrest +aSTAT3 + Caveolin +aSTAT3 - STAT5 +aSTAT3 + STAT5pY694 +aSTAT3 + cRAFV +aSTAT3 - PCNA +aSTAT3 + G1arrest +aSTAT3 + G2arrest +amTOR - p53 +amTOR + p70S6KpT389 +amTOR - CyclinE1 +amTOR - Sarrest +aPI3K + AKTpS473 +aPI3K - CyclinE1 +aCDK4 + RbpS807 +aCDK4 + Caveolin +aCDK4 - SRCpY527 +aCDK4 + IRS1pS307 +aCDK4 - G2arrest +aCDK4 - Sarrest +aCDK4 + cellviab +aSRC + MEKpS217 +aSRC + AKTpS473 +aSRC - b-Catenin +aSRC + SRCpY527 +aSRC + STAT3pY705 +aSRC + AKTpT308 +aSRC - IGF1R-beta +aSRC - c-Myc +aSRC + STAT3 +aSRC + STAT5 +aSRC - CHK2pT68 +aSRC + PI3Kp85 +aSRC + SRCpY416 +aSRC - Sarrest diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/korkut_2015a/ptb.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/korkut_2015a/ptb.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,13 @@ +Target Type Value Comment +aMEK node -1 +aAKT node -1 +aHDAC node -1 +aMDM2 node -1 +aJAK node -1 +aBRAFm node -1 +aPKC node -1 +aSTAT3 node -1 +amTOR node -1 +aPI3K node -1 +aCDK4 node -1 +aSRC node -1 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/molinelli_2013/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/molinelli_2013/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,36 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Molinelli, E. J. et al. +Perturbation biology: inferring signaling networks in cellular systems. +PLoS Computational Biology, (2013) 9(12), e1003290. +http://doi.org/10.1371/journal.pcbi.1003290 + +""" + +import os + +import pandas as pd + +import sfa +import sfa.base + +def create_data(): + return MolinelliData() + + +class MolinelliData(sfa.base.Data): + + def __init__(self): + + self._abbr = "MOLINELLI_2013" + self._name = "Molinell et al. 2013 PLoS Comput Biol 9(12): e1003290" + + self.initialize(__file__) + + # end of def __init__ + + + + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/molinelli_2013/ba.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/molinelli_2013/ba.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,45 @@ + 4EBP1_pS65_V Rb_pS807_V MAPK_pT202 MEK1_2_pS217 AKT_pS473_V BIM_V CyclinB1_V CyclinD1_V p38_pT180_V SRC_pY527_V STAT3_pY705 TSC2_pT1462 p27_mouse c-Myc PLK1 S6_pS240_V cellprol aMEK12 aAKT aHDAC aBRAFm aPKC aSTAT3 amTOR aPI3K +1 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01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/molinelli_2013/exp.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,34 @@ + 4EBP1_pS65_V Rb_pS807_V MAPK_pT202 MEK1_2_pS217 AKT_pS473_V BIM_V CyclinB1_V CyclinD1_V p38_pT180_V SRC_pY527_V STAT3_pY705 TSC2_pT1462 p27_mouse c-Myc PLK1 S6_pS240_V cellprol +1 -0.43 -0.61 -0.86 -0.71 0.1 0.37 -0.34 -0.56 -0.3 -0.31 0.13 -0.28 0.32 -0.11 -0.02 -0.48 0.24 +2 -0.76 -0.46 -0.43 -0.54 -2.53 0.23 -0.39 -0.15 -0.12 -0.32 0.18 -0.65 0.02 -0.04 -0.05 -0.95 -0.26 +3 -0.17 0.08 -0.14 0.1 -0.07 0.16 -0.15 0.15 0.05 0.1 -0.14 -0.17 0.12 -0.13 0.07 0.04 0.1 +4 -0.33 -0.36 -0.63 -0.78 0.18 0.26 -0.15 -0.34 -0.15 -0.23 0.15 -0.31 0.32 -0.08 0.05 0.08 0.13 +5 -0.49 -0.24 -0.26 -0.08 1.26 0.13 -0.84 0.12 -0.06 0.03 -0.17 0.2 -0.05 0.03 -0.42 -2.25 -1.28 +6 -0.62 -0.19 -0.33 -0.21 -2.16 0.01 -0.16 0.25 -0.14 -0.16 -0.21 -0.45 0.08 0.24 -0.15 -0.57 -0.81 +7 -0.79 -1.02 -1.23 -0.69 -2.16 0.56 -0.85 -0.75 -0.04 -0.34 0.2 -0.52 0.44 -0.27 -0.39 -1.87 0.04 +8 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-0.83 -0.44 -0.64 -0.52 -0.83 0.28 -0.91 0.09 -0.27 -0.28 0.01 -0.5 -0.09 0.12 -0.54 -3.76 -1.44 +19 -1.38 -0.88 -0.75 -0.75 -3.31 0.36 -1.16 -0.19 -0.14 -0.25 0.13 -0.61 0.11 0.07 -0.79 -2.45 -1.36 +20 -0.28 -0.22 -0.88 -0.71 0.02 0.41 -0.28 -0.32 -0.12 0 0.02 -0.2 0.26 -0.29 0.09 -0.13 0.22 +21 -0.53 -0.26 0.26 0 0.45 0.01 -0.21 0.23 0.01 0.11 -0.49 0.21 0.06 -0.32 -0.15 -1.24 -0.67 +22 -0.5 0.11 -0.37 0.12 0.03 0.21 -0.03 0.02 -0.09 0.03 -0.25 -0.22 0.07 0.04 0.02 -0.06 -0.36 +23 -0.49 -0.04 -0.43 -0.02 1.35 0.12 -0.62 0.26 -0.02 0.11 -0.2 0.21 -0.04 -0.03 -0.21 -2.33 -1.14 +24 -0.78 -0.16 -0.47 -0.38 -2.36 0.27 -0.64 0.11 -0.14 0.08 -0.07 -0.48 0.09 0.04 -0.13 -0.85 -0.36 +25 -0.49 -0.4 -0.12 -0.7 0.03 0.03 0 -0.4 0.02 -0.17 -0.47 0.03 0.14 -0.44 -0.09 -1.16 -0.73 +26 -0.46 -0.3 -0.62 -0.78 0.14 0.22 -0.21 -0.38 -0.1 -0.24 0.03 -0.34 0.18 -0.04 0.04 -0.2 -0.27 +27 -0.46 -0.55 -0.86 -0.67 1.62 0.21 -0.99 -0.14 -0.13 -0.08 -0.03 0 0.15 0.04 -0.46 -2.91 -1.04 +28 -0.74 -0.73 -0.66 -0.7 -1.86 0.22 -1.09 -0.27 -0.2 -0.18 0.07 -0.34 0.41 -0.03 -0.45 -1 -0.44 +29 -0.46 -0.42 0.29 -0.14 0.34 -0.08 -0.23 -0.02 0 0 -0.45 0.41 -0.1 -0.13 -0.24 -2.9 -1.36 +30 -1.15 -0.59 0.08 -0.38 -2.35 -0.03 -0.61 -0.24 -0.14 -0.17 -0.36 -0.21 0.05 -0.02 -0.31 -2.67 -1.68 +31 -0.73 -0.38 -0.22 -0.47 1.4 0.12 -1.01 0.16 -0.08 -0.03 -0.22 -0.03 -0.03 0.2 -0.43 -2.64 -1.91 +32 -0.73 -0.32 -0.25 -0.34 -2.1 0.1 -0.7 0.05 -0.12 -0.14 -0.07 -0.48 0.03 0.25 -0.23 -0.77 -1.82 +33 -1.25 -0.66 -0.44 -0.41 0.02 0.12 -1.54 0.11 -0.18 -0.06 -0.1 -0.18 0.15 0.1 -0.89 -4.83 -2.41 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/molinelli_2013/network.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/molinelli_2013/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,40 @@ +4EBP1_pS65_V + cellprol +Rb_pS807_V + 4EBP1_pS65_V +Rb_pS807_V + MEK1_2_pS217 +Rb_pS807_V + MAPK_pT202 +MAPK_pT202 + MEK1_2_pS217 +MAPK_pT202 - BIM_V +MEK1_2_pS217 + MAPK_pT202 +MEK1_2_pS217 + Rb_pS807_V +AKT_pS473_V + TSC2_pT1462 +BIM_V - MAPK_pT202 +BIM_V + p27_mouse +CyclinB1_V + PLK1 +CyclinB1_V + 4EBP1_pS65_V +CyclinD1_V + Rb_pS807_V +CyclinD1_V + SRC_pY527_V +CyclinD1_V + c-Myc +p38_pT180_V + TSC2_pT1462 +p38_pT180_V + CyclinB1_V +SRC_pY527_V + MAPK_pT202 +SRC_pY527_V + TSC2_pT1462 +STAT3_pY705 + cellprol +TSC2_pT1462 + SRC_pY527_V +TSC2_pT1462 + p38_pT180_V +TSC2_pT1462 + 4EBP1_pS65_V +c-Myc + CyclinD1_V +PLK1 + Rb_pS807_V +PLK1 + CyclinB1_V +PLK1 + cellprol +PLK1 + S6_pS240_V +aMEK12 + MAPK_pT202 +aMEK12 + CyclinD1_V +aAKT + AKT_pS473_V +aHDAC - SRC_pY527_V +aBRAFm + CyclinD1_V +aBRAFm + MEK1_2_pS217 +aPKC + STAT3_pY705 +aSTAT3 + cellprol +amTOR - AKT_pS473_V +amTOR + S6_pS240_V +aPI3K + AKT_pS473_V diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/molinelli_2013/ptb.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/molinelli_2013/ptb.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,9 @@ +Target Type Value Comment +aMEK12 node -1 "PD0325901" +aAKT node -1 "AKTi pS473" +aHDAC node -1 "HNHA" +aBRAFm node -1 "PLX4720" +aPKC node -1 "RO-31-7549" +aSTAT3 node -1 "Stattic" +amTOR node -1 "Temsirolimus" +aPI3K node -1 "ZSTK474" \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/nelander_2008/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/nelander_2008/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,33 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Nelander, S. et al. +Models from experiments: combinatorial drug perturbations of cancer cells. +Molecular Systems Biology (2008) 4(1), 216. +http://doi.org/10.1038/msb.2008.53 +""" + +import os + +import pandas as pd + +import sfa +import sfa.base + +def create_data(): + return NelenderData() + + +class NelenderData(sfa.base.Data): + + def __init__(self): + + self._abbr = "NELANDER_2008" + self._name = "Nelander et al. 2008 Mol Sys Biol (2008) 4(1), 216" + inputs = {} + inputs['EGF'] = 1.0 + self.initialize(__file__, inputs=inputs) + # end of def __init__ + + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/nelander_2008/ba.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/nelander_2008/ba.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,22 @@ + IGF1R PI3K mTOR PKCdelta p-MEK EGFR p-ERK p-AKT p-P70S6K p-eIF4E p-RAF p-S6 G1-arrest apoptosis +1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 +2 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 +3 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 +4 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 +5 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 +6 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 +7 -1 -1 0 0 0 0 0 0 0 0 0 0 0 0 +8 -1 0 -1 0 0 0 0 0 0 0 0 0 0 0 +9 0 -1 -1 0 0 0 0 0 0 0 0 0 0 0 +10 -1 0 0 -1 0 0 0 0 0 0 0 0 0 0 +11 0 -1 0 -1 0 0 0 0 0 0 0 0 0 0 +12 0 0 -1 -1 0 0 0 0 0 0 0 0 0 0 +13 -1 0 0 0 -1 0 0 0 0 0 0 0 0 0 +14 0 -1 0 0 -1 0 0 0 0 0 0 0 0 0 +15 0 0 -1 0 -1 0 0 0 0 0 0 0 0 0 +16 0 0 0 -1 -1 0 0 0 0 0 0 0 0 0 +17 -1 0 0 0 0 -1 0 0 0 0 0 0 0 0 +18 0 -1 0 0 0 -1 0 0 0 0 0 0 0 0 +19 0 0 -1 0 0 -1 0 0 0 0 0 0 0 0 +20 0 0 0 -1 0 -1 0 0 0 0 0 0 0 0 +21 0 0 0 0 -1 -1 0 0 0 0 0 0 0 0 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/nelander_2008/conds.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/nelander_2008/conds.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,22 @@ + IGF1R PI3K mTOR PKCdelta p-MEK EGFR +1 -1 0 0 0 0 0 +2 0 -1 0 0 0 0 +3 0 0 -1 0 0 0 +4 0 0 0 -1 0 0 +5 0 0 0 0 -1 0 +6 0 0 0 0 0 -1 +7 -1 -1 0 0 0 0 +8 -1 0 -1 0 0 0 +9 0 -1 -1 0 0 0 +10 -1 0 0 -1 0 0 +11 0 -1 0 -1 0 0 +12 0 0 -1 -1 0 0 +13 -1 0 0 0 -1 0 +14 0 -1 0 0 -1 0 +15 0 0 -1 0 -1 0 +16 0 0 0 -1 -1 0 +17 -1 0 0 0 0 -1 +18 0 -1 0 0 0 -1 +19 0 0 -1 0 0 -1 +20 0 0 0 -1 0 -1 +21 0 0 0 0 -1 -1 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/nelander_2008/exp.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/nelander_2008/exp.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,22 @@ + p-MEK p-ERK p-AKT p-P70S6K p-eIF4E p-RAF p-S6 G1-arrest apoptosis +1 -0.07 -0.22 0.32 0.07 -0.65 0.01 0.06 0.14 0.07 +2 -0.31 -0.02 -0.7 -0.91 -0.87 -0.01 -1.0 0.93 0.74 +3 -0.25 -0.58 0.16 -0.88 -0.02 -0.21 -1.0 0.83 0.0 +4 -0.05 -0.25 0.37 -0.5 -0.05 -0.36 -0.25 0.82 0.0 +5 0.96 -0.99 0.12 -0.35 -0.77 -0.8 -0.06 0.69 0.2 +6 -0.84 -0.98 -0.58 -0.63 -1.0 -0.84 -0.28 0.55 0.4 +7 0.42 0.55 -0.97 -0.64 -0.69 0.33 -1.0 0.91 0.81 +8 0.35 0.33 0.15 -0.59 -0.06 -0.57 -1.0 0.67 -0.03 +9 0.2 0.61 -0.94 -0.93 -0.89 -0.47 -1.0 0.91 0.88 +10 0.57 0.55 -0.05 -0.12 -0.32 -0.5 0.0 0.79 0.01 +11 0.47 0.58 -0.64 -0.95 -0.65 -0.7 -1.0 0.89 0.85 +12 0.39 0.42 0.05 -0.96 -0.2 -0.86 -0.99 0.88 0.49 +13 0.91 -1.0 0.04 -0.11 -0.76 -0.93 0.23 0.68 0.02 +14 0.97 -1.0 -0.88 -0.8 -0.93 -0.36 -1.0 0.92 0.84 +15 0.95 -1.0 0.39 -0.74 0.16 -0.39 -1.0 0.87 0.35 +16 0.95 -1.0 0.45 -0.05 -0.48 -0.83 -0.22 0.84 0.11 +17 -0.84 -0.96 -0.25 0.07 -0.99 -0.15 -0.08 0.44 0.25 +18 -0.66 -0.82 -0.98 -0.89 -0.99 -0.08 -1.0 0.92 0.81 +19 -0.78 -0.98 0.0 -0.91 -0.99 -0.59 -1.0 0.78 0.24 +20 -0.8 -0.82 0.24 -0.5 -0.99 -0.8 -0.52 0.82 0.29 +21 0.78 -1.0 -0.61 -0.43 -1.0 -0.95 -0.13 0.81 0.0 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/nelander_2008/network.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/nelander_2008/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,24 @@ +EGF + EGFR +PKCdelta + p-RAF +PKCdelta - G1-arrest +IGF1R - G1-arrest +p-P70S6K + p-S6 +apoptosis - p-P70S6K +apoptosis + G1-arrest +mTOR - apoptosis +mTOR + p-P70S6K +G1-arrest - p-eIF4E +G1-arrest - p-ERK +G1-arrest + EGFR +p-ERK - EGFR +p-ERK + p-eIF4E +p-AKT - apoptosis +PI3K + p-AKT +p-eIF4E - G1-arrest +p-eIF4E + PI3K +EGFR + p-MEK +EGFR - p-RAF +p-MEK + p-RAF +p-MEK + p-ERK +p-MEK - p-MEK +p-ERK + p-ERK \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/nelander_2008/network_noself.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/nelander_2008/network_noself.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,22 @@ +EGF + EGFR +PKCdelta + p-RAF +PKCdelta - G1-arrest +IGF1R - G1-arrest +p-P70S6K + p-S6 +apoptosis - p-P70S6K +apoptosis + G1-arrest +mTOR - apoptosis +mTOR + p-P70S6K +G1-arrest - p-eIF4E +G1-arrest - p-ERK +G1-arrest + EGFR +p-ERK - EGFR +p-ERK + p-eIF4E +p-AKT - apoptosis +PI3K + p-AKT +p-eIF4E - G1-arrest +p-eIF4E + PI3K +EGFR + p-MEK +EGFR - p-RAF +p-MEK + p-RAF +p-MEK + p-ERK \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/nelander_2008/ptb.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/nelander_2008/ptb.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,7 @@ +Target Type Value Comment +EGFR node -1.0 "ZD1839" +IGF1R node -1.0 "A12 antibody" +PI3K node -1.0 "LY294002" +mTOR node -1.0 "rapamycin" +PKCdelta node -1.0 "rottlerin" +p-MEK link 0.1 "PD0325901" diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/pezze_2012/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/pezze_2012/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,112 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Dalle Pezze, P. et al. +A Dynamic Network Model of mTOR Signaling Reveals +TSC-Independent mTORC2 Regulation. +Science Signaling. 2012 Mar 27;5(217):ra25 +http://doi:10.1126/scisignal.2002469 + +[Information] +- The experimental data was generated using ODE model of Pezze et al. + (It is hypothesized that the ODE model is quite well constructed enough to be + a substitute for real experimental data). +- The directed network was created by Junsoo Kang (reality312@kaist.ac.kr). +- The unit of insulin (I) stimulation is nM. +""" + +import sys +if sys.version_info <= (2, 8): + from builtins import super + +import os +import re +import glob + +import numpy as np +import pandas as pd + +import sfa +import sfa.base + + +def create_data(abbr=None): + if abbr is None: # Create all data objects + data_mult = {} # Multiple data + dpath = os.path.dirname(__file__) + + fstr_file = os.path.join(dpath, 'exp_data', '*.tsv') + for abspath in glob.glob(fstr_file): + fname = os.path.basename(abspath) + data_obj = _create_single_data(abbr, fname=fname) + data_mult[data_obj.abbr] = data_obj + + # end of for + + return data_mult + else: # Create a single data object + return _create_single_data(abbr) + +# end of def + +def _create_single_data(abbr=None, fname=None): + dpath = os.path.dirname(__file__) + + if fname: + items = re.split('[._]', fname) + + sim_duration = items[0] + data_type = items[1] + stim_I = items[2] # The concentration of insulin stimulation + + # Fetch the concentration of I + m = re.search("I=((\d|d)+)", stim_I) + dconc_I = m.group(1) + conc_I = dconc_I.replace('d', '.') # Use '.' instead of 'd' + abbr = "%s_%s_I=%s"%(sim_duration, data_type, conc_I) + + elif abbr: # Use abbr + items = re.split('[_]', abbr) + + sim_duration = items[1] + data_type = items[2] + stim_I = items[3] # The concentration of insulin stimulation + + # Fetch the concentration of I + m = re.search("I=((\w|\.)+)", stim_I) + conc_I = m.group(1) + fname = "%s_%s_I=%s.tsv" % (sim_duration, data_type, conc_I) + else: + raise ValueError("One of abbr or fname should be given" + "in %s._create_single_data()"%(__name__)) + # end of if-else + + fname_exp = os.path.join('exp_data', fname) + fname_conds = "conds.tsv" + + + return PezzeData(abbr, data_type, + conc_I, + fname_conds, fname_exp) +# end of def + + +class PezzeData(sfa.base.Data): + def __init__(self, abbr, data_type, conc_I, fname_conds, fname_exp): + super().__init__() + self._abbr = abbr + fstr_name = "PEZZE_2011_%s[I=%snM]" + str_name = fstr_name % (data_type, conc_I) + self._name = str_name + + inputs = {} + inputs['I/IGF'] = 1 #np.log2(float(conc_I)) + + self.initialize(__file__, + inputs=inputs, + fname_conds=fname_conds, + fname_exp=fname_exp) + + # end of def __init__ +# end of def class \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/pezze_2012/conds.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/pezze_2012/conds.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,22 @@ + TSC1/2 IRS1_PI3K PI3K_variant PDK2 mTORC1_pS2448 mTORC2 +1 -1 0 0 0 0 0 +2 0 -1 0 0 0 0 +3 0 0 -1 0 0 0 +4 0 0 0 -1 0 0 +5 0 0 0 0 -1 0 +6 0 0 0 0 0 -1 +7 -1 -1 0 0 0 0 +8 -1 0 -1 0 0 0 +9 -1 0 0 -1 0 0 +10 -1 0 0 0 -1 0 +11 -1 0 0 0 0 -1 +12 0 -1 -1 0 0 0 +13 0 -1 0 -1 0 0 +14 0 -1 0 0 -1 0 +15 0 -1 0 0 0 -1 +16 0 0 -1 -1 0 0 +17 0 0 -1 0 -1 0 +18 0 0 -1 0 0 -1 +19 0 0 0 -1 -1 0 +20 0 0 0 -1 0 -1 +21 0 0 0 0 -1 -1 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/pezze_2012/exp_AUC_I=100.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/pezze_2012/exp_AUC_I=100.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,9 @@ + 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 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-0.84179408 -2.5934743 0.97345868 -0.27977257 0.23443468 1.0267849 -3.231242 -3.3219281 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/pezze_2012/exp_data/60m_SS_I=800.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/pezze_2012/exp_data/60m_SS_I=800.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,22 @@ + TSC1/2 p70S6K AKT AKTP PRAS40 IRS1_PI3K mTORC1_pS2448 mTORC2 +1 -3.2183989 0.15865006 -0.12086793 -0.12069122 -0.037934857999999995 -0.27726972 0.16803329 7.0055073e-13 +2 2.1959512 -0.38638991 -2.1500656000000005 -2.1494299999999997 0.40083152 -3.2210589 -0.52795747 1.7297286000000002e-10 +3 -0.29977949 0.032167412 0.34153072 -0.51686642 -0.07597024200000001 -0.012694335 0.033804881 1.8379848 +4 -0.20025792 0.04685152099999999 0.22789462 -0.33372727 -0.049602644 -0.028655892999999998 0.023271662999999998 6.4184547e-11 +5 -0.19265929 -2.6732769 0.23463486 0.23545908 0.45004056 0.65634275 -3.2995946000000003 3.5627057e-13 +6 -0.39377848 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+IR_b + IRS1_PI3K +IR_b + PI3K_variant +IRS1_PI3K + PI3K +PI3K + PIP3 +PIP3 + PDK1 +PDK1 + AKT +AKT + AKTP +PDK2 + AKTP +PIP3 + AKT +AKT - TSC1/2 +AKTP - TSC1/2 +TSC1/2 - Rheb +mTORC1_pS2448 + p70S6K +p70S6K + S6 +p70S6K - IRS1_PI3K +p70S6K + Protein_Synthesis +mTORC1_pS2448 + eIF4E +eIF4E + Protein_Synthesis +mTORC2 + AKTP +PI3K_variant + mTORC2 +Rheb + mTORC1_Rags +mTORC1_Rags + mTORC1_pS2448 +Amino_Acids + mTORC1_Rags +S6 + Protein_Synthesis +AKT - PRAS40 +AKTP - PRAS40 +PRAS40 - mTORC1 +mTORC1 + mTORC1_Rags \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/pezze_2012/ptb.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/pezze_2012/ptb.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,7 @@ +Target Type Value Comment +TSC1/2 node -1 +IRS1_PI3K node -1 +PI3K_variant node -1 +PDK2 node -1 +mTORC1_pS2448 node -1 +mTORC2 node -1 \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/schliemann_2011/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/schliemann_2011/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,118 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Schliemann, M. et al. +Heterogeneity reduces sensitivity of cell death for TNF-Stimuli. +BMC Systems Biology 2011, 5:204 +http://www.biomedcentral.com/1752-0509/5/204 + +[Information] +- The experimental data was generated using ODE model of Schliemann et al. + (It is hypothesized that the ODE model is quite well constructed enough to be + a substitute for real experimental data). +- The directed network was created by Junsoo Kang (reality312@kaist.ac.kr). +- The unit of TNF stimulation is nM. +""" + +import sys +if sys.version_info <= (2, 8): + from builtins import super + +import os +import re +import glob + +import pandas as pd + +import sfa +import sfa.base + + +def create_data(abbr=None): + if abbr is None: # Create all data objects + data_mult = {} # Multiple data + dpath = os.path.dirname(__file__) + + fstr_file = os.path.join(dpath, 'exp_data', 'exp_*') + for abspath in glob.glob(fstr_file): + fname = os.path.basename(abspath) + data_obj = _create_single_data(abbr, fname=fname) + data_mult[data_obj.abbr] = data_obj + + # end of for + + return data_mult + else: # Create a single data object + return _create_single_data(abbr) + +# end of def + +def _create_single_data(abbr=None, fname=None): + dpath = os.path.dirname(__file__) + + if fname: + items = re.split('[._]', fname) + + sim_duration = items[1] + data_type = items[2] + stim_I = items[3] # The concentration of insulin stimulation + + # Fetch the concentration of I + m = re.search("TNF=((\d|d)+)", stim_I) + dconc_I = m.group(1) + conc_I = dconc_I.replace('d', '.') # Use '.' instead of 'd' + abbr = "%s_%s_TNF=%s"%(sim_duration, data_type, conc_I) + + elif abbr: # Use abbr + items = re.split('[_]', abbr) + + sim_duration = items[1] + data_type = items[2] + stim_I = items[3] # The concentration of insulin stimulation + + # Fetch the concentration of I + m = re.search("TNF=((\w|\.)+)", stim_I) + dconc_I = m.group(1) + conc_I = dconc_I.replace('d', '.') # Use '.' instead of 'd' + fname = "exp_%s_%s_I=%s.tsv" % (sim_duration, data_type, conc_I) + else: + raise ValueError("One of abbr or fname should be given" + "in %s._create_single_data()"%(__name__)) + # end of if-else + + fname_exp = os.path.join('exp_data', fname) + fname_conds = "conds.tsv" + + return SchliemannData(abbr, data_type, + conc_I, + fname_conds, fname_exp) + + +# end of def + + +class SchliemannData(sfa.base.Data): + def __init__(self, + abbr, + data_type, + conc_I, + fname_conds, fname_exp): + + super().__init__() + self._abbr = abbr + + fstr_name = "SCHLIEMANN_2011_%s[I=%snM]" + str_name = fstr_name % (data_type, conc_I) + self._name = str_name + + inputs = {} + inputs['TNF'] = 1 #float(conc_I) + self._inputs = inputs + + self.initialize(__file__, + inputs=inputs, + fname_conds=fname_conds, + fname_exp=fname_exp) + # end of def __init__ +# end of def class \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/schliemann_2011/conds.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/schliemann_2011/conds.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,37 @@ + IKKa NF-kB A20 FLIP XIAP Casp3 Casp6 pCasp8 +1 -1 0 0 0 0 0 0 0 +2 0 -1 0 0 0 0 0 0 +3 0 0 -1 0 0 0 0 0 +4 0 0 0 -1 0 0 0 0 +5 0 0 0 0 -1 0 0 0 +6 0 0 0 0 0 -1 0 0 +7 0 0 0 0 0 0 -1 0 +8 0 0 0 0 0 0 0 -1 +9 -1 -1 0 0 0 0 0 0 +10 -1 0 -1 0 0 0 0 0 +11 -1 0 0 -1 0 0 0 0 +12 -1 0 0 0 -1 0 0 0 +13 -1 0 0 0 0 -1 0 0 +14 -1 0 0 0 0 0 -1 0 +15 -1 0 0 0 0 0 0 -1 +16 0 -1 -1 0 0 0 0 0 +17 0 -1 0 -1 0 0 0 0 +18 0 -1 0 0 -1 0 0 0 +19 0 -1 0 0 0 -1 0 0 +20 0 -1 0 0 0 0 -1 0 +21 0 -1 0 0 0 0 0 -1 +22 0 0 -1 -1 0 0 0 0 +23 0 0 -1 0 -1 0 0 0 +24 0 0 -1 0 0 -1 0 0 +25 0 0 -1 0 0 0 -1 0 +26 0 0 -1 0 0 0 0 -1 +27 0 0 0 -1 -1 0 0 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4.5257758e-07 -4.656917e-08 8.683371300000001e-06 +25 0.023341327999999998 1.6734422e-08 -0.19132835 1.6013715e-09 9.618302099999999e-11 0.032141682000000005 4.5257758e-07 -4.656917e-08 8.683371300000001e-06 +26 0.023341327999999998 1.6734422e-08 -0.19132835 1.6013715e-09 9.618302099999999e-11 0.032141682000000005 4.5257758e-07 -4.656917e-08 8.683371300000001e-06 +27 -1.3960404e-06 -1.0736556999999998e-12 -1.9545814e-14 -0.191235 -3.1549052e-07 -0.056068691 0.00046633628 0.00051497222 0.0006581454300000001 +28 -1.3960404e-06 -1.0736556999999998e-12 -1.9545814e-14 -0.191235 -3.1549052e-07 -0.056068691 0.00046633628 0.00051497222 0.0006581454300000001 +29 -1.3960404e-06 -1.0736556999999998e-12 -1.9545814e-14 -0.191235 -3.1549052e-07 -0.056068691 0.00046633628 0.00051497222 0.0006581454300000001 +30 -1.3960404e-06 -1.0736556999999998e-12 -1.9545814e-14 -0.191235 -3.1549052e-07 -0.056068691 0.00046633628 0.00051497222 0.0006581454300000001 +31 6.6934273 4.7173223e-06 1.7747034e-07 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tools/myTools/bin/sfa/data/schliemann_2011/exp_data/exp_2h_SS_TNF=2.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/schliemann_2011/exp_data/exp_2h_SS_TNF=2.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,37 @@ + IKKa NF-kB A20 FLIP XIAP TNFRC2:FLIP:pCasp8 Casp3 Casp6 Casp8 +1 -0.499038 -2.8108904e-07 -1.2393492e-09 -2.1533243e-09 -6.9595076e-11 2.9885409e-09 7.1106276e-07 1.4793623000000002e-10 2.4148949e-10 +2 0.00024450938999999997 0.03971081 -0.0019480338000000001 -0.0060712255 -0.00022830618999999998 -0.91493403 -1.0 0.018346635 0.022732401 +3 0.056687684 3.0246892999999995e-08 -0.49654053 1.7062293e-10 2.5156133e-10 0.0056963644999999995 0.011437072 -1.4902015e-06 5.0415729000000005e-06 +4 -2.646975e-07 -1.501597e-13 2.5707794e-15 -0.49607879 1.0060367e-07 -0.88913073 0.17845886 -0.0008015235 -0.0028339239000000002 +5 0.42295359 2.3386822e-07 1.2761582e-09 -0.0012012689 -0.49791148 1474623.0 1.0913688999999998 -0.021504508 2243.2631 +6 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tools/myTools/bin/sfa/data/schliemann_2011/exp_data/exp_4h_SS_TNF=200.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/schliemann_2011/exp_data/exp_4h_SS_TNF=200.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,37 @@ + IKKa NF-kB A20 FLIP XIAP TNFRC2:FLIP:pCasp8 Casp3 Casp6 Casp8 +1 -0.3198733 -0.000136467 -1.5421046000000002e-06 -3.2228011000000005e-06 -1.0994183e-07 -3.0020164999999995e-06 -3.7582412e-05 2.1241195e-08 -8.6817826e-09 +2 0.00016414417000000002 -0.00062667927 -0.0010545538 -0.0040043210999999995 -0.00017674352999999998 -0.0052398913 -0.14387363 0.0070188535 0.0096683545 +3 0.056362301 2.4530536000000004e-05 -0.31893849 4.3147480000000003e-07 1.4693234e-08 0.055817966 -0.00042579589000000005 -1.2863569e-07 0.0031735603000000003 +4 -3.6597741e-08 -2.0485888e-11 2.3519629e-12 -0.31862383 1.4005653e-07 -0.30489075 0.029594743 -0.00044596992999999997 -0.0027930446000000004 +5 0.0025334548 1.1461805e-06 2.0461398999999995e-08 -0.0021442208 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tools/myTools/bin/sfa/data/schliemann_2011/exp_data/exp_8h_SS_TNF=0d02.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/schliemann_2011/exp_data/exp_8h_SS_TNF=0d02.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,37 @@ + IKKa NF-kB A20 FLIP XIAP TNFRC2:FLIP:pCasp8 Casp3 Casp6 Casp8 +1 -0.15892948 -5.9431117000000005e-08 -1.2896807e-09 -3.3651315e-09 -1.2536503000000002e-10 -2.912719e-09 8.412119300000001e-07 -5.8465454000000005e-12 9.5804992e-12 +2 0.0001207776 0.00052787533 -0.00052641227 -0.002210437 -0.00011199502 -0.0043698026 -0.49452802 0.0017250993 0.0029907502 +3 0.038046402 1.4462505e-08 -0.15860879 6.7218166e-10 9.4439418e-11 0.049556530999999994 0.0014883447 -9.7074134e-08 3.2036419e-06 +4 -5.870858e-07 -2.4363211000000003e-13 -3.4560447999999997e-16 -0.15850362 -3.1313406e-07 -0.14119586 0.28216217 0.00059630342 0.0010591729 +5 11.714288 4.3757019e-06 9.624828799999999e-08 -0.0027241784999999996 -0.15876138 965565.11 0.19986744 0.21970531 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0.0030892653000000004 +26 0.038050056 1.4432379e-05 -0.15860756 6.663786999999999e-07 2.4965319999999998e-08 0.04982431 -4.8957606e-05 6.938337699999999e-07 0.0030892653000000004 +27 -5.0118868e-07 -2.6472528e-10 3.0830959e-11 -0.15850118 1.1826214e-08 -0.13731668 -0.31129103 -2.861927e-05 -0.00040949267999999995 +28 -5.0118868e-07 -2.6472528e-10 3.0830959e-11 -0.15850118 1.1826214e-08 -0.13731668 -0.31129103 -2.861927e-05 -0.00040949267999999995 +29 -5.0118868e-07 -2.6472528e-10 3.0830959e-11 -0.15850118 1.1826214e-08 -0.13731668 -0.31129103 -2.861927e-05 -0.00040949267999999995 +30 -5.0118868e-07 -2.6472528e-10 3.0830959e-11 -0.15850118 1.1826214e-08 -0.13731668 -0.31129103 -2.861927e-05 -0.00040949267999999995 +31 0.011754918 4.3791437e-06 9.7848398e-08 -0.0027342109000000002 -0.15876044 974.59959 0.53906455 0.21668356 223.40523 +32 0.011754918 4.3791437e-06 9.7848398e-08 -0.0027342109000000002 -0.15876044 974.59959 0.53906455 0.21668356 223.40523 +33 0.011754918 4.3791437e-06 9.7848398e-08 -0.0027342109000000002 -0.15876044 974.59959 0.53906455 0.21668356 223.40523 +34 1.7589126999999997e-07 -1.8591273e-09 1.1014708e-09 1.0228786999999999e-07 1.3664726999999998e-06 0.0055732247 -0.13474139 -0.0024956106 -0.0026737221000000004 +35 1.7589126999999997e-07 -1.8591273e-09 1.1014708e-09 1.0228786999999999e-07 1.3664726999999998e-06 0.0055732247 -0.13474139 -0.0024956106 -0.0026737221000000004 +36 1.1681162000000001e-08 4.8125690000000006e-12 3.5136243e-14 1.2851576e-08 1.6279954e-07 1.6919113e-08 -0.55991748 -0.4049006 -0.43599295 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/schliemann_2011/exp_data/exp_8h_SS_TNF=200.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/schliemann_2011/exp_data/exp_8h_SS_TNF=200.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,37 @@ + IKKa NF-kB A20 FLIP XIAP TNFRC2:FLIP:pCasp8 Casp3 Casp6 Casp8 +1 -0.15892752 -0.00058154504 -1.2730221e-05 -3.3241243e-05 -1.2391282e-06 -2.8785761e-05 0.0013750872 1.3201580999999998e-07 -3.2504579e-07 +2 0.00012101841 0.00049894083 -0.00052704907 -0.0022118851000000003 -0.00011235680000000001 -0.001719056 -0.03080778 0.0023156095000000003 0.0025044544 +3 0.038082757 0.00014165633 -0.15859659 6.5932589000000005e-06 2.4673185999999996e-07 0.049909887 -0.0088436673 7.4483384e-06 0.019960141 +4 -4.9383443e-07 -2.0095644e-09 -1.4264779e-11 -0.1584802 7.6710478e-08 -0.1369038 -0.05898383 -0.00014460133999999998 -0.0017676287 +5 0.001200294 4.3818472e-06 1.0172736e-07 -0.0027812155 -0.15876399 99.625946 -1.0 0.22603416 148.17574 +6 5.5244738e-08 1.1149029e-10 5.6635544e-11 3.3935633000000006e-08 5.361187800000001e-07 0.0004441266 -1.0 -0.0010229155999999999 -0.00043254904999999997 +7 1.1731011e-08 4.7431436e-11 3.7242417e-13 1.3373888e-08 1.7096745000000002e-07 1.7331107e-08 -0.39733719 -0.40518254 -0.28476448 +8 2.6967992999999998e-08 -2.315943e-10 1.8237714e-10 1.1753403e-07 8.928748e-07 -0.014617679 0.021752473999999997 -0.0015662781 -0.30075163 +9 -0.15892752 -0.00058154504 -1.2730221e-05 -3.3241243e-05 -1.2391282e-06 -2.8785761e-05 0.0013750872 1.3201580999999998e-07 -3.2504579e-07 +10 -0.15892752 -0.00058154504 -1.2730221e-05 -3.3241243e-05 -1.2391282e-06 -2.8785761e-05 0.0013750872 1.3201580999999998e-07 -3.2504579e-07 +11 -0.15892752 -0.00058154504 -1.2730221e-05 -3.3241243e-05 -1.2391282e-06 -2.8785761e-05 0.0013750872 1.3201580999999998e-07 -3.2504579e-07 +12 -0.15892752 -0.00058154504 -1.2730221e-05 -3.3241243e-05 -1.2391282e-06 -2.8785761e-05 0.0013750872 1.3201580999999998e-07 -3.2504579e-07 +13 -0.15892752 -0.00058154504 -1.2730221e-05 -3.3241243e-05 -1.2391282e-06 -2.8785761e-05 0.0013750872 1.3201580999999998e-07 -3.2504579e-07 +14 -0.15892752 -0.00058154504 -1.2730221e-05 -3.3241243e-05 -1.2391282e-06 -2.8785761e-05 0.0013750872 1.3201580999999998e-07 -3.2504579e-07 +15 -0.15892752 -0.00058154504 -1.2730221e-05 -3.3241243e-05 -1.2391282e-06 -2.8785761e-05 0.0013750872 1.3201580999999998e-07 -3.2504579e-07 +16 0.00012101841 0.00049894083 -0.00052704907 -0.0022118851000000003 -0.00011235680000000001 -0.001719056 -0.03080778 0.0023156095000000003 0.0025044544 +17 0.00012101841 0.00049894083 -0.00052704907 -0.0022118851000000003 -0.00011235680000000001 -0.001719056 -0.03080778 0.0023156095000000003 0.0025044544 +18 0.00012101841 0.00049894083 -0.00052704907 -0.0022118851000000003 -0.00011235680000000001 -0.001719056 -0.03080778 0.0023156095000000003 0.0025044544 +19 0.00012101841 0.00049894083 -0.00052704907 -0.0022118851000000003 -0.00011235680000000001 -0.001719056 -0.03080778 0.0023156095000000003 0.0025044544 +20 0.00012101841 0.00049894083 -0.00052704907 -0.0022118851000000003 -0.00011235680000000001 -0.001719056 -0.03080778 0.0023156095000000003 0.0025044544 +21 0.00012101841 0.00049894083 -0.00052704907 -0.0022118851000000003 -0.00011235680000000001 -0.001719056 -0.03080778 0.0023156095000000003 0.0025044544 +22 0.038082757 0.00014165633 -0.15859659 6.5932589000000005e-06 2.4673185999999996e-07 0.049909887 -0.0088436673 7.4483384e-06 0.019960141 +23 0.038082757 0.00014165633 -0.15859659 6.5932589000000005e-06 2.4673185999999996e-07 0.049909887 -0.0088436673 7.4483384e-06 0.019960141 +24 0.038082757 0.00014165633 -0.15859659 6.5932589000000005e-06 2.4673185999999996e-07 0.049909887 -0.0088436673 7.4483384e-06 0.019960141 +25 0.038082757 0.00014165633 -0.15859659 6.5932589000000005e-06 2.4673185999999996e-07 0.049909887 -0.0088436673 7.4483384e-06 0.019960141 +26 0.038082757 0.00014165633 -0.15859659 6.5932589000000005e-06 2.4673185999999996e-07 0.049909887 -0.0088436673 7.4483384e-06 0.019960141 +27 -4.9383443e-07 -2.0095644e-09 -1.4264779e-11 -0.1584802 7.6710478e-08 -0.1369038 -0.05898383 -0.00014460133999999998 -0.0017676287 +28 -4.9383443e-07 -2.0095644e-09 -1.4264779e-11 -0.1584802 7.6710478e-08 -0.1369038 -0.05898383 -0.00014460133999999998 -0.0017676287 +29 -4.9383443e-07 -2.0095644e-09 -1.4264779e-11 -0.1584802 7.6710478e-08 -0.1369038 -0.05898383 -0.00014460133999999998 -0.0017676287 +30 -4.9383443e-07 -2.0095644e-09 -1.4264779e-11 -0.1584802 7.6710478e-08 -0.1369038 -0.05898383 -0.00014460133999999998 -0.0017676287 +31 0.001200294 4.3818472e-06 1.0172736e-07 -0.0027812155 -0.15876399 99.625946 -1.0 0.22603416 148.17574 +32 0.001200294 4.3818472e-06 1.0172736e-07 -0.0027812155 -0.15876399 99.625946 -1.0 0.22603416 148.17574 +33 0.001200294 4.3818472e-06 1.0172736e-07 -0.0027812155 -0.15876399 99.625946 -1.0 0.22603416 148.17574 +34 5.5244738e-08 1.1149029e-10 5.6635544e-11 3.3935633000000006e-08 5.361187800000001e-07 0.0004441266 -1.0 -0.0010229155999999999 -0.00043254904999999997 +35 5.5244738e-08 1.1149029e-10 5.6635544e-11 3.3935633000000006e-08 5.361187800000001e-07 0.0004441266 -1.0 -0.0010229155999999999 -0.00043254904999999997 +36 1.1731011e-08 4.7431436e-11 3.7242417e-13 1.3373888e-08 1.7096745000000002e-07 1.7331107e-08 -0.39733719 -0.40518254 -0.28476448 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/schliemann_2011/network.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/schliemann_2011/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,30 @@ +TNF + TNFR +TNFR + TNFRC1 +TNFRC1 + IKKa +TNFRC1 + TNFRC2 +IKKa + NF-kB +NF-kB + A20 +NF-kB + FLIP +NF-kB + XIAP +NF-kB + IkBa +IkBa - NF-kB +A20 - TNFRC1 +TNFRC2 + TNFRC2:FLIP +TNFRC2 + TNFRC2:pCasp8 +TNFRC2:FLIP + TNFRC2:FLIP:pCasp8 +TNFRC2:pCasp8 + TNFRC2:FLIP:pCasp8 +TNFRC2:FLIP:pCasp8 + IKKa +FLIP + TNFRC2:FLIP +FLIP + TNFRC2:FLIP:pCasp8 +pCasp8 + Casp8 +pCasp8 + TNFRC2:pCasp8 +pCasp8 + TNFRC2:FLIP:pCasp8 +TNFRC2:pCasp8 + Casp8 +Casp8 + Casp3 +Casp3 - TNFRC1 +Casp3 + Casp6 +Casp3 - FLIP +Casp3 - XIAP +XIAP - Casp3 +Casp6 + Casp8 +Casp6 - pCasp8 \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/schliemann_2011/ptb.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/schliemann_2011/ptb.tsv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,9 @@ +Target Type Value Comment +IKKa node -1 +NF-kB node -1 +A20 node -1 +FLIP node -1 +XIAP node -1 +Casp3 node -1 +Casp6 node -1 +pCasp8 node -1 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/steinway_2015/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/steinway_2015/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,61 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +[1] Steinway et al. + Combinatorial interventions inhibit TGFβ-driven epithelial-to-mesenchymal + transition and support hybrid cellular phenotypes. + Npj Systems Biology and Applications (2015) 1(1), 15014. + + +[2] Steinway, S. N. et al. + Network modeling of TGFβ signaling in hepatocellular carcinoma + epithelial-to-mesenchymal transition reveals joint sonic hedgehog + and Wnt pathway activation. + Cancer Research (2014) 74(21), 5963–77. + +[Information] +The topology of network is curated from reference [1]. + +""" + +import os + +import pandas as pd + +import sfa +import sfa.base + + +def create_data(): + return SteinwayData() + + +class SteinwayData(sfa.base.Data): + + def __init__(self): + + self._abbr = "steinway_2015" + self._name = "Steinway et al. Npj Syst Biol Appl (2015) 1(1), 15014" + inputs = {} + inputs['TGFβ'] = 1.0 + + dpath = os.path.dirname(__file__) + fpath_network = os.path.join(dpath, 'network.sif') + A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True) + self._A = A + self._n2i = n2i + self._i2n = {idx: name for name, idx in n2i.items()} + self._dg = dg + self._inputs = inputs + + # The following members are not defined due to the lack of data. + self._df_conds = None + self._df_exp = None + self._df_ptb = None + self._has_link_perturb = False + self._names_ptb = None + self._iadj_to_idf = None + # end of def __init__ +# end of def class + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/steinway_2015/network.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/steinway_2015/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,134 @@ +Wnt + Frizzled +GLI + SHH +SMAD + SHH +SHH - PTCH +PTCH - SMO +SMO + FUS +FUS - SUFU +SUFU - GLI +SUFU - β-Catenin_nuc +GLI + TGFβ +GLI + Wnt +GLI + SNAI1 +TCF/LEF + GLI +Frizzled + DSH +DSH - GSK3β +GSK3β - RAS +Csn - GSK3β +GSK3β - SNAI1 +AKT - GSK3β +GSK3β + Dest_compl +Dest_compl - GSK3β +ERK - GSK3β +AXIN2 + Dest_compl +Dest_compl - β-Catenin_nuc +β-Catenin_nuc + Dest_compl +β-Catenin_nuc + TCF/LEF +β-Catenin_nuc + SNAI2 +E-Cadherin - β-Catenin_nuc +β-Catenin_nuc - β-Catenin_memb +β-Catenin_memb - β-Catenin_nuc +β-Catenin_memb + E-Cadherin +E-Cadherin + β-Catenin_memb +HGF + cMet +cMet + SOS/GRB2 +cMet + SRC +CD44 + cMet +TCF/LEF + CD44 +TCF/LEF + Jagged +TCF/LEF + RAS +TCF/LEF + AXIN2 +TCF/LEF + TWIST1 +AXIN2 + AXIN2 +SNAI2 + SNAI2 +ERK + SNAI2 +TWIST1 + SNAI2 +SNAI2 - E-Cadherin +PDGF + PDGFR +PDGFR + SOS/GRB2 +PDGFR + SRC +IGF1R + SRC +EGFR + SRC +FGFR + SRC +SRC + RAS +SRC + STAT +STAT + LIV1 +LIV1 + SNAI1 +Csn - βTrCP +NfκB + Csn +βTrCP - SNAI1 +IGF1 + IGF1R +IGF1R + SOS/GRB2 +RAS + DELTA +SOS/GRB2 + RAS +RAS + RAF +RAS + PI3K +PI3K + AKT +RKIP - MEK +ERK - RKIP +ILK + AKT +AKT + IKKα +RAF + MEK +MEK + ERK +ERK - SOS/GRB2 +ERK + SMAD +ERK + c-fos +SNAI1 - RKIP +EGR1 + SNAI1 +SNAI1 + TGFβ +CSL + SNAI1 +LOXL23 + SNAI1 +Goosecoid + SNAI1 +PAK1 + SNAI1 +SMAD + SNAI1 +SNAI1 + TWIST1 +SNAI1 + ZEB1 +SNAI1 - miR200 +SNAI1 + ZEB2 +SNAI1 + FOXC2 +SNAI1 - E-Cadherin +TWIST1 + FOXC2 +Goosecoid + FOXC2 +FOXC2 - E-Cadherin +HEY1 - E-Cadherin +TWIST1 - E-Cadherin +ZEB1 - E-Cadherin +ZEB2 - E-Cadherin +E-Cadherin - EMT +EGF + EGFR +EGFR + SOS/GRB2 +FGF + FGFR +FGFR + SOS/GRB2 +TGFβR + SOS/GRB2 +SMAD + ILK +IKKα + NfκB +NfκB + TWIST1 +c-fos + EGR1 +Jagged + NOTCH +SMAD + Jagged +DELTA + NOTCH +NOTCH + NOTCH_ic +CHD1L + CDC42 +NOTCH_ic + CSL +CSL + HEY1 +SMAD + HEY1 +TGFβR + CDC42 +CDC42 + PAK1 +HIF1α + LOXL23 +Goosecoid + TWIST1 +HIF1α + TWIST1 +TWIST1 + TGFβ +miR200 - ZEB1 +ZEB1 - miR200 +ZEB2 - miR200 +miR200 - ZEB2 +HIF1α + ZEB2 +Goosecoid + ZEB2 +ZEB2 - SMAD +TGFβ + TGFβR +Goosecoid + TGFβ +TGFβR + SMAD +ZEB1 + SMAD +HIF1α + ZEB1 +Goosecoid + ZEB1 +Hypoxia + HIF1α diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/turei_2016/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/turei_2016/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,47 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Türei, D., Korcsmáros, T., & Saez-Rodriguez, J. +OmniPath: Guidelines and gateway for +literature-curated signaling pathway resources. +Nature Methods, (2016) 13(12), 966–967. +""" + +import os + +import sfa +import sfa.base + + +def create_data(): + return TureiData() + + +class TureiData(sfa.base.Data): + + def __init__(self): + + self._abbr = "turei_2016" + self._name = "Türei et al. " \ + "Nature Methods, (2016) 13(12), 966–967" + inputs = {} + + dpath = os.path.dirname(__file__) + fpath_network = os.path.join(dpath, 'network_giant_component.sif') + A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True) + self._A = A + self._n2i = n2i + self._i2n = {idx: name for name, idx in n2i.items()} + self._dg = dg + self._inputs = inputs + + # The following members are not defined due to the lack of data. + self._df_conds = None + self._df_exp = None + self._df_ptb = None + self._has_link_perturb = False + self._names_ptb = None + self._iadj_to_idf = None + # end of def __init__ +# end of def class \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/turei_2016/network.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/turei_2016/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,15512 @@ +Q13616 - P35222 +O14818 + Q13616 +Q13616 + Q01094 +Q86VP6 - Q13616 +Q13616 - P84022 +P62877 + Q13616 +P61081 + Q13616 +Q13616 - P25963 +Q13616 - P24941 +Q13616 - P38936 +P63208 + Q13616 +P01106 + Q13616 +P25789 + Q13616 +P60900 + Q13616 +P28070 + Q13616 +P25787 + Q13616 +Q04323 + Q13616 +O60500 + Q9Y210 +P17252 + O60500 +O60500 + P06241 +P06241 - O60500 +P17252 + Q14432 +P17612 + Q14432 +Q5W0U4 - Q9NQA5 +Q01970 - Q96QT4 +P17252 - Q01970 +P17612 - Q01970 +Q15599 + Q01970 +Q16555 + P07437 +P07437 + P48995 +P07437 - P84022 +Q14980 + P07437 +P07437 - Q15796 +P43403 + P07437 +P62942 - P42345 +P36897 - P62942 +P62942 - P01137 +P62942 - O15105 +P62942 - Q9HCE7 +P10275 + P37088 +Q96PU5 - P37088 +Q13190 - Q13563 +Q13563 + Q13507 +Q13563 + Q9HCX4 +P98161 + Q13563 +Q13563 + Q9HBA0 +Q9HBA0 + Q13563 +P14314 - Q13563 +P48995 + Q13563 +Q13563 + P48995 +Q9Y496 + Q13563 +P68400 + Q13563 +P63244 - Q13224 +Q13224 + Q05586 +P06241 + Q13224 +P68400 - Q13224 +Q5VU43 + P13861 +P17252 - Q01844 +Q15637 - Q01844 +P09234 - Q01844 +Q16620 + P06241 +Q16620 + P19174 +P20783 + Q16620 +Q13562 + Q16620 +Q16620 + P29353 +Q16620 + Q9Y4H2 +P23560 + Q16620 +Q16620 + P35568 +Q13501 + Q16620 +Q16620 + O14492 +Q16620 + O43639 +Q16620 + Q9NRF2 +Q16620 + Q92529 +P08138 - 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Q13351 +Q01543 + P10415 +P11488 - Q08828 +Q9Y6C5 + Q99835 +Q969D9 + Q9HC73 +Q969D9 + P16871 +Q969D9 + P42229 +Q9HC73 + O60674 +Q9HC73 + P42229 +P31785 - P14784 +P31785 - P60568 +P31785 + P52333 +P31785 + P62993 +P31785 + P42224 +P31785 + P29353 +P31785 + P23458 +P31785 + O60674 +P31785 + P42229 +Q01113 + P52333 +Q01113 + P40763 +Q01113 + P23458 +Q01113 + P42229 +P40238 + P29597 +P40238 + P29353 +P40238 + O60674 +P40238 + P42229 +O95376 - Q99684 +Q13261 + P14784 +Q13261 + P40763 +Q13261 + P42229 +P10828 + P28702 +P10828 + P19793 +P10828 + P37231 +P10828 + P42229 +Q92786 - P41235 +Q92786 - Q92858 +Q92786 - Q99684 +Q15742 - P18146 +P29597 + P51452 +P29597 + Q5VWK5 +P29597 + P51692 +P29597 + P40763 +P29597 + P61978 +P29597 + P27986 +P29597 + Q06124 +P29597 + P19525 +P29597 + P52630 +P29597 + P14317 +P29597 + P06748 +P29597 + P17181 +P29597 + P42224 +P29597 + P42229 +Q13506 - P18146 +P16471 + O60674 +P16471 + P42229 +P24394 + P31785 +P24394 + P78552 +P24394 + P52333 +P24394 + P35568 +P24394 + Q9Y4H2 +P24394 + P29353 +P24394 + P42226 +P24394 + P23458 +P24394 + O60674 +P24394 + P42229 +Q01344 + P23458 +Q01344 + O60674 +Q01344 + P42229 +P15509 + O60674 +P15509 + P42229 +P26951 + P62993 +P26951 + P23458 +P26951 + O60674 +P26951 + P42229 +O43781 + Q9Y3T9 +O43781 + Q9UHJ3 +O43781 + Q96I25 +O43781 - Q96B36 +O43781 + Q9NYB0 +O43781 + P14317 +O43781 + P23025 +O43781 + P18858 +O43683 + Q9Y5B6 +O43683 + Q9Y3T9 +O43683 + Q8N2Z9 +O43683 + Q9H814 +O43683 + Q9Y4W2 +O43683 + Q8NFH5 +O43683 + Q12972 +O43683 + O15151 +O43683 + Q8WUF5 +O43683 + Q00987 +O43683 + P06493 +O43683 + P19338 +O43683 + P53350 +O43683 + P51532 +O43683 + Q14738 +O43683 + P18887 +O43683 + P26651 +O43683 + Q14683 +O43683 + P18858 +Q96AP0 + O14746 +Q96AP0 + Q9BSI4 +Q12967 + Q92963 +Q12967 + P62070 +Q12967 + Q13671 +Q12967 + P11233 +Q9Y5S8 + Q86UR1 +O15503 + Q12770 +O15503 - Q8NC51 +Q969H0 - Q16204 +Q969H0 - Q99466 +Q969H0 + P10242 +Q969H0 - Q9UBK2 +Q969H0 - Q9Y6Q9 +Q969H0 - P05412 +Q969H0 - O14965 +Q969H0 - Q9H3D4 +Q969H0 - Q8NC51 +O14511 + Q15303 +O14511 + P21860 +P01374 + Q92956 +P01374 + P19438 +P01374 + P20333 +P01374 + P36941 +Q06643 + P36941 +Q92730 + P45983 +Q13202 - Q16539 +Q13202 - P45983 +O95989 + P31751 +Q16829 - P10912 +Q16829 - Q16539 +Q16829 - P28482 +Q16829 - P45983 +Q9H1R3 + Q06413 +Q9H1R3 + Q9Y4K4 +Q9H1R3 + P30154 +Q9H1R3 + P45983 +O95163 + P45983 +Q9BY84 + Q13387 +Q9BY84 - Q15759 +Q9BY84 - P53779 +Q9BY84 - Q16539 +Q9BY84 - P45984 +Q9BY84 - P28482 +Q9BY84 + Q9UQF2 +Q9BY84 - P45983 +Q13387 + O14733 +Q13387 + P53779 +Q13387 + O15264 +Q13387 + Q16539 +Q13387 + P45984 +Q13387 + Q9UQF2 +Q13387 + P45983 +Q9UPT6 + Q16584 +Q9UPT6 + O14733 +Q9UPT6 + P45985 +Q9UPT6 + P53779 +Q9UPT6 + P45984 +Q9UPT6 + Q13233 +Q9UPT6 + P45983 +Q12904 + Q9HAU4 +P19419 + P01100 +Q8WYK2 - P15336 +Q8WYK2 - P05412 +P28324 + P01100 +P36896 + Q15796 +P36896 + P84022 +P36896 + Q13485 +P36896 + P45983 +Q99956 - Q16539 +Q99956 - P45984 +Q99956 - P45983 +Q9H0E2 + Q9Y4K3 +Q92993 - Q01130 +Q92993 + Q9BZS1 +Q92993 + P68431 +Q92993 + P01106 +Q92993 - P40763 +Q92993 + P62805 +Q92993 + P10275 +Q92993 - P16220 +Q92993 + Q13315 +Q92993 + O75385 +Q92993 + P0C0S5 +Q92993 + P78527 +Q92993 + P15172 +Q92993 + P05067 +Q92993 + P04637 +Q86TM6 + O15354 +P30305 + P06493 +P04632 + P17655 +Q96LC9 - Q07820 +Q96LC9 - Q07817 +Q96LC9 - P10415 +Q8TDB6 + P62805 +O75460 + P17861 +O75460 + Q12933 +O75460 + P45983 +Q56P42 - Q9ULZ3 +P16104 + O60934 +P16104 + Q13535 +P16104 + Q8NEM0 +P16104 + Q14676 +P16104 + Q92830 +P16104 + Q12888 +Q99575 - Q9ULZ3 +Q9UFF9 - Q9ULZ3 +Q13115 - Q16539 +Q13115 - P45984 +Q9BZR9 - O15524 +P61604 - P10809 +Q99640 - P06493 +P28562 - P53778 +P28562 - Q9NVI1 +P28562 - Q15759 +P28562 - P53779 +P28562 - P45984 +Q9P035 - P63000 +Q7L576 + Q6T4R5 +Q7Z6J0 + Q12852 +Q7Z6J0 + Q02779 +Q7Z6J0 + Q16584 +Q7Z6J0 + O14733 +Q7Z6J0 + P45985 +Q7Z6J0 + P15153 +Q7Z6J0 + Q9UQF2 +Q7Z6J0 + P45983 +O43312 + Q9UMX1 +O43312 + P08151 +O43312 + P63000 +Q16082 + Q9Y243 +Q16082 + P31749 +Q16082 - P42574 +Q16082 - P45983 +Q13233 + P09488 +Q13233 + Q6UUV9 +Q13233 + P54764 +Q13233 + P52564 +Q13233 + P27361 +Q13233 + O14733 +Q13233 + O15111 +Q13233 - Q13158 +Q13233 + P40763 +Q13233 + P45985 +Q13233 + Q15796 +Q13233 + O14920 +Q13233 + Q02750 +Q13233 + Q99558 +Q13233 + P28482 +Q13233 + P45983 +P31260 + Q12778 +O75469 - Q15466 +O75469 + P41235 +O75469 + P19793 +O75469 + Q12778 +P17676 + P10147 +P17676 + P40763 +P17676 + Q13950 +P17676 + P02763 +P17676 + Q16254 +P17676 + P18428 +P17676 + P07900 +P17676 + Q12778 +P06401 + P27361 +P06401 + P24941 +P06401 + P28482 +P06401 + Q12778 +Q16828 - Q16659 +Q16828 + Q12778 +O43521 - Q92843 +O43521 + P21796 +O43521 + Q07812 +O43521 - Q07820 +O43521 - Q16548 +O43521 - Q07817 +O43521 - P10415 +O43521 + Q16611 +Q49AN0 - O00327 +Q49AN0 - O15516 +O43474 - P11831 +P08833 + P05019 +Q9Y2E6 - Q9UHD2 +Q96CV9 + Q92664 +P12980 - P15923 +Q9H2T7 + P15923 +O60682 - P15923 +Q02779 + P78352 +Q02779 + O14733 +Q02779 + P45985 +Q02779 + Q92630 +Q02779 - P15923 +P17542 - P15923 +P49137 + P33241 +P49137 + Q9NY61 +P49137 + P07101 +P49137 + O15511 +P49137 - P30305 +P49137 - Q92574 +P49137 + Q00987 +P49137 + P08670 +P49137 + P11831 +P49137 + P16220 +P49137 + P53350 +P49137 + Q9UKV8 +P49137 + O75925 +P49137 - Q07352 +P49137 + Q13151 +P49137 + P53667 +P49137 - P30307 +P49137 + Q00613 +P49137 - P30304 +P49137 - O95453 +P49137 + Q16539 +P49137 + Q16082 +P49137 + Q15717 +P49137 + O95816 +P49137 + P15923 +Q02535 - Q92858 +Q02535 - P15923 +P15923 + P15172 +P32780 + Q13888 +P32780 + P19447 +P32780 + P18074 +P32780 + Q01831 +P32780 + P24928 +P32780 + Q92889 +Q8IY92 + Q92889 +P23025 + P32780 +P23025 + P15927 +P23025 + P07992 +P23025 + Q92466 +P23025 + Q9UPV0 +P23025 + P27694 +P23025 + Q92889 +Q99743 + O15055 +Q99743 + O15534 +Q09013 + P26678 +Q09013 + Q15942 +Q09013 + O43294 +Q09013 + O14974 +Q01664 + Q12857 +Q01664 + Q13547 +Q01664 + P08047 +P00748 + P03952 +P01042 - P08311 +P03952 + P00748 +P03952 + P01042 +P03952 + P54619 +Q96P09 - P55211 +Q13075 - P42574 +Q13075 - P55211 +P99999 + O14727 +P99999 + Q07812 +P99999 + P55211 +Q96CA5 - Q9NR28 +Q96CA5 - P55210 +Q96CA5 - P42574 +Q96CA5 - P55211 +Q07817 - P21796 +Q07817 - Q07812 +Q07817 - P99999 +Q07817 - Q13794 +Q07817 - O43521 +Q07817 - Q9BZR8 +Q07817 + P10415 +Q07817 - Q9C000 +Q07817 + Q12983 +Q07817 - Q16611 +Q07817 - P04637 +Q07817 - P55957 +Q07817 - P55211 +Q9NR09 - O43464 +Q9NR09 - Q9NR28 +Q9NR09 - P55210 +Q9NR09 - P42574 +Q9NR09 - P55211 +O75143 + Q8TDY2 +O75143 + O75385 +O75143 + Q8IYT8 +Q9UJX4 + Q9UJX5 +Q9UJX4 + Q9UM13 +Q9UJX4 + Q13042 +Q9UJX4 + Q9UJX6 +Q9UJX4 + Q9UJX2 +Q9UJX4 + Q9H1A4 +Q9UJX4 + P30260 +Q9UJX4 + Q9UM11 +Q9UJX4 + Q9UJX3 +Q8NHZ8 + Q9UJX5 +Q8NHZ8 + Q13042 +Q8NHZ8 + Q9UJX6 +Q8NHZ8 + Q9UJX2 +Q8NHZ8 + Q9H1A4 +Q8NHZ8 + P30260 +Q8NHZ8 + Q9UM11 +Q8TDY2 - Q14289 +Q8TDY2 - Q92574 +Q8TDY2 + Q676U5 +Q8TDY2 + O75385 +Q8TDY2 + P04637 +Q8TDY2 + Q8IYT8 +Q9UM13 + Q9UJX4 +Q9UM13 + Q9UJX5 +Q9UM13 + Q13042 +Q9UM13 + Q9UJX6 +Q9UM13 + Q9UJX2 +Q9UM13 + Q9H1A4 +Q9UM13 + P30260 +Q9UM13 + Q9UM11 +Q9UM13 + Q9NYG5 +Q9UM13 + Q9UJX3 +Q9UM13 - P06493 +Q4G163 - Q12834 +Q9UJX2 + Q9BS18 +Q9UJX2 + Q9UJX4 +Q9UJX2 + Q9UJX5 +Q9UJX2 + Q8NHZ8 +Q9UJX2 + Q9UM13 +Q9UJX2 + Q13042 +Q9UJX2 + Q9UJX6 +Q9UJX2 + Q9H1A4 +Q9UJX2 + P30260 +Q9UJX2 + Q9UM11 +Q9UJX2 + Q9UJX3 +Q9UJX2 - P06493 +O60216 - P49711 +Q7Z5K2 - O60216 +O94761 + P27695 +O94761 + P39748 +O94761 + P06746 +P20941 - O43186 +Q9NS56 + P63165 +Q9NS56 - Q99801 +P62324 + P17482 +P62324 + Q99873 +P0DN86 + P22888 +P01229 + P22888 +Q8TEW0 + Q9BX67 +Q8TEW0 + P57087 +Q8TEW0 + Q9Y624 +P19338 + P68871 +P19338 - P10243 +P19338 - Q00987 +P19338 - P10242 +P19338 + P27694 +Q03060 - Q9Y2W7 +Q03060 - P16220 +Q03060 + P23443 +Q14449 + Q05513 diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/zanudo_2015a/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/zanudo_2015a/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,49 @@ +# -*- coding: utf-8 -*- + +""" +[Reference] +Zañudo, J. G. T., & Albert, R. +Cell Fate Reprogramming by Control of Intracellular Network Dynamics. +PLoS Computational Biology, (2015) 11(4), e1004193. + +""" + +import os + +import pandas as pd + +import sfa +import sfa.base + + +def create_data(): + return ZanudoAData() + + +class ZanudoAData(sfa.base.Data): + + def __init__(self): + + self._abbr = "zanudo_2015a" + self._name = "Zañudo et al. PLoS Computational Biology, 11(4), e1004193" + inputs = {} + + dpath = os.path.dirname(__file__) + fpath_network = os.path.join(dpath, 'network.sif') + A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True) + self._A = A + self._n2i = n2i + self._i2n = {idx: name for name, idx in n2i.items()} + self._dg = dg + self._inputs = inputs + + # The following members are not defined due to the lack of data. + self._df_conds = None + self._df_exp = None + self._df_ptb = None + self._has_link_perturb = False + self._names_ptb = None + self._iadj_to_idf = None + # end of def __init__ +# end of def class + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/data/zanudo_2015a/network.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/data/zanudo_2015a/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,142 @@ +TCR + CTLA4 +Stimuli + TCR +CTLA4 - TCR +S1P + PDGFR +PDGF + PDGFR +TCR + FYN +IL2RB + FYN +FYN + CytoskeletonSignaling +CD45 + LCK +TCR + LCK +IL2RB + LCK +ZAP70 - LCK +LCK + ZAP70 +FYN - ZAP70 +IL2RB + GRB2 +ZAP70 + GRB2 +GRB2 + PLCG1 +PDGFR + PLCG1 +GRB2 + RAS +PLCG1 + RAS +GAP - RAS +RAS + GAP +PDGFR + GAP +GAP + GAP +IL15 - GAP +IL2 - GAP +RAS + MEK +PDGFR + PI3K +RAS + PI3K +TPL2 + NFKB +PI3K + NFKB +FLIP + NFKB +TRADD + NFKB +IAP + NFKB +PI3K + NFAT +NFKB + RANTES +NFKB + IL2 +STAT3 + IL2 +NFAT + IL2 +TBET - IL2 +ERK + IL2RBT +TBET + IL2RBT +IL2RBT + IL2RB +IL2 + IL2RB +IL15 + IL2RB +IL2 + IL2RAT +STAT3 + IL2RAT +NFKB + IL2RAT +IL2 + IL2RA +IL2RAT + IL2RA +IL2RA - IL2RA +IL2RA + JAK +IL2RB + JAK +RANTES + JAK +IFNG + JAK +SOCS - JAK +CD45 - JAK +JAK + SOCS +IL2 - SOCS +IL15 - SOCS +JAK + STAT3 +STAT3 + P27 +STAT3 + Proliferation +P27 - Proliferation +JAK + TBET +TBET + TBET +ERK + CREB +IFNG + CREB +TBET + IFNGT +STAT3 + IFNGT +NFAT + IFNGT +IL2 + IFNG +IL15 + IFNG +Stimuli + IFNG +IFNGT + IFNG +SMAD - IFNG +P2 - IFNG +IFNG + P2 +P2 + P2 +Stimuli2 - P2 +CREB + GZMB +IFNG + GZMB +TBET + GZMB +TAX + TPL2 +PI3K + TPL2 +TNF + TPL2 +NFKB + TNF +TNF + TRADD +IAP - TRADD +A20 - TRADD +STAT3 + FasL +NFKB + FasL +NFAT + FasL +ERK + FasL +NFKB + FasT +FasT + Fas +FasL + Fas +sFas - Fas +FasT + sFas +S1P + sFas +Apoptosis - sFas +Fas + Ceramide +S1P - Ceramide +FasT + DISC +Fas + DISC +IL2 + DISC +Ceramide + DISC +FLIP - DISC +TRADD + Caspase +GZMB + Caspase +BID + Caspase +IAP - Caspase +DISC + Caspase +NFKB + FLIP +CREB + FLIP +IFNG + FLIP +DISC - FLIP +NFKB + A20 +Caspase + BID +GZMB + BID +BclxL - BID +MCL1 - BID +NFKB + IAP +BID - IAP +NFKB + BclxL +STAT3 + BclxL +BID - BclxL +GZMB - BclxL +DISC - BclxL +IL2RB + MCL1 +STAT3 + MCL1 +NFKB + MCL1 +PI3K + MCL1 +DISC - MCL1 +Caspase + Apoptosis +S1P + GPCR +GPCR + SMAD +PDGFR + SPHK1 +SPHK1 + S1P +Ceramide - S1P +MEK + ERK +PI3K + ERK diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/fileio.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/fileio.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,146 @@ + +import os +import codecs +from collections import defaultdict + +import numpy as np +import networkx as nx + +from .base import Data + +__all__ = [ + "read_inputs", + "read_sif", + "create_from_sif", +] + + +def read_inputs(fpath): + inputs = {} + with codecs.open(fpath, "r", encoding="utf-8-sig") as fin: + for line in fin: + if line.isspace(): + continue + + items = line.split() + node = items[0].strip() + defval = float(items[1].strip()) + inputs[node] = defval + return inputs + + +def read_sif(fpath, signs={'+':1, '-':-1}, sort=True, as_nx=False): + dict_links = defaultdict(list) + set_nodes = set() + n2i = {} + with codecs.open(fpath, "r", encoding="utf-8-sig") as fin: + for line in fin: + if line.isspace(): + continue + + items = line.strip().split() + src = items[0] + trg = items[2] + sign = items[1] + + set_nodes.add(src) + set_nodes.add(trg) + int_sign = signs[sign] + dict_links[src].append((trg, int_sign)) + # end of for + # end of with + + if sort == True: + list_nodes = sorted(set_nodes) + else: + list_nodes = list(set_nodes) + + N = len(set_nodes) + adj = np.zeros((N, N), dtype=np.int) + + for isrc, name in enumerate(list_nodes): + n2i[name] = isrc # index of source + # end of for + for name_src in n2i: + isrc = n2i[name_src] + for name_trg, int_sign in dict_links[name_src]: + itrg = n2i[name_trg] + adj[itrg, isrc] = int_sign + # end of for + # end of for + + if not as_nx: + return adj, n2i + else: # NetworkX DiGraph + dg = nx.DiGraph() + # Add nodes + for name in list_nodes: + dg.add_node(name) + + # Add edges (links) + for name_src in list_nodes: + for name_trg, sign in dict_links[name_src]: + dg.add_edge(name_src, name_trg) + dg.edges[name_src, name_trg]['SIGN'] = sign + # end of for + # end of for + return adj, n2i, dg + # end of else + + +# end of def + + +def create_from_sif(fpath, abbr=None, inputs=None, outputs=None): + """Create sfv.base.Data object from SIF file. + + Parameters + ---------- + fpath : str + Absolute path of SIF file + abbr : str + Abbreviation to denote this data object for the network. + inputs : dict, optional + Input information with default values + outputs : sequence, optional + Output information. + + Returns + ------- + obj : sfv.base.Data + Data object with the information of network topology. + + """ + class __Data(Data): + def __init__(self): + if abbr: + self._abbr = abbr + else: + self._abbr = os.path.basename(fpath) + + self._name = self._abbr + A, n2i, dg = read_sif(fpath, as_nx=True) + self._A = A + self._n2i = n2i + self._i2n = {idx: name for name, idx in n2i.items()} + self._dg = dg + self._inputs = inputs + + if outputs: + self._outputs = outputs + + # The following members are not defined due to the lack of data. + self._df_conds = None + self._df_exp = None + self._df_ptb = None + self._has_link_perturb = False + self._names_ptb = None + self._iadj_to_idf = None + # end of def __init__ + # end of def class + + fname, ext = os.path.splitext(os.path.basename(fpath)) + fname = ''.join([c for c in fname.title() if c.isalnum()]) + fname += "Data" + __Data.__name__ = fname + return __Data() \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/manager.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/manager.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,46 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Jul 5 15:05:25 2016 + +@author: dwlee +""" + +from abc import ABCMeta, abstractmethod + +from .base import Algorithm +from .base import Data + +class JobManager(object): + __metaclass__ = ABCMeta + + def add_algorithm(self, alg): + + if not isinstance(alg, Algorithm): + raise TypeError("The algorithm should be " \ + "a subclass of sfa.base.Algorithm.") + # end of def add_algorithm + + def add_data(self, data): + + if not isinstance(data, Data): + raise TypeError("The data should be " \ + "a subclass of sfa.base.Data.") + + # end of def add_data + + #def (self, nprocs=False): + # pass + + # end of def process + + def _create_job(self, alg, data): + pass + + def _process_parallel(self): + pass + + def _process_single(self): + pass + + + \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/plot/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/plot/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- + +from .heatmap import Heatmap +from .table_batch import BatchResultTable +from .table_condition import ConditionTable +from .table_hierarchical_clustering import HierarchicalClusteringTable + +from .si import siplot \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/plot/base.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/plot/base.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,144 @@ +# -*- coding: utf-8 -*- + +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec + + +class BaseGridPlot(object): + + def __init__(self, colors=None, *args, **kwargs): + if not colors: + self._colors = dict() + else: + self._colors = dict(colors) + + self._set_colors(colors) + self._parse_kwargs(**kwargs) + + # Create figure and axis + self._create_figure() + self._create_axes() + + # end of def __init__ + + def _set_default_color(self, prop, defval): + if prop not in self._colors: + self._colors[prop] = defval + # end of def + + def _set_colors(self): + raise NotImplementedError() + + def _parse_kwargs(self, **kwargs): + """Parse the parameters for GridSpec + """ + self._dim = kwargs.get('dim', (1, 1)) + self._wspace = kwargs.get('wspace', 0) + self._hspace = kwargs.get('hspace', 0) + self._width_ratios = kwargs.get('width_ratios', [1]) + self._height_ratios = kwargs.get('height_ratios', [1]) + + def _create_figure(self): + self._fig = plt.figure() + self._gridspec = GridSpec(*self._dim, + wspace=self._wspace, + hspace=self._hspace, + width_ratios=self._width_ratios, + height_ratios=self._height_ratios) + + self._fig.set_facecolor('white') + + def _create_axes(self): + self._axes = {} + ax = self._fig.add_subplot(self._gridspec[0, 0]) + self._axes['base'] = ax + ax.grid(b=False) + ax.set_frame_on(False) + ax.invert_yaxis() + ax.xaxis.tick_top() + + @property + def column_tick_fontsize(self): + return self._column_tick_fontsize + + @column_tick_fontsize.setter + def column_tick_fontsize(self, val): + self._column_tick_fontsize = val + for ax in self._axes.values(): + ax.tick_params(axis='x', which='major', + labelsize=self._column_tick_fontsize) + + @property + def row_tick_fontsize(self): + return self._row_tick_fontsize + + @row_tick_fontsize.setter + def row_tick_fontsize(self, val): + self._row_tick_fontsize = val + for ax in self._axes.values(): + ax.tick_params(axis='y', which='major', + labelsize=self._row_tick_fontsize) + + # # Properties + # @property + # def column_tick_fontsize(self): + # raise NotImplementedError() + # + # @column_tick_fontsize.setter + # def column_tick_fontsize(self, val): + # raise NotImplementedError() + # + # @property + # def row_tick_fontsize(self): + # raise NotImplementedError() + # + # @row_tick_fontsize.setter + # def row_tick_fontsize(self, val): + # raise NotImplementedError() + + # @property + # def text_fontsize(self): + # raise NotImplementedError() + # + # @text_fontsize.setter + # def text_fontsize(self, val): + # raise NotImplementedError() + # + # @property + # def linewidth(self): + # raise NotImplementedError() + # + # @linewidth.setter + # def linewidth(self, val): + # raise NotImplementedError() + + # Read-only properties + @property + def colors(self): + return self._colors + + @property + def fig(self): + return self._fig + + @property + def axes(self): + return self._axes + + @property + def gridspec(self): + return self._gridspec + + +class BaseTable(BaseGridPlot): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._create_tables() + + def _create_tables(self): + raise NotImplementedError() + + @property + def tables(self): + return self._tables \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/plot/heatmap.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/plot/heatmap.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,147 @@ +# -*- coding: utf-8 -*- + +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sns + +from .base import BaseGridPlot + + +class Heatmap(BaseGridPlot): + + def __init__(self, df, *args, fmt='.3f', + cmap=None, vmin=0.0, vmax=1.0, + annot=True, **kwargs): + + super().__init__(*args, **kwargs) + + # Set references for data objects + self._df = df # A dataFrame + sns.heatmap(self._df, + ax=self._axes['base'], + annot=annot, + fmt=fmt, + annot_kws={"size": 10}, + linecolor=self._colors['table_edge_color'], + vmin=vmin, vmax=vmax, + cmap=cmap, + cbar_kws={"orientation": "vertical", + "pad": 0.02, }) + + + self._qm = self._axes['heatmap'].collections[0] + #self._qm.set_edgecolor(self._colors['table_edge_color']) + + # Change the labelsize + cb = self._qm.colorbar + cb.outline.set_linewidth(0.5) + cb.outline.set_edgecolor(self._colors['table_edge_color']) + cb_ax_asp = cb.ax.get_aspect() + cb.ax.set_aspect(cb_ax_asp * 2.0) + + # Remove inner lines + children = cb.ax.get_children() + for child in children: + if isinstance(child, matplotlib.collections.LineCollection): + child.set_linewidth(0) + + self._axes['heatmap'].xaxis.tick_top() + plt.xticks(rotation=90) + plt.yticks(rotation=0) + + self._axes['heatmap'].tick_params(axis='x', which='major', pad=-2) + self._axes['heatmap'].tick_params(axis='y', which='major', pad=3) + + # Hide axis labels + self._axes['heatmap'].set_xlabel('') + self._axes['heatmap'].set_ylabel('') + + # Text element of the heatmap object + self._texts = [] + ch = self._axes['heatmap'].get_children() + for child in ch: + if isinstance(child, matplotlib.text.Text): + if child.get_text() != '': + self._texts.append(child) + + # Set default values using properties + self.row_tick_fontsize = 10 + self.column_tick_fontsize = 10 + self.colorbar_label_fontsize = 10 + self.linewidth = 0.5 + + # end of __init__ + + def _set_colors(self, colors): + """Assign default color values for heatmap and colorbar + """ + self._set_default_color('table_edge_color', 'black') + self._set_default_color('colorbar_edge_color', 'black') + + def _create_axes(self): + super()._create_axes() + ax = self._axes['base'] + self._axes['heatmap'] = ax + #del self._axes['base'] + + # Properties + @property + def text_fontsize(self): + return self._text_fontsize + + @text_fontsize.setter + def text_fontsize(self, val): + """Resize text fonts + """ + self._text_fontsize = val + for t in self._texts: + t.set_fontsize(val) + + @property + def column_tick_fontsize(self): + return self._column_tick_fontsize + + @column_tick_fontsize.setter + def column_tick_fontsize(self, val): + self._column_tick_fontsize = val + self._axes['heatmap'].tick_params( + axis='x', + which='major', + labelsize=self._column_tick_fontsize) + + @property + def row_tick_fontsize(self): + return self._row_tick_fontsize + + @row_tick_fontsize.setter + def row_tick_fontsize(self, val): + self._row_tick_fontsize = val + self._axes['heatmap'].tick_params( + axis='y', + which='major', + labelsize=self._row_tick_fontsize) + + @property + def colorbar_tick_fontsize(self): + return self._colorbar_tick_fontsize + + @colorbar_tick_fontsize.setter + def colorbar_tick_fontsize(self, val): + self._colorbar_tick_fontsize = val + self._qm.colorbar.ax.tick_params(axis='y', labelsize=val) + + @property + def linewidth(self): + return self._linewidth + + @linewidth.setter + def linewidth(self, val): + """Adjust the width of table lines + """ + self._linewidth = val + self._qm.set_linewidth(self._linewidth) + + # Read-only properties + @property + def colorbar(self): + return self._qm.colorbar \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/plot/si.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/plot/si.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,165 @@ + +from collections import Counter + +import numpy as np +import pandas as pd +import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt +from matplotlib.patches import Rectangle +from matplotlib.ticker import FormatStrFormatter +from matplotlib import rcParams + +rcParams['font.family'] = 'sans-serif' +rcParams['font.sans-serif'] = ['Arial'] + +def siplot(df_splo, + df_inf, + output, + min_splo=None, + max_splo=None, + thr_inf=1e-10, + fmt_inf='%f', + fig=None, + cnt_max=None, + ncol=4, + designated=None, + color='silver', + dcolor='red', + zcolor='red', + alpha=0.7, + xfontsize=8, + yfontsize=8): + + # SPLO-Influence Data + if not min_splo: + min_splo = df_splo.min() + + if not max_splo: + max_splo = df_splo.max() + + mask_splo = (min_splo <= df_splo) & (df_splo <= max_splo) + df_splo = df_splo[mask_splo] + + df_splo = pd.DataFrame(df_splo) + df_splo.columns = ['SPLO'] + + + if output in df_splo.index: + df_splo.drop(output, inplace=True) + + index_common = df_splo.index.intersection(df_inf.index) + df_inf = pd.DataFrame(df_inf.loc[index_common]) + + mark_drop = df_inf[output].abs() <= thr_inf + df_inf.drop(df_inf.loc[mark_drop, output].index, + inplace=True) + + + df_si = df_inf.join(df_splo.loc[index_common]) + df_si.index.name = 'Source' + df_si.reset_index(inplace=True) + + cnt_splo = Counter(df_si['SPLO']) + if not cnt_max: + cnt_max = max(cnt_splo.values()) + + splos = sorted(cnt_splo.keys()) + nrow = int(np.ceil(len(splos)/ncol)) + + # Plot + if not fig: + fig = plt.figure() + + gs = gridspec.GridSpec(nrow, ncol) + + yvals = np.arange(1, cnt_max +1) + for i, splo in enumerate(splos): + idx_row = int(i / ncol) + idx_col = int(i % ncol) + ax = fig.add_subplot(gs[idx_row, idx_col]) + df_sub = df_si[df_si['SPLO'] == splo] + df_sub = df_sub.sort_values(by=output) + num_items = df_sub[output].count() + + influence = np.zeros((cnt_max,)) # Influence + num_empty = cnt_max - num_items + influence[num_empty:] = df_sub[output] + names = df_sub['Source'].tolist() + names = ['' ] *(num_empty) + names + + # Plot bars + plt.barh(yvals, influence, align='center', + alpha=alpha) + + ax.set_title('SPLO=%d'%(splo)) + ax.set_xlabel('') + + ax.xaxis.set_major_formatter(FormatStrFormatter(fmt_inf)) + ax.tick_params(axis='x', + which='major', + labelsize=xfontsize) + + ax.set_ylabel('') + ax.yaxis.set_ticks_position('right') + ax.tick_params(axis='y', + which='major', + labelsize=yfontsize) + + plt.yticks(yvals, names) + + # Draw zero line. + if not((influence <= 0).all() or (influence >= 0).all()): + ax.vlines(x=0.0, ymin=0, ymax=yvals[-1]+1, color=zcolor) + + # Set limitations + ax.set_ylim(0, cnt_max +1) + + if designated: + # Filter bar graphics. + bars = [] + cnt_bars = 0 + for obj in ax.get_children(): + if cnt_bars == cnt_max: + break + if isinstance(obj, Rectangle): + bars.append(obj) + obj.set_color(color) + cnt_bars += 1 + # end of for + + # Change the bars of the designated names. + for i, name in enumerate(names): + if name in designated: + bars[i].set_color(dcolor) + # end of for + + # Change the text colors of the designated names. + for obj in ax.get_yticklabels(): + name = obj.get_text() + if name in designated: + obj.set_color(dcolor) + # end of for + # end of for + + # Make zero notation more simple. + fig.canvas.draw() + for ax in fig.axes: + labels = [] + for obj in ax.get_xticklabels(): + try: + text = obj.get_text() + num = float(text) + except ValueError: + labels.append(text) + continue + + if num == 0: + labels.append('0') + else: + labels.append(text) + # end of for + ax.set_xticklabels(labels) + # end of for + # end of for + + return fig diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/plot/table_batch.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/plot/table_batch.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,75 @@ +# -*- coding: utf-8 -*- + +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec + +from .tableaxis import ResultTableAxis +from .table_condition import ConditionTable + + +class BatchResultTable(ConditionTable): + + def __init__(self, data, cons, *args, **kwargs): + # Set references for data objects + self._dfe = data.df_exp # DataFrame of experiment results + self._dfr = cons # DataFrame of consensus between exp. and sim. + super().__init__(data.df_conds, *args, **kwargs) + # end of def __init__ + + def _parse_kwargs(self, **kwargs): + """Parse the keyword arguments. + """ + self._dim = kwargs.get('dim', (1, 2)) + self._wspace = kwargs.get('wspace', 0.05) + self._hspace = kwargs.get('hspace', 0) + + ncols_conds = self._dfc.shape[1] + ncols_res = self._dfr.shape[1] + self._width_ratios = kwargs.get('width_ratios', [ncols_conds, + ncols_res]) + self._height_ratios = kwargs.get('height_ratios', [1]) + + default_position = {'condition': (0, 0), 'result': (0, 1), } + self._axes_position = kwargs.get('axes_position', + default_position) + + def _create_axes(self): + self._axes = {} + pos = self._axes_position['condition'] + ax_conds = self._fig.add_subplot(self._gridspec[pos[0], pos[1]]) + self._axes['condition'] = ax_conds + + pos = self._axes_position['result'] + ax_res = self._fig.add_subplot(self._gridspec[pos[0], pos[1]]) + self._axes['result'] = ax_res + + for ax in self._axes.values(): + ax.grid(b=False) + ax.set_frame_on(False) + ax.invert_yaxis() + ax.xaxis.tick_top() + + def _create_tables(self): + super()._create_tables() + tb = ResultTableAxis(self._axes['result'], + self._dfr, + self._dfe, + self._colors) + tb.fontsize = 4 + tb.linewidth = 0.5 + self._tables.append(tb) + + def _set_colors(self, colors): + super()._set_colors(colors) + self._set_default_color('result_up_cell', '#35FF50') # Green + self._set_default_color('result_dn_cell', '#FF0000') # Red + self._set_default_color('result_up_text', 'black') + self._set_default_color('result_dn_text', 'white') + + def _add_labels(self): + super()._add_labels() + + # The second table, which is result DataFrame, adds + # column labels only. + tb = self._tables[1] + tb.add_column_labels() \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/plot/table_condition.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/plot/table_condition.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,44 @@ +from .base import BaseTable +from .tableaxis import ConditionTableAxis + + +class ConditionTable(BaseTable): + + def __init__(self, conds, *args, **kwargs): + self._dfc = conds # DataFrame of condition cases + super().__init__(*args, **kwargs) + """ + Add labels using x and y axes. + The default values should be assigned before adding labels. + """ + self.row_tick_fontsize = 5 + self.column_tick_fontsize = 5 + self._add_labels() + # end of def __init__ + + def _set_colors(self, colors): + """Assign default color values, if it is not defined. + """ + self._set_default_color('cond_up_cell', 'blue') + self._set_default_color('cond_dn_cell', 'white') + + def _create_axes(self): + super()._create_axes() + ax = self._axes['base'] + self._axes['condition'] = ax + del self._axes['base'] + + def _create_tables(self): + self._tables = [] + tb = ConditionTableAxis(self._axes['condition'], + self._dfc, self._colors) + tb.fontsize = 4 + tb.linewidth = 0.5 + self._tables.append(tb) + + def _add_labels(self): + tb = self._tables[0] + tb.add_row_labels() + tb.add_column_labels() + + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/plot/table_hierarchical_clustering.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/plot/table_hierarchical_clustering.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,286 @@ + +import numpy as np +import scipy.spatial.distance as distance +import scipy.cluster.hierarchy as sch + +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec + +import seaborn as sns + +from .table_condition import ConditionTable + + +class HierarchicalClusteringTable(ConditionTable): + + def __init__(self, conds, samples, *args, **kwargs): + # Set references for data objects + self._dfs = samples # DataFrame of samples to be clustered. + super().__init__(conds, *args, **kwargs) + self._create_colorbar() + self.column_tick_fontsize = self._table_tick_fontsize + # end of def __init__ + + def _parse_kwargs(self, **kwargs): + """Parse the keyword arguments. + """ + + self._vmin = kwargs.get('vmin', None) + self._vmax = kwargs.get('vmax', None) + + colors_blend = ['red', 'white', np.array([0, 1, 0, 1])] + default_cmap = sns.blend_palette(colors_blend, + n_colors=100, + as_cmap=True) + self._cmap = kwargs.get('cmap', default_cmap) + + self._dim = kwargs.get('dim', (2, 5)) + self._wspace = kwargs.get('wspace', 0.005) + self._hspace = kwargs.get('hspace', 0.005) + + default_width_ratios = [self._dfc.shape[1], + 0.25, + self._dfs.shape[1], + 0.5*self._dfs.shape[1], + 0.05*self._dfs.shape[1]] + + default_height_ratios = [self._dfs.shape[1], + self._dfc.shape[0]] + + self._width_ratios = kwargs.get('width_ratios', + default_width_ratios) + self._height_ratios = kwargs.get('height_ratios', + default_height_ratios) + + default_position = {'condition': np.array([1, 0]), + 'heatmap': np.array([1, 2]), + 'row_dendrogram': np.array([1, 3]), + 'col_dendrogram': np.array([0, 2]), + 'colorbar': np.array([1, 4])} + + self._axes_position = kwargs.get('axes_position', + default_position) + + self._row_cluster = kwargs.get('row_cluster', True) + self._col_cluster = kwargs.get('col_cluster', True) + + if self._row_cluster: + self._row_method = kwargs.get('row_method', 'single') + self._row_metric = kwargs.get('row_metric', 'cityblock') + self._row_dend_linewidth = kwargs.get('row_dend_linewidth', 0.5) + + if self._col_cluster: + self._col_method = kwargs.get('col_method', 'single') + self._col_metric = kwargs.get('col_metric', 'cityblock') + self._col_dend_linewidth = kwargs.get('col_dend_linewidth', 0.5) + + self._table_linewidth = kwargs.get('table_linewidth', 0.5) + self._table_tick_fontsize = kwargs.get('table_tick_fontsize', 5) + self._colorbar_tick_fontsize = kwargs.get('colorbar_tick_fontsize', 5) + + def _create_axes(self): + self._axes = {} + # pos = self._axes_position['heatmap'] + # ax_heatmap = self._fig.add_subplot(self._gridspec[pos[0], pos[1]]) + # self._axes.append(ax_heatmap) + + pos = self._axes_position['condition'] + ax_conds = self._fig.add_subplot(self._gridspec[pos[0], pos[1]]) + self._axes['condition'] = ax_conds + ax_conds.grid(b=False) + ax_conds.set_frame_on(False) + ax_conds.invert_yaxis() + ax_conds.xaxis.tick_bottom() + + self._perform_clustering() + + def _create_tables(self): + super()._create_tables() + ax_heatmap = self._axes['heatmap'] + + # Draw lines on table and heatmap + self.tables[0].linewidth = self._table_linewidth + for x in range(self._dfs.shape[1]+1): + ax_heatmap.axvline(x-0.5, + linewidth=self._table_linewidth, + color='k', zorder=10) + + for y in range(self._dfs.shape[0]+1): + ax_heatmap.axhline(y-0.5, + linewidth=self._table_linewidth, + color='k', zorder=10) + + def _perform_clustering(self): + sch.set_link_color_palette(['black']) + if self._row_cluster: + + row_pairwise_dists = distance.pdist(self._dfs, + metric=self._row_metric) + row_clusters = sch.linkage(row_pairwise_dists, + metric=self._row_metric, + method=self._row_method) + + with plt.rc_context({'lines.linewidth': self._row_dend_linewidth}): + # Dendrogram for row clustering + pos = self._axes_position['row_dendrogram'] + subgs = self._gridspec[pos[0], pos[1]] + ax_row_den = self._fig.add_subplot(subgs) + row_den = sch.dendrogram(row_clusters, + color_threshold=np.inf, + orientation='right') + + ax_row_den.set_facecolor("white") + self._clean_axis(ax_row_den) + self._axes['row_dendrogram'] = ax_row_den + + ind_row = row_den['leaves'] + # Rearrange the DataFrame for condition according to + # the clustering result. + self._dfc = self._dfc.iloc[ind_row, :] + else: + ind_row = range(self._dfs.index.size) #self._dfs.index.ravel() + + if self._col_cluster: + col_pairwise_dists = distance.pdist(self._dfs.T, + metric=self._col_metric) + col_clusters = sch.linkage(col_pairwise_dists, + metric=self._col_metric, + method=self._col_method) + + with plt.rc_context({'lines.linewidth': self._col_dend_linewidth}): + # Dendrogram for column clustering + pos = self._axes_position['col_dendrogram'] + ax_col_den = self._fig.add_subplot(self._gridspec[pos[0], pos[1]]) + col_den = sch.dendrogram(col_clusters, + color_threshold=np.inf, + orientation='top') + ax_col_den.set_facecolor("white") + self._clean_axis(ax_col_den) + self._axes['col_dendrogram'] = ax_col_den + ind_col = col_den['leaves'] + else: + # ind_col = self._dfs.columns.ravel() + ind_col = range(self._dfs.columns.size) + + # Heatmap + pos = self._axes_position['heatmap'] + subgs = self._gridspec[pos[0], pos[1]] + ax_heatmap = self._fig.add_subplot(subgs) + self._heatmap = ax_heatmap.matshow(self._dfs.iloc[ind_row, ind_col], + vmin=self._vmin, + vmax=self._vmax, + interpolation='nearest', + aspect='auto', + #origin='lower', + cmap=self._cmap) + + ax_heatmap.grid(b=False) + ax_heatmap.set_frame_on(True) + ax_heatmap.xaxis.tick_bottom() + self._axes['heatmap'] = ax_heatmap + self._clean_axis(ax_heatmap) + + # Remove the y-labels of condition table + #ax_conds = self._axes['condition'] + + # Add column labels + ax_heatmap.set_xticks(np.arange(0, self._dfs.shape[1], 1)) + ax_heatmap.set_xticklabels(np.array(self._dfs.columns[ind_col]), + rotation=90, minor=False) + + ax_heatmap.tick_params(axis='x', which='major', pad=-2) + + # Remove the tick lines + for line in ax_heatmap.get_xticklines(): + line.set_markersize(0) + + for line in ax_heatmap.get_yticklines(): + line.set_markersize(0) + + def _create_colorbar(self): + pos = self._axes_position['colorbar'] + subgs = self._gridspec[pos[0], pos[1]] + ax_colorbar = self._fig.add_subplot(subgs) + + cb = self._fig.colorbar(self._heatmap, ax_colorbar, + drawedges=False) #True) + self._colorbar = cb + #cb.ax.yaxis.set_ticks_position('right') + #self._clean_axis(cb.ax) + # for sp in cb.ax.spines.values(): + # sp.set_visible(False) + cb.ax.yaxis.set_ticks_position('none') + cb.ax.yaxis.set_tick_params(pad=-2) + cb.ax.yaxis.set_label_position('right') + cb.outline.set_edgecolor('black') + cb.outline.set_linewidth(self._table_linewidth) + self.colorbar_fontsize = self._colorbar_tick_fontsize + + def _clean_axis(self, ax): + """Remove ticks, tick labels, and frame from axis + """ + ax.xaxis.set_ticks_position('none') + ax.yaxis.set_ticks_position('none') + ax.xaxis.set_ticks([]) + ax.yaxis.set_ticks([]) + for sp in ax.spines.values(): + sp.set_visible(False) + + def _add_labels(self): + """Add only column labels for condition table. + """ + tb = self._tables[0] + tb.add_column_labels() + + @property + def table_linewidth(self): + return self._table_linewidth + + @table_linewidth.setter + def table_linewidth(self, val): + self._table_linewidth = val + + @property + def colorbar(self): + return self._colorbar + + @property + def colorbar_fontsize(self): + return self._colorbar_tick_fontsize + + @colorbar_fontsize.setter + def colorbar_fontsize(self, val): + self._colorbar_tick_fontsize = val + ticks = self._colorbar.ax.yaxis.get_ticklabels() + for t in ticks: + t.set_fontsize(self._colorbar_tick_fontsize) + + # @column_tick_fontsize.setter + # def column_tick_fontsize(self, val): + # self._column_tick_fontsize = val + # for ax in self._axes: + # ax.tick_params(axis='x', which='major', + # labelsize=self._column_tick_fontsize) + + # def _set_colors(self, colors): + # super()._set_colors(colors) + # + # def _add_labels(self): + # super()._add_labels() # Add labels for condition table + + + # def _add_column_labels(self): + # """Add column labels using x-axis + # """ + # xlabels = list(self._dfs.columns) + # ax_heatmap.set_xticks(np.arange(self._dfs.shape[0])) + # ax_heatmap.set_xticklabels(xlabels, + # rotation=90, minor=False) + # ax_heatmap.tick_params(axis='x', which='major', pad=3) + # + # # Hide the small bars of ticks + # for tick in self._ax.xaxis.get_major_ticks(): + # tick.tick1On = False + # tick.tick2On = False + # # # end of def \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/plot/tableaxis.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/plot/tableaxis.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,175 @@ + +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.table import Table + + +class TableAxis(object): + def __init__(self, ax, df, colors): + self._ax = ax + self._df = df + self._colors = colors + + # Create matplotlib's Table object + self._tb = Table(self._ax, bbox=[0, 0, 1, 1]) + self._tb.auto_set_font_size(False) + self._ax.add_table(self._tb) + + self._calculate_cell_size() + self._add_cells() + self._remove_ticks() + + def _calculate_cell_size(self): + # The number of total rows and columns of the table + self._nrows = self._df.shape[0] + self._ncols = self._df.shape[1] + + # Calculate the width and height of a single cell in the table + L = 1.0 + H = 1.0 + self._w_cell = L / self._ncols + self._h_cell = H / self._nrows + + def _add_cells(self): + raise NotImplementedError() + + def _remove_ticks(self): + self._ax.get_xaxis().set_ticks([]) + self._ax.get_yaxis().set_ticks([]) + + def add_row_labels(self): + """Add row labels using y-axis + """ + ylabels = list(self._df.index) + self._ax.yaxis.tick_left() + + yticks = [self._h_cell/2.0] # The position of the first label + + # The row labels of condition subtable + for j in range(1, self._nrows): + yticks.append(yticks[j-1] + self._h_cell) + + self._ax.set_yticks(yticks) + self._ax.set_yticklabels(ylabels, minor=False) + self._ax.tick_params(axis='y', which='major', pad=3) + + # Hide the small bars of ticks + for tick in self._ax.yaxis.get_major_ticks(): + tick.tick1On = False + tick.tick2On = False + # end of def + + def add_column_labels(self): + """ + Add column labels using x-axis + """ + xlabels = list(self._df.columns) + xticks = [self._w_cell/2.0] # The position of the first label + # The column labels of condition subtable + for j in range(1, self._ncols): + xticks.append(xticks[j - 1] + self._w_cell) + + self._ax.xaxis.set_ticks_position('none') + + # # Hide the small bars of ticks + # for tick in self._ax.xaxis.get_major_ticks(): + # tick.tick1On = False + # tick.tick2On = False + + self._ax.set_xticks(xticks) + self._ax.set_xticklabels(xlabels, rotation=90, minor=False) + self._ax.tick_params(axis='x', which='major', pad=-2) + + # end of def + + @property + def fontsize(self): + return self._table_fontsize + + @fontsize.setter + def fontsize(self, val): + """ + Resize text fonts + """ + self._table_fontsize = val + self._tb.set_fontsize(val) + + @property + def linewidth(self): + return self._linewidth + + @linewidth.setter + def linewidth(self, val): + """Adjust the width of table lines + """ + self._linewidth = val + celld = self._tb.get_celld() + for (i, j), cell in celld.items(): + cell.set_linewidth(self._linewidth) + + +class ConditionTableAxis(TableAxis): + + def _add_cells(self): + for (i, j), val in np.ndenumerate(self._df): + if val != 0: + fcolor = self._colors['cond_up_cell'] + else: + fcolor = self._colors['cond_dn_cell'] + + self._tb.add_cell(i, j, + self._w_cell, self._h_cell, + loc='center', facecolor=fcolor) + + +class ResultTableAxis(TableAxis): + + def __init__(self, ax, dfr, dfe, colors): + self._dfr = dfr + self._dfe = dfe + super().__init__(ax, dfr, colors) + + def _add_cells(self): + """Add cells for representing the agreement between + the results of simulation and experiment. + """ + for (i, j), val_cons in np.ndenumerate(self._dfr): + if val_cons == True: + fcolor = self._colors['result_up_cell'] + elif val_cons == False: + fcolor = self._colors['result_dn_cell'] + else: + raise ValueError("The value of result table element " + "should be bool.") + + val_exp = self._dfe.iloc[i, j] + if val_exp > 0: + text_arrow = 'UP' + elif val_exp < 0: + text_arrow = 'DN' + else: # val_Exp == 0 + text_arrow = '─' + + self._tb.add_cell(i, j, + self._w_cell, self._h_cell, + text=text_arrow, + loc='center', + facecolor=fcolor) + # end of for + + # Set colors of the result + celld = self._tb.get_celld() + for (i, j), val in np.ndenumerate(self._dfr): + cell = celld[(i, j)] + + # Set colors + if val > 0: + tcolor = self._colors['result_up_text'] + else: + tcolor = self._colors['result_dn_text'] + # end of if-else + + # Adjust text + cell.set_text_props(color=tcolor, + weight='bold') + # end of for \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/stats.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/stats.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,263 @@ + +import numpy as np +import scipy as sp +import pandas as pd + + + + +__all__ = ["calc_accuracy",] + # "calc_auroc", + # "calc_auprc", + # "calc_roc_curve", + # "calc_pr_curve"] + + +def calc_accuracy(df1, df2, get_cons=False): + """ + Count the same sign of each element between df1 and df2 + + df1: pandas.DataFrame or numpy.ndarray to be compared + df2: pandas.DataFrame or numpy.ndarray to be compared + getcons: decide whether to return consensus array in DataFrame or not + """ + + np.sign(df1) + np.sign(df2) + if df1.ndim == 1: + num_total = df1.shape[0] + elif df1.ndim == 2: + num_total = df1.shape[0] * df1.shape[1] + diff_abs = np.abs(np.sign(df1) - np.sign(df2)) + consensus = (diff_abs == 0) + + if isinstance(consensus, pd.DataFrame): + num_cons = consensus.values.sum() + else: + #num_cons = consensus.sum(axis=1).sum() # Number of consensus + num_cons = consensus.sum() + + acc = (num_cons) / np.float(num_total) # Accuracy + if get_cons: + return acc, consensus + else: + return acc + + +# end of def + +# +# from sklearn.metrics import roc_curve, auc +# from sklearn.metrics import roc_auc_score +# from sklearn.metrics import precision_recall_curve +# from sklearn.metrics import average_precision_score +# +# def _pre_process(class_type, arr_exp, arr_sim): +# if not isinstance(class_type, str): +# raise TypeError("class_type should be str.") +# +# if class_type == 'UP': +# arr_exp[arr_exp > 0] = 1 +# arr_exp[arr_exp <= 0] = 0 +# elif class_type == 'DN': +# arr_exp *= -1 # Flip the sign +# arr_sim *= -1 +# arr_exp[arr_exp > 0] = 1 +# arr_exp[arr_exp <= 0] = 0 +# elif class_type == '-': +# arr_exp[arr_exp == 0] = 1 +# arr_exp[arr_exp != 0] = 0 +# +# +# # end of def +# +# def calc_auroc(df_exp, df_sim, class_type='UP'): +# auroc = dict() +# +# arr_exp_ravel = np.array(df_exp.unstack()) +# arr_sim_ravel = np.array(df_sim.unstack()) +# _pre_process(class_type, arr_exp_ravel, arr_sim_ravel) +# auroc['mean'] = roc_auc_score(arr_exp_ravel, arr_sim_ravel) +# return auroc +# +# for idx_roc, (name_roc, col) in enumerate(df_exp.iteritems()): +# +# arr_exp = np.array(col) +# arr_sim = np.array(df_sim[name_roc]) +# +# _pre_process(class_type, arr_exp, arr_sim) +# +# try: +# auc = roc_auc_score(arr_exp, arr_sim) +# auroc[name_roc] = auc +# except ValueError as ve: +# print ("Skip calculating AUROC of %s " +# "due to the following." % name_roc) +# print (ve) +# continue +# +# # end of for +# auroc["mean"] = sum(auroc.values()) / len(auroc) +# return auroc +# # end of def +# +# def calc_auprc(df_exp, df_sim, class_type='UP'): +# auprc = dict() +# +# arr_exp_ravel = np.array(df_exp.unstack()) +# arr_sim_ravel = np.array(df_sim.unstack()) +# _pre_process(class_type, arr_exp_ravel, arr_sim_ravel) +# auprc['mean'] = average_precision_score(arr_exp_ravel, arr_sim_ravel) +# return auprc +# +# for idx_roc, (name_roc, col) in enumerate(df_exp.iteritems()): +# +# arr_exp = np.array(col) +# arr_sim = np.array(df_sim[name_roc]) +# +# _pre_process(class_type, arr_exp, arr_sim) +# +# try: +# auc = average_precision_score(arr_exp, arr_sim) +# auprc[name_roc] = auc +# except ValueError as ve: +# print ("Skip calculating AUPRC of %s " +# "due to the following." % name_roc) +# print (ve) +# continue +# except FloatingPointError as fpe: +# print("Skip calculating AUPRC of %s " +# "due to the following." % name_roc) +# print(fpe) +# continue +# +# # end of for +# auprc["mean"] = sum(auprc.values()) / len(auprc) +# return auprc +# +# +# # end of def +# +# def calc_roc_curve(df_exp, df_sim, class_type='UP'): +# dict_fpr = dict() +# dict_tpr = dict() +# dict_thr = dict() +# dict_auroc = dict() +# +# # name: node name or ID +# for idx_roc, (name, col) in enumerate(df_exp.iteritems()): +# arr_exp = np.array(col) +# arr_sim = np.array(df_sim[name]) +# +# _pre_process(class_type, arr_exp, arr_sim) +# +# try: +# fpr, tpr, thr = roc_curve(arr_exp, arr_sim) +# dict_auroc[name] = auc(fpr, tpr) +# except FloatingPointError as fpe: +# print("Skip calculating AUROC of %s " +# "due to the following." % name) +# print(fpe) +# continue +# +# dict_fpr[name] = fpr +# dict_tpr[name] = tpr +# dict_thr[name] = thr +# # end of for +# +# """ +# Use different thresholds for each entity (e.g., protein) to test with ROC +# # First aggregate all false positive rates +# fprs = [] +# names_valid = [] # Names which has no nan values in false positive rates. +# for name in dict_fpr: +# if np.any(np.isnan(dict_fpr[name])) \ +# or np.any(np.isnan(dict_tpr[name])): +# continue +# fprs.append(dict_fpr[name]) +# names_valid.append(name) +# # end of for +# all_fpr = np.unique(np.concatenate(fprs)) +# mean_tpr = np.zeros_like(all_fpr) +# for name in names_valid: +# mean_tpr += sp.interp(all_fpr, dict_fpr[name], dict_tpr[name]) +# +# mean_tpr /= len(names_valid) +# +# dict_fpr["mean"] = all_fpr +# dict_tpr["mean"] = mean_tpr +# dict_auroc["mean"] = auc(dict_fpr["mean"], dict_tpr["mean"]) +# """ +# arr_exp_ravel = np.array(df_exp.unstack()) +# arr_sim_ravel = np.array(df_sim.unstack()) +# _pre_process(class_type, arr_exp_ravel, arr_sim_ravel) +# fpr, tpr, thr = roc_curve(arr_exp_ravel, arr_sim_ravel) +# +# dict_fpr["mean"] = fpr +# dict_tpr["mean"] = tpr +# dict_thr["mean"] = thr +# dict_auroc["mean"] = roc_auc_score(arr_exp_ravel, arr_sim_ravel) #auc(fpr, tpr) +# +# return dict_fpr, dict_tpr, dict_thr, dict_auroc +# # end of def +# +# +# def calc_pr_curve(df_exp, df_sim, class_type='UP'): +# dict_precision = dict() +# dict_recall = dict() +# dict_thr = dict() +# dict_auprc = dict() +# +# # name: node name or ID +# for idx_roc, (name, col) in enumerate(df_exp.iteritems()): +# arr_exp = np.array(col) +# arr_sim = np.array(df_sim[name]) +# +# _pre_process(class_type, arr_exp, arr_sim) +# +# try: +# precision, recall, thr = precision_recall_curve(arr_exp, arr_sim) +# dict_auprc[name] = average_precision_score(arr_exp, arr_sim) +# except FloatingPointError as fpe: +# print("Skip calculating AUPRC of %s " +# "due to the following." % name) +# print(fpe) +# continue +# +# dict_precision[name] = precision +# dict_recall[name] = recall +# dict_thr[name] = thr +# # end of for +# +# # First aggregate all false positive rates +# # recalls = [] +# # names_valid = [] # Names which has no nan values in false positive rates. +# # for name in dict_recall: +# # if np.any(np.isnan(dict_recall[name])) \ +# # or np.any(np.isnan(dict_precision[name])): +# # continue +# # recalls.append(dict_recall[name]) +# # names_valid.append(name) +# # # end of for +# # all_recall = np.unique(np.concatenate(recalls)) +# # #all_recall = np.array(sorted(all_recall, reverse=True)) +# # mean_precision = np.zeros_like(all_recall) +# # for name in names_valid: +# # mean_precision += sp.interp(all_recall, +# # dict_recall[name], +# # dict_precision[name]) +# # +# # mean_precision /= len(names_valid) +# +# arr_exp_ravel = np.array(df_exp.unstack()) +# arr_sim_ravel = np.array(df_sim.unstack()) +# _pre_process(class_type, arr_exp_ravel, arr_sim_ravel) +# precision, recall, thr = precision_recall_curve(arr_exp_ravel, +# arr_sim_ravel) +# +# dict_recall["mean"] = recall #all_recall +# dict_precision["mean"] = precision #mean_precision +# dict_thr["mean"] = thr +# #dict_auprc["mean"] = auc(dict_recall["mean"], dict_precision["mean"]) +# dict_auprc["mean"] = average_precision_score(arr_exp_ravel, arr_sim_ravel) +# return dict_recall, dict_precision, dict_thr, dict_auprc +# # end of def \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/topology.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/topology.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,74 @@ +# -*- coding: utf-8 -*- +import sys +if sys.version_info <= (2, 8): + from builtins import super + +import numpy as np +import pandas as pd +import networkx as nx +from networkx import shortest_paths as nxsp + +__all__ = ["max_spl", + "splo"] + + +def max_spl(nxdg): + """Find the maximum length of the shortest path + """ + all_spl = nxsp.all_pairs_shortest_path_length(nxdg) + max_spl = 0 + for src, targets in all_spl: + for tgt, spl in targets.items(): + if spl > max_spl: + max_spl = spl + # end of for + # end of for + return max_spl + + +def splo(nxdg, sources, outputs, rtype='df'): + """Calculate the shortest path length + from each source node to the outputs. + SPLO represents + 'shortest path length to output'. + + Parameters + ---------- + nxdg: NetworkX.DiGraph + A directed network in NetworkX. + sources: list (or iterable) of str + Names of source nodes in nxdg. + outputs: list (or iterable) of str + Names of output nodes in nxdg. + rtype: str (optional) + Return object type: 'df' or 'dict'. + + Returns + ------- + splo: dict + All the shortest path lengths + to the outputs. + + """ + if isinstance(outputs, str): + outputs = [outputs] + + dict_splo = {} + for trg in outputs: + dict_splo[trg] = {} + for src in sources: + try: + splo = nxsp.shortest_path_length(nxdg, + src, + trg) + except nx.NetworkXNoPath: + continue # splo = np.inf + + dict_splo[trg][src] = splo + + if rtype == 'df': + df = pd.DataFrame(dict_splo) + df.index.name = 'Source' + return df + + return dict_splo diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/utils.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/utils.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,354 @@ +# -*- coding: utf-8 -*- +import sys +if sys.version_info <= (2, 8): + from builtins import super + +import os +import codecs +from collections import defaultdict + +import numpy as np +import scipy as sp +import pandas as pd +import networkx as nx + + +__all__ = ["FrozenClass", + "Singleton", + "to_networkx_digraph", + "normalize", + "rand_swap", + "rand_flip", + "rand_weights", + "rand_structure", + "get_akey", + "get_avalue",] + + +class FrozenClass(object): + + __isfrozen = False + + def __setattr__(self, key, value): + if self.__isfrozen and not hasattr(self, key): + raise TypeError( "%r is a frozen class" % self ) + object.__setattr__(self, key, value) + + def _freeze(self): + self.__isfrozen = True + + def _melt(self): + self.__isfrozen = False + +""" + +http://stackoverflow.com/questions/3603502/prevent-creating-new-attributes-outside-init +""" +# end of def FrozenClass + + +def Singleton(_class): + class __Singleton(_class): + __instance = None + + def __new__(cls, *args, **kwargs): + if cls.__instance is None: + cls.__instance = super().__new__(cls, *args, **kwargs) + + # Creation and initialization of '__initialized' + cls.__instance.__initialized = False + # end of if + return cls.__instance + + def __init__(self, *args, **kwargs): + if self.__initialized: + return + + super().__init__(*args, **kwargs) + self.__initialized = True + + def __repr__(self): + return '<{0} Singleton object at {1}>'.format( + _class.__name__, hex(id(self))) + + def __str__(self): + return super().__str__() + # end of def class + + __Singleton.__name__ = _class.__name__ + return __Singleton + +""" + +http://m.egloos.zum.com/mataeoh/v/7081556 +""" +# end of def Singleton + +def normalize(A, norm_in=True, norm_out=True): + # Check whether A is a square matrix + if A.shape[0] != A.shape[1]: + raise ValueError( + "The A (adjacency matrix) should be square matrix.") + + # Build propagation matrix (aka. transition matrix) _W from A + W = A.copy() + + # Norm. out-degree + if norm_out == True: + sum_col_A = np.abs(A).sum(axis=0) + sum_col_A[sum_col_A == 0] = 1 + if norm_in == False: + Dc = 1 / sum_col_A + else: + Dc = 1 / np.sqrt(sum_col_A) + # end of else + W = Dc * W # This is not matrix multiplication + + # Norm. in-degree + if norm_in == True: + sum_row_A = np.abs(A).sum(axis=1) + sum_row_A[sum_row_A == 0] = 1 + if norm_out == False: + Dr = 1 / sum_row_A + else: + Dr = 1 / np.sqrt(sum_row_A) + # end of row + W = np.multiply(W, np.mat(Dr).T) + # Converting np.mat to ndarray + # does not cost a lot. + W = W.A + # end of if + """ + The normalization above is the same as the follows: + >>> np.diag(Dr).dot(A.dot(np.diag(Dc))) + """ + return W + + +# end of def normalize + +def to_networkx_digraph(A, n2i=None): + if not n2i: + return nx.from_numpy_array(A, create_using=nx.Digraph) + + i2n = {ix:name for name, ix in n2i.items()} + dg = nx.DiGraph() + ind_row, ind_col = A.nonzero() + for ix_trg, ix_src in zip(ind_row, ind_col): + name_src = i2n[ix_src] + name_trg = i2n[ix_trg] + sign = np.sign(A[ix_trg, ix_src]) + dg.add_edge(name_src, name_trg) + dg.edges[name_src, name_trg]['SIGN'] = sign + # end of for + return dg + # end of for +# end of def to_networkx_digraph + +def rand_swap(A, nsamp=10, noself=True, pivots=None, inplace=False): + """Randomly rewire the network connections by swapping. + + Parameters + ---------- + A : numpy.ndarray + Adjacency matrix (connection matrix). + nsamp : int, optional + Number of sampled connections to rewire + noself : bool, optional + Whether to allow self-loop link. + pivots : list, optional + Indices of pivot nodes + inplace : bool, optional + Modify the given adjacency matrix for rewiring. + + + Returns + ------- + B : numpy.ndarray + The randomized matrix. + The reference of the given W is returned, when inplace is True. + """ + + + if not inplace: + A_org = A + B = A.copy() #np.array(A, dtype=np.float64) + else: + A_org = A.copy() #np.array(A, dtype=np.float64) + B = A + + cnt = 0 + while cnt < nsamp: + ir, ic = B.nonzero() + if pivots: + if np.random.uniform() < 0.5: + isrc1 = np.random.choice(pivots) + nz = B[:, isrc1].nonzero()[0] + if len(nz) == 0: + continue + itrg1 = np.random.choice(nz) + else: + itrg1 = np.random.choice(pivots) + nz = B[itrg1, :].nonzero()[0] + if len(nz) == 0: + continue + isrc1 = np.random.choice(nz) + # if-else + + itrg2, isrc2 = itrg1, isrc1 + while isrc1 == isrc2 and itrg1 == itrg2: + i2 = np.random.randint(0, ir.size) + itrg2, isrc2 = ir[i2], ic[i2] + else: + i1, i2 = 0, 0 + while i1 == i2: + i1, i2 = np.random.randint(0, ir.size, 2) + + itrg1, isrc1 = ir[i1], ic[i1] + itrg2, isrc2 = ir[i2], ic[i2] + + if noself: + if itrg2 == isrc1 or itrg1 == isrc2: + continue + + # Are the swapped links new? + if B[itrg2, isrc1] == 0 and B[itrg1, isrc2] == 0: + a, b = B[itrg1, isrc1], B[itrg2, isrc2] + + # Are the swapped links in the original network? + if A_org[itrg2, isrc1] == a and A_org[itrg1, isrc2] == b: + continue + + B[itrg2, isrc1], B[itrg1, isrc2] = a, b + B[itrg1, isrc1], B[itrg2, isrc2] = 0, 0 + cnt += 1 + else: + continue + # end of while + + if not inplace: + return B + + +def rand_flip(A, nsamp=10, pivots=None, inplace=False): + """Randomly flip the signs of connections. + + Parameters + ---------- + A : numpy.ndarray + Adjacency matrix (connection matrix). + nsamp : int, optional + Number of sampled connections to be flipped. + pivots : list, optional + Indices of pivot nodes + inplace : bool, optional + Modify the given adjacency matrix for rewiring. + + Returns + ------- + B : numpy.ndarray + The randomized matrix. + The reference of the given W is returned, when inplace is True. + """ + if not inplace: + B = A.copy() #np.array(A, dtype=np.float64) + else: + B = A + + ir, ic = B.nonzero() + if pivots: + iflip = np.random.choice(pivots, nsamp) + else: + iflip = np.random.randint(0, ir.size, nsamp) + + B[ir[iflip], ic[iflip]] *= -1 + return B + + +def rand_weights(W, lb=-3, ub=3, inplace=False): + """ Randomly sample the weights of connections in W from 10^(lb, ub). + + Parameters + ---------- + W : numpy.ndarray + Adjacency (connection) or weight matrix. + lb : float, optional + The 10's exponent for lower bound + inplace : bool, optional + Modify the given adjacency matrix for rewiring. + + Returns + ------- + B : numpy.ndarray + The randomly sampled weight matrix. + The reference of the given W is returned, when inplace is True. + """ + if not inplace: + B = np.array(W, dtype=np.float64) + else: + if not np.issubdtype(W.dtype, np.floating): + raise ValueError("W.dtype given to rand_weights should be " + "a float type, not %s"%(W.dtype)) + + B = W + # end of if-else + + ir, ic = B.nonzero() + weights_rand = 10 ** np.random.uniform(lb, ub, + size=(ir.size,)) + + B[ir, ic] = weights_rand*np.sign(B[ir, ic], dtype=np.float) + """The above code is equal to the following: + + for i in range(ir.size): + p, q = ir[i], ic[i] + B[p, q] = weights_rand[i] * np.sign(B[p, q], dtype=np.float) + """ + return B + + +def rand_structure(A, nswap=10, nflip=10, noself=True, pivots=None, inplace=False): + if not inplace: + B = A.copy() + else: + B = A + if nflip > 0: + B = rand_flip(B, nflip, pivots, inplace) + if nswap > 0: + B = rand_swap(B, nswap, noself, pivots, inplace) + return B + + +def get_akey(d): + """Get a key from a given dictionary. + It returns the first key in d.keys(). + + Parameters + ---------- + d : dict + Dictionary of objects. + + Returns + ------- + obj : object + First item of iter(d.keys()). + """ + return next(iter(d.keys())) + + +def get_avalue(d): + """Get a value from a given dictionary. + It returns the value designated by sfa.get_akey(). + + Parameters + ---------- + d : dict + Dictionary of objects. + + Returns + ------- + obj : object + First item of d[iter(d.keys())]. + """ + akey = next(iter(d.keys())) + return d[akey] \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/vis/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/vis/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,7 @@ + + + +from .utils import * + +__all__ = [] +__all__ += utils.__all__ \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/vis/sfv/__init__.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/vis/sfv/__init__.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,2 @@ +from .sfv import create_from_graphics +from .sfv import visualize_signal_flow diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/vis/sfv/sfv.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/vis/sfv/sfv.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,338 @@ +import numpy as np +import networkx as nx + +from qtpy.QtGui import QFont +from qtpy.QtGui import QColor +from qtpy.QtCore import QPointF +from qtpy.QtCore import Qt + +import sfv +from sfv.graphics import LabelClassFactory +from sfv.graphics import HeaderClassFactory + +import sfa + +NegHeader = HeaderClassFactory.create('HAMMER') +PosHeader = HeaderClassFactory.create('ARROW') +TextLabel = LabelClassFactory.create('TEXT_LABEL') + +def create_from_graphics(net, abbr=None, inputs=None, outputs=None): + """Create sfv.base.Data object from SIF file. + + Parameters + ---------- + net : sfv.graphics.Network + Network graphics object of SFV. + abbr : str + Abbreviation to denote this data object for the network. + inputs : dict, optional + Input information with default values + outputs : sequence, optional + Output information. + + Returns + ------- + obj : sfv.base.Data + Data object with the information of network topology. + + """ + + if not abbr: + abbr = net.name + + class __Data(sfa.base.Data): + def __init__(self): + self._abbr = abbr + self._name = self._abbr + + self._dg = sfv.to_networkx(net) + nodes = sorted(self._dg.nodes) + self._n2i = {name: i for i, name in enumerate(nodes)} + self._i2n = {idx: name for name, idx in self._n2i.items()} + + self._A = nx.to_numpy_array(self._dg, nodes).T + ir, ic = self._A.nonzero() + for i in range(ir.size): + r, c = ir[i], ic[i] + + src = self._i2n[c] + tgt = self._i2n[r] + sign = self._dg.edges[src, tgt]['SIGN'] + self._A[r, c] *= sign + + self._inputs = inputs + + if outputs: + self._outputs = outputs + + # The following members are not defined due to the lack of data. + self._df_conds = None + self._df_exp = None + self._df_ptb = None + self._has_link_perturb = False + self._names_ptb = None + self._iadj_to_idf = None + # end of def __init__ + # end of def class + + class_name = ''.join([c for c in abbr.title() if c.isalnum()]) + __Data.__name__ = class_name + "Data" + return __Data() + + +def visualize_signal_flow(net, F, act, + A, + n2i, + color_up=None, color_dn=None, + lw_min=1.0, + lw_max=10.0, + pct_link=90, + show_label=True, + show_act=True, + pct_act=50, + fmt_act='%.5f', + fix_node_size=False, + fix_act_label=False, + font=None): + """Visualize signal flow using SFV. + + SFV (Seamless Flow Visualization) is a light-weight, + programming-oriented python package to visualize + graphs and networks. + + This function is used in the SFV function, + 'execute(nav, net)', which is called in SFV program. + + Parameters + ---------- + net : sfv.graphics.Network + Network object that is given by sfv. + + F : numpy.ndarray + A matrix of signal flows. + It is usually calculated as W2*x1 - W1*x1, + where W is weight matrix and + x is a vector of activities at steady-state. + + act : numpy.ndarray + Change in the activities. It is usually calculated + as x2 - x1, where x is + the a vector of activities at steady-state. + + A : numpy.ndarray + Adjacency matrix of the network. + + n2i : dict + Name to index dictionary. + + color_up : numpy.ndarray or QtGui.QColor, optional + Default is blue (i.e., QColor(0, 0, 255)), + if it is None. + + color_dn : numpy.ndarray or QtGui.QColor, optional + Default is red (i.e., QColor(255, 0, 0)), + if it is None. + + lw_min : float, optional + Minimum link width, which is also used for unchanged flow. + + lw_max : float, optional + Maximum link width. + + pct_link : int, optional + Percentile of link width, which is used to set + the maximum value for setting link widths. + Default value is 90. + + show_label : bool, optional + Show node label or not. + + show_act : bool, optional + Show activity label or not. + + pct_act : int, optional + Percentile of activity, which is used to set + the maximum value for coloring nodes. + Default value is 50. + + fmt_act : str, optional + Format string for activity label. + + fix_node_size : bool, optional + Change the size of node or not. + Default is False. + + fix_act_label : bool, optional + Change the graphics of activity label or not. + The activity value is changed irrespective of + this parameter. Default is False. + + font : QFont, optional + Font for the name and activity labels. + + Returns + ------- + None + """ + + i2n = {val:key for key, val in n2i.items()} + color_white = np.array([255, 255, 255]) + + if not color_up: + color_up = np.array([255, 0, 0]) + elif isinstance(color_up, QColor): + color_up = np.array([color_up.red(), + color_up.green(), + color_up.blue()]) + else: + raise ValueError("color_up should be 3-dimensional np.ndarray " + "or QtGui.QColor") + + if not color_dn: + color_dn = np.array([0, 0, 255]) + elif isinstance(color_dn, QColor): + color_dn = np.array([color_dn.red(), + color_dn.green(), + color_dn.blue()]) + else: + raise ValueError("color_dn should be 3-dimensional np.ndarray " + "or QtGui.QColor") + + # Set the default font + if not font: + font = QFont('Arial', 10) + + abs_act = np.abs(act) + thr = np.percentile(abs_act, pct_act) + thr = 1 if thr == 0 else thr + + arr_t = np.zeros_like(act) + + for i, elem in enumerate(act): + t = np.clip(np.abs(elem)/thr, a_min=0, a_max=1) + arr_t[i] = t + + for iden, node in net.nodes.items(): + idx = n2i[iden] + + if not fix_node_size: + radius = 20 + node.width = node.height = radius + + fold = act[idx] + + if fold > 0: + color = color_white + arr_t[idx] * (color_up - color_white) + elif fold <= 0: + color = color_white + arr_t[idx] * (color_dn - color_white) + + color = np.int32(color) + node['FILL_COLOR'] = QColor(*color) + node['BORDER_WIDTH'] = 2 + node['BORDER_COLOR'] = QColor(40, 40, 40) + + if show_label: + _update_single_label_name(net, node, node.name, + fix_node_size, font) + + if show_act: + _update_single_label_activity(net, node, fold, + fix_act_label, + fmt_act, font) + else: + iden_label = '%s_act' % iden.upper() + if iden_label in net.labels: + net.remove_label(net.labels[iden_label]) + # end of for : update nodes and labels + + _update_links(net, A, F, i2n, pct_link, lw_min, lw_max) + + +def _update_links(net, A, F, i2n, pct_link, lw_min, lw_max): + log_flows = np.log10(np.abs(F[F.nonzero()])) + flow_max = log_flows.max() + flow_min = log_flows.min() + flow_thr = np.percentile(log_flows, pct_link) + + ir, ic = A.nonzero() #F.nonzero() + for i, j in zip(ir, ic): + tgt = i2n[i] + src = i2n[j] + f = F[i, j] + + link = net.nxdg[src][tgt]['VIS'] + + header_old = link.header + args_header = header_old.width, header_old.height, header_old.offset + if f > 0: + header = PosHeader(*args_header) + color_link = QColor(255, 10, 10, 70) + elif f < 0: + header = NegHeader(*args_header) + color_link = QColor(10, 10, 255, 70) + else: # When flow is zero, show the sign of the original link. + if A[i, j]>0: + header = PosHeader(*args_header) + elif A[i, j]<0: + header = NegHeader(*args_header) + else: + raise RuntimeError("The logic is abnormal.") + + color_link = QColor(100, 100, 100, 100) + + link.header = header + link['FILL_COLOR'] = color_link + + if f == 0: + link.width = lw_min + elif (flow_max - flow_min) == 0: + link.width = 0.5*(lw_max + lw_min) + else: + log_f = np.log10(np.abs(f)) + log_f = np.clip(log_f, a_min=flow_min, a_max=flow_thr) + lw = (log_f-flow_min)/(flow_max-flow_min)*(lw_max-lw_min) + lw_min + link.width = lw + + +def _update_single_label_name(net, node, name, + fix_node_size, font): + label_name = net.labels[name] + + lightness = QColor(node['FILL_COLOR']).lightness() + label_name['TEXT_COLOR'] = Qt.black + + label_name['FONT'] = font + + if lightness < 200: + label_name['TEXT_COLOR'] = Qt.white + label_name['FONT_BOLD'] = True + else: + label_name['TEXT_COLOR'] = Qt.black + label_name['FONT_BOLD'] = False + + rect = label_name.boundingRect() + label_name.setPos(-rect.width() / 2, -rect.height() / 2) # center + if not fix_node_size: + node.width = 1.1 * rect.width() + node.height = 1.1 * rect.height() + + +def _update_single_label_activity(net, node, x, fix_act_label, fmt, font): + iden = '%s_act' % node.iden.upper() + str_x = fmt % (x) + if iden not in net.labels: + label_act = TextLabel(node, text=str_x) + label_act.iden = iden + else: + label_act = net.labels[iden] + label_act.text = str_x % (x) + + if not fix_act_label: + label_act['FONT'] = font + label_act['TEXT_COLOR'] = QColor(20, 20, 20) + rect = label_act.boundingRect() + pos_x = node.width/2 + 0.5 + label_act.setPos(pos_x, -rect.height() / 2) + + if iden not in net.labels: + net.add_label(label_act) diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/sfa/vis/utils.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/sfa/vis/utils.py Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,184 @@ + +import numpy as np +import networkx as nx + + +__all__ = [ + 'compute_graphics', +] + + +def _rgb_to_hex(tup): + """Convert RGBA to #AARRGGBB + """ + tup = tuple(tup) + if len(tup) == 3: + return '#%02x%02x%02x' % tup + elif len(tup) == 4: + return '#%02x%02x%02x%02x' % (tup[3], tup[0], tup[1], tup[2]) + else: + raise ValueError("Array or tuple for RGB or RGBA should be given.") + + +def compute_graphics( + F, + act, + A, + n2i, + lw_min=1.0, + lw_max=10.0, + pct_link=90, + pct_act=50, + dg=None): + """Compute graphics of signal flow. + + This method performs a calculation for generating colors + of nodes and links for visualizing purpose. + + Parameters + ---------- + F : numpy.ndarray + A matrix of signal flows. + It is usually calculated as W2*x1 - W1*x1, + where W is weight matrix and + x is a vector of activities at steady-state. + + act : numpy.ndarray + Change in the activities. It is usually calculated + as x2 - x1, where x is + the a vector of activities at steady-state. + + A : numpy.ndarray + Adjacency matrix of the network. + + n2i : dict + Name to index dictionary. + + lw_min : float, optional + Minimum link width, which is also used for unchanged flow. + + lw_max : float, optional + Maximum link width. + + pct_link : int, optional + Percentile of link width, which is used to set + the maximum value for setting link widths. + Default value is 90. + + pct_act : int, optional + Percentile of activity, which is used to set + the maximum value for coloring nodes. + Default value is 50. + + dg : NetworkX.DiGraph, optional + Existing NetworkX object to contain graphics information + for visualizing nodes and links. + + Returns + ------- + dg : NetworkX.DiGraph + NetworkX object containing graphics information + for visualizing nodes and links. + + """ + + if not dg: + dg = nx.DiGraph() + dg.add_nodes_from(n2i) + + _compute_graphics_nodes(dg, n2i, act, pct_act) + _compute_graphics_links(dg, n2i, A, F, pct_link, lw_min, lw_max) + + return dg + + +def _compute_graphics_nodes(dg, n2i, act, pct_act): + color_white = np.array([255, 255, 255]) + color_up = np.array([255, 0, 0]) + color_dn = np.array([0, 0, 255]) + + abs_act = np.abs(act) + thr = np.percentile(abs_act, pct_act) + thr = 1 if thr == 0 else thr + + arr_t = np.zeros_like(act) + for i, elem in enumerate(act): + t = np.clip(np.abs(elem) / thr, a_min=0, a_max=1) + arr_t[i] = t + + for iden, idx in n2i.items(): + fold = act[idx] + + if fold > 0: + color = color_white + arr_t[idx] * (color_up - color_white) + elif fold <= 0: + color = color_white + arr_t[idx] * (color_dn - color_white) + + color = _rgb_to_hex(np.int32(color)) + + data = dg.nodes[iden] + data['FILL_COLOR'] = color + data['BORDER_WIDTH'] = 2 + data['BORDER_COLOR'] = _rgb_to_hex((40, 40, 40)) + # end of for + + +def _compute_graphics_links(dg, n2i, A, F, pct_link, lw_min, lw_max): + i2n = {val: key for key, val in n2i.items()} + + log_flows = np.log10(np.abs(F[F.nonzero()])) + flow_max = log_flows.max() + flow_min = log_flows.min() + flow_thr = np.percentile(log_flows, pct_link) + + ir, ic = A.nonzero() # F.nonzero() + for i, j in zip(ir, ic): + tgt = i2n[i] + src = i2n[j] + f = F[i, j] + + #link = net.nxdg[src][tgt]['VIS'] + dg.add_edge(src, tgt) + data = dg.edges[src, tgt] + + + #header_old = link.header + #args_header = header_old.width, header_old.height, header_old.offset + if f > 0: + sign_link = +1 # PosHeader(*args_header) + color_link = _rgb_to_hex((255, 10, 10, 70)) + elif f < 0: + sign_link = -1 # NegHeader(*args_header) + color_link = _rgb_to_hex((10, 10, 255, 70)) + else: # When flow is zero, show the sign of the original link. + if A[i, j] > 0: + sign_link = +1 # PosHeader(*args_header) + elif A[i, j] < 0: + sign_link = -1 # NegHeader(*args_header) + else: + raise RuntimeError("Abnormal state has been reached in " + "_compute_graphics_links.") + + color_link = _rgb_to_hex((100, 100, 100, 100)) + + + # If header exists, it should be removed, + # because the sign of signal flow can be different + # from the original sign of header. + if 'HEADER' in data: + data.pop('HEADER') + + data['SIGN'] = sign_link + data['FILL_COLOR'] = color_link + + if f == 0: + data['WIDTH'] = lw_min + elif (flow_max - flow_min) == 0: + data['WIDTH'] = 0.5 * (lw_max + lw_min) + else: + log_f = np.log10(np.abs(f)) + log_f = np.clip(log_f, a_min=flow_min, a_max=flow_thr) + lw = (log_f - flow_min) / (flow_max - flow_min) * ( + lw_max - lw_min) + lw_min + data['WIDTH'] = lw + diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/test-data/expressions.csv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/test-data/expressions.csv Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,5 @@ +,A_1,A_2,A_3,B_1,B_2,B_3 +A,2,1,2,6,7,6 +B,6,7,6,2,1,1 +D,-1,-1,-2,6,7,6 +E,2,1,2,6,7,6 \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/test-data/fvs.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/test-data/fvs.txt Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,3 @@ +name +C +D \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/test-data/internal_control.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/test-data/internal_control.txt Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,1 @@ +C \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/test-data/network.sif --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/test-data/network.sif Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,9 @@ +A activates B +B activates C +C activates D +C inhibits B +E inhibits D +E inhibits B +D activates F +D activates E +D inhibits D \ No newline at end of file diff -r f24d4892aaed -r 7e5c71b2e71f tools/myTools/bin/test-data/pipe.nf --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/myTools/bin/test-data/pipe.nf Wed Dec 22 16:00:34 2021 +0000 @@ -0,0 +1,332 @@ +#!/usr/bin/env nextflow + +params.expressions = "$baseDir/input_data/expressions.csv" +params.network = "$baseDir/input_data/network.sif" +params.samples = "$baseDir/input_data/samples.txt" +params.alpha = 0.9 +params.internal_control="$baseDir/input_data/internal_control.txt" +params.undesired = 'resistant' +params.desired = 'sensitive' +params.filter ="strict" + + +params.kmeans_min_val = 2 +params.kmeans_max_val = 10 + + +params.num_nodes = 4 // that have expression data +params.num_states = 1000 + + +params.mut = false + +process sfa_exp { + publishDir 'results', mode: 'copy', overwrite: true + input: + path "expressions.csv" from params.expressions + path "network.sif" from params.network + + output: + path 'attrs_exp.txt' into records_expattr + + script: + """ + SFA_exp_attr.py network.sif expressions.csv attrs_exp.txt + """ +} + +process get_exp_internal_control_nodes { + publishDir 'results', mode: 'copy', overwrite: true + + input: + path 'attrs_exp.txt' from records_expattr + path "internal_control.txt" from params.internal_control + + output: + path 'attrs_exp_internal_controls.txt' into records_exp_icns + + script: + """ + get_RONs.py attrs_exp.txt internal_control.txt + """ + +} + + + +process insilico_inits { + + output: + path 'init.txt' into records_insilicoinit + + script: + """ + generate_basal_states.py $params.num_states $params.num_nodes init.txt attr -1,0,1 + """ +} +process insilico { + publishDir 'results', mode: 'copy', overwrite: true + input: + path 'expressions.csv' from params.expressions + path 'network.sif' from params.network + path 'samples.txt' from params.samples + path 'init.txt' from records_insilicoinit + + output: + path 'attrs_insilico*' into records_insilico + + script: + + """ + SFA_insilico.py "network.sif" "expressions.csv" "samples.txt" init.txt $params.alpha + """ +} + +process getFVS { + publishDir 'results', mode: 'copy', overwrite: true + + input: + path 'network.sif' from params.network + + output: + path 'fvs.txt' into records_fvs + script: + """ + FVS_run.py network.sif + """ +} +process perturbation_inits { + publishDir 'results', mode: 'copy', overwrite: true + input: + path "fvs.txt" from records_fvs + // path 'fvs.txt' from records3a + + output: + path 'fvs_init.txt' into records_pert_inits + + + script: + """ + generate_perts.py fvs.txt fvs_init.txt pert -1,0,1 + """ +} +process sfa_perts { + publishDir 'results', mode: 'copy', overwrite: true + input: + path 'expressions.csv' from params.expressions + path 'network.sif' from params.network + path 'samples.txt' from params.samples + path "fvs.txt" from records_fvs + path 'fvs_init.txt' from records_pert_inits + + output: + path 'pert*' into records_perts + + script: + """ + SFA_virtscreen.py network.sif expressions.csv $params.undesired samples.txt fvs.txt fvs_init.txt $params.alpha + + """ +} + +process check_icns{ + publishDir 'results', mode: 'copy', overwrite: true + input: + path 'attrs_exp_internal_controls.txt' from records_exp_icns + path 'samples.txt' from params.samples + + output: + path 'internal_control_nodes_exp_attrs.pdf' into records_icn_check + + script: + """ + module load R/3.6.3 + icn_check1.R attrs_exp_internal_controls.txt samples.txt + """ + +} + +process kmeans_opt { + publishDir 'results', mode: 'copy', overwrite: true + + input: + path 'attrs_exp.txt' from records_expattr + path 'attrs_insilico*' from records_insilico + + output: + env ELBOW into records_elbow + env SILHOUETTE into records_silhouette + path '*.pdf' into records_silplots + path '*.png' into records_elbowplots + + shell: + """ + datasets=\$(ls -m attr* | sed 's/ //g') + echo \$datasets + export SILHOUETTE=\$(silhouette_metric.py \$datasets results/ $params.kmeans_max_val) + export ELBOW=\$(elbow_metric.py \$datasets results/ $params.kmeans_max_val) + """ +} +process kmeans { + // this one checks to see if clusters and phenotypes match up + publishDir 'results', mode: 'copy', overwrite: true + input: + val ELBOW from records_elbow + val SILHOUETTE from records_silhouette + path 'attrs_exp.txt' from records_expattr + path 'attrs_insilico*' from records_insilico + path 'samples.txt' from params.samples + + output: + path 'kmeans.txt' into records_kmeans + + script: + if (ELBOW==SILHOUETTE) + """ + datasets=\$(ls -m attr* | sed 's/ //g') + kmeans.py $ELBOW \$datasets + """ + + else if (ELBOW