# HG changeset patch
# User proteore
# Date 1545145129 18000
# Node ID edbb84a94a362cd3b8942186439a769e42f5b5dd

planemo upload commit bdd7e8a1f08c11db2a9f1b6db5535c6d32153b2b

diff -r 000000000000 -r edbb84a94a36 heatmap.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/heatmap.xml	Tue Dec 18 09:58:49 2018 -0500
@@ -0,0 +1,168 @@
+<tool id="heatmap" name="HeatMap" version="2018.12.12">
+    <description></description>
+    <requirements>
+        <requirement type="package" version="4.7.1">r-plotly</requirement>
+        <requirement type="package" version="0.14.1">r-heatmaply</requirement>
+        <requirement type="package" version="2.1.1">phantomjs</requirement>
+        <requirement type="package" version="2.2.1">pandoc</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+        Rscript $__tool_directory__/heatmap_viz.R 
+            --input='$file' 
+            --output="$file.name"  
+            --type='$output_type' 
+            --cols='$select_data_columns.cols' 
+            --row_names=$rownames 
+            --header='$header' 
+            --col_text_angle='$angle_col'
+            --dist="$distance"
+            --clust="$clustering"
+            --dendrogram="$dendrogram"
+
+    ]]></command>
+    <inputs>
+        <param name="file" type="data" format="txt,tabular" label="Select your file (table)" help="" />
+        <param name="header" type="boolean" checked="true" truevalue="true" falsevalue="false" label="Does your file contain a header?" />
+        <conditional name="select_data_columns">
+            <param name="enter_cols" type="select" label="Select columns or a range of columns containing expression values">
+                <option value="cols_number">Select columns to be used one by one</option>
+                <option value="cols_range">Select a range of columns to be used</option>
+            </param>
+            <when value="cols_number">
+                <param name="cols" type="text" label="Enter column number (separated by a comma)" help="For example : c3,c5,c7"/>
+            </when>
+            <when value="cols_range">
+                <param name="cols" type="text" label="Enter a range of column number, first and last column separated by ':'" help="For example : c2:c7"/>
+            </when>
+        </conditional>
+        <param name="rownames" type="text" value="c1" label="Enter column number containing row labels" help="for example : c1 if labels are in column n°1"/>
+        <param type="integer" name="angle_col" label="Angle of column labels" value="0" min="-90" max="90" />
+        <param name="clustering" type="select" label="Clustering method" value="average">
+            <option value="ward.D">Ward</option>
+            <option value="ward.D2">Ward2</option>
+            <option value="single">Single linkage (nearest neighbor)</option>
+            <option value="complete">Complete linkage (farthest neighbor)</option>
+            <option value="average" selected="true">Group average linkage (UPGMA)</option>
+            <option value="mcquitty">Simple average method (WPGMA)</option>
+            <!--option value="median">Median (WPGMC)</option>
+            <option value="centroid">Centroid (UPGMC)</option-->
+        </param>
+         <param name="distance" type="select" label="Distance measurement method" value="euclidean">
+            <option value="euclidean" selected="true">Euclidean</option>
+            <option value="pearson" selected="true">Pearson</option>
+            <option value="spearman">Spearman</option>
+            <option value="kendall">Kendall</option>
+            <option value="maximum">Maximum</option>
+            <option value="manhattan">Manhattan</option>
+            <option value="canberra">Canberra</option>
+            <option value="binary">Binary</option>
+            <option value="minkowski">Minkowski</option>
+        </param>
+        <param name="dendrogram" type="select" label="Apply clustering on :" value="both">
+            <option value="row">Rows</option>
+            <option value="column">Columns</option>
+            <option value="both" selected="true">Rows and columns</option>
+            <option value="none">None</option>
+        </param>
+        
+        <param name="output_type" type="select" label="Select output format">
+            <option value="html">html</option>
+            <option value="pdf">pdf</option>
+            <option value="jpeg">jpeg</option>
+            <option value="png">png</option>
+        </param>
+
+    </inputs>
+    <outputs>
+        <data name="output" format="html">
+            <discover_datasets pattern="(?P&lt;designation&gt;.+)\.html" ext="html" visible="true" assign_primary_output="true"/>
+                <filter>output_type=="html"</filter>
+        </data>
+        <data name="pdf" format="pdf">
+            <discover_datasets pattern="(?P&lt;designation&gt;.+)\.pdf" ext="pdf" visible="true" assign_primary_output="true"/>
+                <filter>output_type=="pdf"</filter>
+        </data>
+        <data name="jpeg" format="jpg">
+            <discover_datasets pattern="(?P&lt;designation&gt;.+)\.jpg" ext="jpg" visible="true" assign_primary_output="true"/>
+                <filter>output_type=="jpeg"</filter>
+        </data>
+        <data name="png" format="png">
+            <discover_datasets pattern="(?P&lt;designation&gt;.+)\.png" ext="png" visible="true" assign_primary_output="true"/>
+                <filter>output_type=="png"</filter>
+        </data>
+    </outputs>
+    <tests>
+        <test>
+            <output name="output" file="heatmap.html"/>
+        </test>
+    </tests>
+    <help><![CDATA[
+**Description**
+
+This tool allows users to generate, cluster and visualize expression-based heat maps from transcriptomic, proteomic and metabolomic experiments.
+It is based on heatmaply, an R package for easily creating interactive cluster heatmaps (see reference below)
+
+-----
+
+**Input**
+
+A file (tab-delimited) having a column with labels (e.g. a gene name, Uniprot accession number...) and colums with numerical value (intensities) for clustering.
+See table below for an example input file
+
+.. csv-table:: example of input file
+   :header: "Uniprot","iBAQ_CTR1","iBAQ_CTR2","iBAQ_CTR3","iBAQ_pTCN1","iBAQ_pTCN2","iBAQ_pTCN3"
+
+
+   "Q49AN9",17.4091970440807,16.0474907255521,14.9687330755858,21.8454060245779,18.9468529040903,21.2330797498008
+   "O00148",14.1001686145694,14.806777888004,15.3555560564928,17.2942797505583,18.2106568817514,16.9479095182613
+   "F5H6E2",15.0235503328855,16.6142578028388,20.5969569088489,14.6615767253835,17.9752549753108,20.4023495267791
+   "E9PPW7",18.0770953690935,15.312218369812,13.8048301075204,17.5522130063356,15.9664520099065,15.1597932646987
+   "O00483",17.4188205774495,16.783665086968,15.1589556127476,19.7398973660168,20.8648965533665,20.1781898785682
+   "O00571",12.9049717044645,16.717296441372,13.8708732177805,19.8879681981565,21.0815521014477,17.4710040202845
+
+-----
+
+**Parameters**
+
+"Select columns or a range of columns containing expression values": choose the columns to use to perform clustering and to create the heatmap.
+You can enter specific column number (e.g. c2,c5 will create a heatmap for column 2 and 5 corresponding to condition from the example file above ) or a range of columns to use (e.g. c2:c7 will consider all replicates of each condition in the example above)).
+
+"Enter column number containing row labels": enter the column number containing the rows labels (e.g. "c1" in the example above).
+
+"Angle of column labels": In case of long label name, you might want to incline the column labels for practical display.
+
+"Clustering method": methods for computing hierarchical clustering (six available) 
+
+"Distance measurement method": function used to compute the distance (dissimilarity) between both rows and columns (nine available). The options "pearson", "spearman" and "kendall" can
+be used to use correlation-based clustering.
+
+-----
+
+**Output**
+
+Default output is html; it allows browsing the heatmap in an interactive way (of note: for large file, display and interactivity can be altered)
+pdf, jpeg or png format are propsoed for static output.
+
+-----
+
+.. class:: infomark
+
+**Authors**
+Galili T, O'Callaghan A, Sidi J, Sievert C. heatmaply: an R package for creating interactive cluster heatmaps for online publishing. Bioinformatics. 2018. 34(9):1600-1602. doi: 10.1093/bioinformatics/btx657. PubMed PMID: 29069305
+
+-----
+
+.. class:: infomark
+
+**Galaxy integration**
+
+David Christiany, Florence Combes, Yves Vandenbrouck CEA, INSERM, CNRS, Grenoble-Alpes University, BIG Institute, FR
+
+Sandra Dérozier, Olivier Rué, Christophe Caron, Valentin Loux INRA, Paris-Saclay University, MAIAGE Unit, Migale Bioinformatics platform, FR
+
+This work has been partially funded through the French National Agency for Research (ANR) IFB project.
+
+Contact support@proteore.org for any questions or concerns about the Galaxy implementation of this tool.
+
+    ]]></help>
+</tool>
diff -r 000000000000 -r edbb84a94a36 heatmap_viz.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/heatmap_viz.R	Tue Dec 18 09:58:49 2018 -0500
@@ -0,0 +1,172 @@
+#!/usr/bin/Rscript
+
+suppressMessages(library('plotly',quietly = T))
+suppressMessages(library('heatmaply',quietly = T))
+
+#packageVersion('plotly')
+
+get_args <- function(){
+  
+  ## Collect arguments
+  args <- commandArgs(TRUE)
+  
+  ## Default setting when no arguments passed
+  if(length(args) < 1) {
+    args <- c("--help")
+  }
+  
+  ## Help section
+  if("--help" %in% args) {
+    cat("Pathview R script
+    Arguments:
+      --help                  Print this test
+      --input                 path of the input  file (must contains a colum of uniprot and/or geneID accession number)
+      --output                Output file
+      --type                  type of output file, could be html, pdf, jpg or png
+      --cols                  Columns to use for heatmap, exemple : '3:8' to use columns from the third to the 8th
+      --row_names             Column which contains row names
+      --header                True or False
+      --col_text_angle        Angle of columns label ; from -90 to 90 degres
+      --dist_fun              function used to compute the distance
+
+      Example:
+      ./heatmap_viz.R --input='dat.nucl.norm.imputed.tsv' --output='heatmap.html' --cols='3:8' --row_names='2' --header=TRUE --col_text_angle=0 \n\n")
+    
+    q(save="no")
+  }
+  
+  parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
+  argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
+  args <- as.list(as.character(argsDF$V2))
+  names(args) <- argsDF$V1
+  
+  return(args)
+}
+
+read_file <- function(path,header){
+  file <- try(read.csv(path,header=header, sep="\t",stringsAsFactors = FALSE, quote="",fill=TRUE,check.names = F),silent=TRUE)
+  if (inherits(file,"try-error")){
+    stop("File not found !")
+  }else{
+    return(file)
+  }
+}
+
+#convert a string to boolean
+str2bool <- function(x){
+  if (any(is.element(c("t","true"),tolower(x)))){
+    return (TRUE)
+  }else if (any(is.element(c("f","false"),tolower(x)))){
+    return (FALSE)
+  }else{
+    return(NULL)
+  }
+}
+
+#remove remaining quote 
+#only keep usefull columns
+#remove lines with at least one empty cell in a matrix between two defined columns
+clean_df <- function(mat,cols,rownames_col){
+  uto = mat[,cols]
+  uto <- as.data.frame(apply(uto,c(1,2),function(x) gsub(",",".",x)))
+  uto <- as.data.frame(apply(uto,c(1,2),function(x) {ifelse(is.character(x),as.numeric(x),x)}))
+  rownames(uto) <- mat[,rownames_col]
+  #bad_lines <- which(apply(uto, 1, function(x) any(is.na(x))))
+  #if (length(bad_lines) > 0) {
+  #  uto <- uto[- bad_lines,]
+  #  print(paste("lines",bad_lines, "has been removed: at least one non numeric content"))
+  #}
+  return(uto)
+}
+
+get_cols <-function(input_cols) {
+  input_cols <- gsub("c","",input_cols)
+  if (grepl(":",input_cols)) {
+    first_col=unlist(strsplit(input_cols,":"))[1]
+    last_col=unlist(strsplit(input_cols,":"))[2]
+    cols=first_col:last_col
+  } else {
+    cols = as.integer(unlist(strsplit(input_cols,",")))
+  }
+  return(cols)
+}
+
+#get args
+args <- get_args()
+
+#save(args,file="/home/dchristiany/proteore_project/ProteoRE/tools/heatmap_viz/args.rda")
+#load("/home/dchristiany/proteore_project/ProteoRE/tools/heatmap_viz/args.rda")
+
+header=str2bool(args$header)
+output <- rapply(strsplit(args$output,"\\."),c) #remove extension
+output <- paste(output[1:length(output)-1],collapse=".")
+output <- paste(output,args$type,sep=".")
+cols = get_cols(args$cols)
+rownames_col = as.integer(gsub("c","",args$row_names))
+if (length(cols) <=1 ){
+  stop("You need several colums to build a heatmap")
+}
+dist=args$dist
+clust=args$clust
+dendrogram=args$dendrogram
+
+#cleaning data
+uto <- read_file(args$input,header)
+uto <- clean_df(uto,cols,rownames_col)
+uto <- uto[rowSums(is.na(uto)) != ncol(uto), ]  #remove emptylines
+
+if (header) {
+  col_names = names(data)
+} else {
+  col_names = cols
+}
+
+#building heatmap
+if (dist %in% c("pearson","spearman","kendall")){
+  heatmaply(uto, file=output, margins=c(100,50,NA,0), plot_method="plotly", labRow = rownames(uto), labCol = col_names, distfun=dist, 
+            hclust_method = clust, dendrogram = dendrogram, grid_gap = 0,cexCol = 1, column_text_angle = as.numeric(args$col_text_angle), 
+            width = 1000, height=1000, colors = c('blue','green','yellow','red'))
+} else {
+  heatmaply(uto, file=output, margins=c(100,50,NA,0), plot_method="plotly", labRow = rownames(uto), labCol = col_names, dist_method = dist, 
+          hclust_method = clust, dendrogram = dendrogram, grid_gap = 0,cexCol = 1, column_text_angle = as.numeric(args$col_text_angle), 
+          width = 1000, height=1000, colors = c('blue','green','yellow','red'))
+}
+
+####heatmaply
+
+simulateExprData <- function(n, n0, p, rho0, rho1){ row 
+  # n: total number of subjects 
+  # n0: number of subjects with exposure 0 
+  # n1: number of subjects with exposure 1 
+  # p: number of genes 
+  # rho0: rho between Z_i and Z_j for subjects with exposure 0 
+  # rho1: rho between Z_i and Z_j for subjects with exposure 1
+  
+  # Simulate gene expression values according to exposure 0 or 1, 
+  # according to a centered multivariate normal distribution with 
+  # covariance between Z_i and Z_j being rho^|i-j| 
+  n1 <- n - n0 
+  times <- 1:p
+  H <- abs(outer(times, times, "-")) 
+  V0 <- rho0^H 
+  V1 <- rho1^H 
+  
+  # rows are people, columns are genes 
+  genes0 <- MASS::mvrnorm(n = n0, mu = rep(0,p), Sigma = V0) 
+  genes1 <- MASS::mvrnorm(n = n1, mu = rep(0,p), Sigma = V1) 
+  genes <- rbind(genes0,genes1) 
+  return(genes)
+}
+
+#genes <- simulateExprData(n = 50, n0 = 25, p = 100, rho0 = 0.01, rho1 = 0.95)
+
+#heatmaply(genes, k_row = 2, k_col = 2)
+
+#heatmaply(cor(genes), k_row = 2, k_col = 2)
+
+
+
+
+
+
+
diff -r 000000000000 -r edbb84a94a36 test-data/dat.nucl.norm.imputed.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/dat.nucl.norm.imputed.tsv	Tue Dec 18 09:58:49 2018 -0500
@@ -0,0 +1,318 @@
+id	Uniprot	iBAQ_CTR1	iBAQ_CTR2	iBAQ_CTR3	iBAQ_pTCN1	iBAQ_pTCN2	iBAQ_pTCN3
+85	Q49AN9	17.4091970440807	16.0474907255521	14.9687330755858	21.8454060245779	18.9468529040903	21.2330797498008
+89	O00148	14.1001686145694	14.806777888004	15.3555560564928	17.2942797505583	18.2106568817514	16.9479095182613
+90	F5H6E2	15.0235503328855	16.6142578028388	20.5969569088489	14.6615767253835	17.9752549753108	20.4023495267791
+92	E9PPW7	18.0770953690935	15.312218369812	13.8048301075204	17.5522130063356	15.9664520099065	15.1597932646987
+101	O00483	17.4188205774495	16.783665086968	15.1589556127476	19.7398973660168	20.8648965533665	20.1781898785682
+105	O00571	12.9049717044645	16.717296441372	13.8708732177805	19.8879681981565	21.0815521014477	17.4710040202845
+121	O14880	15.7795152357562	16.9056638034428	13.337884572062	17.7044082430845	18.2623291335539	15.6013730432451
+131	O15020	13.2364391435825	16.0875734675595	17.3471072965076	11.3932084948267	17.5224254853992	16.0210381525735
+153	O43172	14.0986553604854	14.9471566023657	14.6745546404104	20.0511708977192	20.4174260826947	17.9102233324039
+154	Q5SZU1	14.8343706352117	14.9336932868586	15.863517960295	16.5553115638733	13.2384247130195	15.6278162199574
+155	O43181	16.9585411843144	18.0253414246133	14.9688011176101	18.4506581478283	18.060715749432	17.1508218902826
+162	O43395	12.7815915377713	13.2570182327942	14.2151159490732	18.7982994745559	19.0606274400876	17.2081917897096
+170	O43707	14.4053518406046	14.9335246225864	16.7473467820106	13.9490316421179	12.6512953774468	12.488509829076
+172	O43795	16.6433848083358	15.6311118029749	20.0692252954892	14.1953551525139	16.1740085549157	19.5449370166335
+173	O43809	18.8780918275901	15.5053991792138	10.6705596934982	20.0325214098905	19.0562345939939	18.8788858733645
+179	O43920	17.8555846173049	16.0317021406993	15.4193073529273	17.0748503358331	18.6638138225965	15.2226042707806
+198	C9JZR2	12.7604492273804	15.1102454594255	13.2719163803705	14.2208765008612	16.2566097942392	16.1902621154649
+212	O75152	15.6680820455343	15.0519438286799	11.6759710588754	17.1958169834068	18.1627806617006	17.7637389118576
+215	O75251	16.0380636771286	17.3118540988743	14.8408713638196	18.2933996427914	19.2469406522803	17.7901518813423
+216	O75306	17.7731476104228	17.0436820947162	14.3216198760797	18.0682714348397	17.7038323287087	17.4649529913645
+220	O75369	10.5809642077475	15.1397204878621	14.9763339043522	14.4145798670977	13.0731660922891	10.2481720848799
+229	O75489	17.9752051061699	18.5422711615252	15.8278574543733	17.7647633731306	19.0794616174147	14.6897924274007
+234	O75533	10.7492978788409	11.6906933545874	12.3558422513063	16.0541183067786	18.1338889547966	18.1993226892225
+239	O75643	9.56772460294106	14.5115371196252	12.6720983186065	19.6873642615915	19.968822501587	19.1004765946838
+242	O75746	17.9078927655008	11.3730435111001	10.0131616287334	18.185826994594	13.1691675113752	15.1221180214157
+256	O94776	11.3490449754863	13.197432796574	11.5083281047808	14.5931066465682	16.4802205283196	16.5456932336907
+260	O94832	16.7310585463838	17.2925285377812	19.8521284451617	14.8901614386487	18.1901635960251	19.1007398885939
+265	O94906	14.6213840503359	13.6515629350167	9.1203482682031	18.6422967033089	19.6520809706827	18.5888550328822
+273	O95168	20.5206925179917	20.8313406769694	16.0118017974777	17.8571177393245	20.8185612151699	19.4884749760408
+280	A0A087WXC5	18.2572586904791	18.4546534154334	15.203018002337	19.220193815815	19.5429473866248	14.1031742766836
+286	O95425	14.8220020167805	14.4200178489881	18.0635120378048	16.0513169608008	14.8920412166631	16.966836273813
+300	P00403	19.0735282785645	17.7464110488726	17.4028764600704	17.9152591108248	17.7078602604404	17.7924135731373
+306	P02545	13.5624248927807	13.5988458131129	15.373527755546	17.2622702956547	17.0214060903224	15.5358419795327
+312	P04637	15.710400343291	14.6264097916308	17.5410808663017	17.7039400941162	18.245258057954	16.6148965562149
+315	P04843	11.1501180465525	14.5410139104238	15.1142096535426	13.9070108353945	14.9946317101858	15.6177627411281
+316	P04844	13.2205893394573	9.80631645725701	13.8106499648954	15.8896811504861	15.1363155605214	16.954069444608
+317	P04899	14.1089416574541	14.3632665224233	14.4355224479383	15.3586328365907	14.8002176344558	13.3866559138586
+320	P05141	17.1042396344764	18.4565875995602	17.0205670038887	22.4639957989801	22.4564604418619	19.4435442922826
+325	P05386	15.546872751224	15.9125363950717	17.4649155606126	22.7101892779655	22.3561454677901	25.693290301184
+326	P05387	18.9618175201485	18.3136745344987	17.0547365676808	25.4576432928542	23.2593536747674	23.7719309477717
+327	P05388	17.5371190592929	19.4439847832436	18.5385618243395	24.480863487145	24.1234163147804	25.4025376309194
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diff -r 000000000000 -r edbb84a94a36 test-data/heatmap.html
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/heatmap.html	Tue Dec 18 09:58:49 2018 -0500
@@ -0,0 +1,20 @@
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