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Commit message:
"planemo upload for repository https://github.com/workflow4metabolomics/tools-metabolomics/blob/master/tools/correlation_analysis/ commit 35a01e4ef59a91f43d0b1de1d08db29dcc7aae1e" |
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abims_correlation_analysis.xml correlation_analysis.r static/images/MetaboAnalysisCorrelation_workflow.png test-data/in_DM1.tabular test-data/in_SM1.tabular test-data/in_VM1.tabular test-data/out_VM1.tabular test-data/out_corr1.tabular test-data/out_sif1.tabular |
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| diff -r 000000000000 -r 58997c28b268 abims_correlation_analysis.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/abims_correlation_analysis.xml Tue Jan 19 16:41:47 2021 +0000 |
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| b'@@ -0,0 +1,322 @@\n+<tool id="correlation_analysis" name="Metabolites Correlation Analysis" version="1.0.1+galaxy0" >\n+\n+ <description>to highlight ion correlations considering PC-groups</description>\n+\n+ <requirements>\n+ <requirement type="package" version="1.1_5">r-batch</requirement>\n+ <requirement type="package" version="0.8.8">r-reshape</requirement>\n+ <requirement type="package" version="7.3_53">r-mass</requirement>\n+ </requirements>\n+\n+ <command detect_errors=\'exit_code\'>\n+ Rscript \'$__tool_directory__/correlation_analysis.r\'\n+\n+ #if $cond_input_type.select_input_type == "select_input_from_w4m" and $cond_input_type.cond_function.select_funtion == "sort_only" :\n+ sorting 1 variable_metadata \'$cond_input_type.variableMetadata\'\n+ data_matrix \'$cond_input_type.dataMatrix\'\n+ sample_metadata \'$cond_input_type.sampleMetadata\'\n+ corrdel 0\n+ param_correlation ""\n+ param_cytoscape ""\n+ matrix_corr 0\n+ user_matrix_corr ""\n+ corr_method ""\n+ #end if\n+ #if $cond_input_type.select_input_type == "select_input_from_w4m" and $cond_input_type.cond_function.select_funtion == "sort_and_corr" :\n+ sorting 1\n+ variable_metadata \'$cond_input_type.variableMetadata\'\n+ data_matrix \'$cond_input_type.dataMatrix\'\n+ sample_metadata \'$cond_input_type.sampleMetadata\'\n+ corrdel 1\n+ param_correlation $cond_input_type.cond_function.param_correlation\n+ param_cytoscape $cond_input_type.cond_function.param_cytoscape\n+ matrix_corr 0\n+ user_matrix_corr ""\n+ corr_method $cond_input_type.cond_function.corr_method\n+ #end if\n+ ##Create correlation matrix from a user table file.##\n+ #if $cond_input_type.select_input_type == "select_input_other" :\n+ sorting 0\n+ variable_metadata ""\n+ data_matrix ""\n+ sample_metadata ""\n+ corrdel 0\n+ param_correlation ""\n+ param_cytoscape $cond_input_type.param_cytoscape\n+ matrix_corr 1\n+ user_matrix_corr \'$cond_input_type.user_matrix_corr\'\n+ corr_method $cond_input_type.corr_method\n+ #end if\n+\n+ </command>\n+\n+ <inputs>\n+\n+\n+ <conditional name="cond_input_type" >\n+ <param name="select_input_type" type="select" label="Choice of your input files" help="" >\n+ <option value="select_input_from_w4m" selected="true">Files from the metabolomic workflow</option>\n+ <option value="select_input_other" >Your table file</option>\n+ </param>\n+ <when value="select_input_from_w4m">\n+ <param name="dataMatrix" type="data" label="Data matrix" format="tabular" help="dataMatrix file from the CAMERA.annotate step for example" />\n+ <param name="sampleMetadata" type="data" label="Sample metadata" format="tabular" help="sampleMetadata file from the xcms.xcmsSet step for example" />\n+ <param name="variableMetadata" type="data" label="Variable metadata" format="tabular" help="variableMetadata file from the CAMERA.annotate step for example" />\n+ <conditional name="cond_function" >\n+ <param name="select_funtion" type="select" label="Function to be used" help="" >\n+ <option value="sort_only" selected="true">Sorting your table</option>\n+ <option value="sort_and_corr" >Sorting your table and doing correlation analysis</option>\n+ </param>\n+ <when value="sort_only" />\n+ <when value="sort_and_corr">\n+ <param name="corrdel" type="hidden" value="1"/>\n+ <param name="param_correlation" type="float" label="Correlation threshold for pcgroup" value="0.60" help="Threshold valu'..b'po.tsv | Tabular|\n++---------------------------+--------------------------------------+--------+\n+\n+\n+\n+**General schema of the metabolomic workflow**\n+\n+.. image:: MetaboAnalysisCorrelation_workflow.png\n+\n+-----------\n+Input files\n+-----------\n+\n++--------------------------------+------------+\n+| Parameter: label | Format |\n++================================+============+\n+| Data matrix | Tabular |\n++--------------------------------+------------+\n+| Sample metadata | Tabular |\n++--------------------------------+------------+\n+| Variable metadata | Tabular |\n++--------------------------------+------------+\n+| User table file | Tabular |\n++--------------------------------+------------+\n+\n+\n+----------\n+Parameters\n+----------\n+\n+**Choice of your input files**\n+\n+ | **variableMetadata**\n+ |\n+ | For example, the "variableMetadata.tsv" tabular file generated by the CAMERA.annotate step of the workflow.\n+ | This table must contain in particular two columns named "**pcgroup**" and "**rt**" (it is case-sensitive).\n+ |\n+ | **dataMatrix**\n+ |\n+ | For example, the "dataMatrix.tsv" tabular file generated by the CAMERA.annotate step of the workflow.\n+ |\n+ | **sampleMetadata**\n+ |\n+ | For example, the tabular file with the samples metadata generated by the xcmsSet step: one sample per line and at least two columns: ids and one variable.\n+ |\n+ | **user table**\n+ |\n+ | Tabular containing intensities where your variables (metabolites) are in columns (for example a transposition of your datamatrix file)\n+\n+**Correlation threshold for pcgroup** *(metabolomic workflow only)*\n+\n+The threshold value that will determine if two metabolites are correlated inside a same pcgroup after the creation of the global correlation matrix.\n+If you do not want to use the intra-pcgroup filter (see "corrdel" function in the description section), put this threshold to 1 and all ions will be kept.\n+\n+**Choice of the correlation method**\n+\n+Choose the correlation method (pearson, kendall or spearman).\n+\n+**Cytoscape correlation threshold**\n+\n+Choose a threshold value for selecting edges (i.e. correlations between metabolites) that will be exported to the Cytoscape sif format file.\n+\n+------------\n+Output files\n+------------\n+\n+\n+\n+**sorted_variableMetadata.tsv** *(metabolomic workflow only)*\n+\n+ | A tabular file which:\n+ | 1) contains the original variable metadata columns\n+ | 2) is sorted by the pcgroup column\n+ | 3) contains a new column "signal_moy" (mean of all the signal values of the metabolites by sample)\n+ | 4) (depending of parameters) contains a "suppress" column\n+\n+**correlation_matrix_selected.tsv** *(metabolomic workflow only)*\n+\n+ | A correlation matrix containing only the metabolites selected in each pcgroup (metabolites tagged as "DEL" in "suppress" column are removed),\n+ | completed with two columns "rtmed" and "signal_moy".\n+\n+**sif_table.tsv**\n+\n+ | A tabular file (three columns: Metabolite1, Correlation coefficient, Metabolite 2) that can be used in Cytoscape.\n+\n+------\n+\n+.. class:: infomark\n+\n+The output **selected_metabolites_dataMatrix.tsv** is a tabular file. You can continue your analysis using it for example in the following statistical tools:\n+ | Hierarchical Clustering\n+ | ANOVA\n+\n+\n+---------------------------------------------------\n+\n+Changelog/News\n+--------------\n+\n+\n+**Version 1.0.1+galaxy0 - 10/12/2020**\n+\n+- Update of some of the outputs\' formats to match standard W4M table format\n+- Standard output (stdout) log improvement\n+- Change of testing data for faster job running for tests\n+- Fix: generation of the outputs for "Your table file" option\n+\n+**Version 1.0.1 - 20/09/2016**\n+\n+- TEST: refactoring to pass functional test using conda dependencies\n+- Help improvement\n+\n+\n+**Version 20141118 - 18/11/2014**\n+\n+ </help>\n+ <citations>\n+ <citation type="doi">10.1093/bioinformatics/btu813</citation>\n+ </citations>\n+\n+</tool>\n' |
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| diff -r 000000000000 -r 58997c28b268 correlation_analysis.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/correlation_analysis.r Tue Jan 19 16:41:47 2021 +0000 |
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| b'@@ -0,0 +1,213 @@\n+#!/usr/local/public/bin/Rscript --vanilla --slave --no-site-file\n+#For questions: Antoine Gravot (Protocole conception) and Misharl Monsoor (for galaxy wrapper and R script).\n+\n+#Load the different libraries\n+library(batch) #necessary for parseCommandArgs function\n+library(reshape) #necessary for using melt function\n+library(MASS) # necessary for using the write.matrix()\n+#interpretation of arguments given in command line as an R list of objects\n+list_arguments <- parseCommandArgs(evaluate = FALSE)\n+\n+cat("\\nJob starting time:\\n", format(Sys.time(), "%a %d %b %Y %X"),\n+ "\\n\\n--------------------------------------------------------------------",\n+ "\\nParameters used in \'Metabolites Correlation Analysis\':\\n\\n")\n+print(list_arguments)\n+cat("--------------------------------------------------------------------\\n\\n")\n+\n+#The main function of this script that will execute all the other functions\n+\n+\n+main_function <- function(sorting, variable_metadata, data_matrix, sample_metadata, corrdel, param_correlation, param_cytoscape, matrix_corr, user_matrix_corr, corr_method) {\n+\n+\n+ if (sorting == 1) {\n+ ####Executing the sorting function####\n+ cat("\\nExecuting the sorting function\\n")\n+ #Read the tsv annotateDiffreport file and don\'t modify the columns name (check.names = FALSE)\n+ variable_metadata_input <- read.csv(variable_metadata, header = T, sep = "\\t", dec = ".", check.names = FALSE)\n+ data_matrix_input <- read.csv(data_matrix, header = T, sep = "\\t", dec = ".", check.names = FALSE)\n+ first_column_variable <- toString(names(variable_metadata_input)[1])\n+ first_column_datamatrix <- toString(names(data_matrix_input)[1])\n+ #@TODO merge\n+ input_tsv <- cbind(variable_metadata_input, data_matrix_input[, !(colnames(data_matrix_input) %in% c(first_column_datamatrix))])\n+ #Load the sample.info from the xcmsSet\n+ sample_metadata_info_tsv <- read.table(sample_metadata, header = T, sep = "\\t", dec = ",", check.names = FALSE)\n+ #Extract the samples name from the sample.info file generated from the xcmsSet step in ABIMS Workflow4Metabo.\n+ samples_name <- as.vector(t(sample_metadata_info_tsv[1]))\n+ output_tsv <- sorting(input_tsv, samples_name)\n+ # output now if corrdel == 0\n+ if (corrdel == 0) {\n+ output_tsv_vm <- output_tsv[, which(!(colnames(output_tsv) %in% samples_name))]\n+ write.table(output_tsv_vm[, c(3:ncol(output_tsv_vm), 2, 1)], sep = "\\t", quote = FALSE, col.names = TRUE, row.names = FALSE, file = "sorted_table.tsv")\n+ }\n+\n+ }\n+ if (corrdel == 1) {\n+ cat("\\nExecuting the corr_matrix_del function\\n")\n+ corr_matrix_del(output_tsv, samples_name, param_correlation, param_cytoscape, corr_method)\n+ }\n+\n+ if (matrix_corr == 1) {\n+ cat("\\nExecuting the corr_matrix function\\n")\n+ corr_matrix_user(user_matrix_corr, param_cytoscape, corr_method)\n+\n+ }\n+\n+}\n+\n+#The sorting function will sort the dataframe by rt column.\n+#Then it creates a "signal_moy" column which contains the mean values of the signal values of the sample by row.\n+#It finally creates a table tsv format "sorted_table.tsv".\n+\n+sorting <- function(input_tsv, samples_name) {\n+\n+ #Sort by rt column\n+ new_input <- input_tsv[order(input_tsv$rt), ]\n+ #Compute the mean operation of all the signal values of the sample by row, and put the results in a new column "signal_moy"\n+ new_input["signal_moy"] <- data.frame(Means = rowMeans(new_input[, colnames(new_input) %in% samples_name]))\n+ #Rearrange the data frame in order to have the columns "signal_moy" and "pcgroup" at the beginning of the table\n+ new_input <- cbind(new_input["signal_moy"], new_input["pcgroup"], new_input[, !(colnames(new_input) %in% c("signal_moy", "pcgroup"))])\n+ #Sort the "signal_moy" column data frame by pcgroup\n+ new_input <- new_input[order(new_input$pcgroup, -new_input$signal_moy), ]\n+ '..b'e <- selected_metabolite_dataframe[, colnames(selected_metabolite_dataframe) %in% metabolites_selected_list]\n+\n+ #Export to siff table format for visualization in cytoscape for the selected metabolites\n+ siff_table <- melt(selected_metabolite_dataframe[, !colnames(selected_metabolite_dataframe) %in% c("pcgroup", "rt", "signal_moy")])\n+ #Remove the values equal to 1 (correlation between two metabolite identical)\n+ siff_table <- siff_table[siff_table$value != 1, ]\n+ #Keep only the values corresponding to the param_cytoscape\n+ siff_table <- siff_table[siff_table$value >= param_cytoscape, ]\n+ #Change the order of the columns\n+ siff_table <- cbind(siff_table[first_column], siff_table["value"], siff_table["variable"])\n+\n+\n+\n+ #Join the two datasets to keep only the selected metabolite, with all the information about the intensity value for statistics analysis in the next step of the workflow.\n+ joined_dataframe <- merge(statmatrix2, selected_metabolite_dataframe[first_column], by = first_column)\n+ #Order by the signal intensity\n+ joined_dataframe <- joined_dataframe[order(-joined_dataframe$signal_moy), ]\n+ #Transposition of the dataframe\n+ joined_dataframe <- joined_dataframe[, !(colnames(joined_dataframe) %in% c("signal_moy"))]\n+\n+\n+ #Write the different tables into files\n+ write.table(selected_metabolite_dataframe, sep = "\\t", quote = FALSE, col.names = TRUE, row.names = FALSE, file = "correlation_matrix_selected.tsv")\n+ write.table(siff_table, sep = "\\t", quote = FALSE, col.names = FALSE, row.names = FALSE, file = "siff_table.tsv")\n+ output_tsv_vm <- output_tsv[, which(!(colnames(output_tsv) %in% samples_name))]\n+ output_tsv_vm <- data.frame(output_tsv_vm[, c(3:ncol(output_tsv_vm), 2, 1)], ori.or = seq_len(nrow(output_tsv_vm)))\n+ output_tsv_vm <- merge(x = output_tsv_vm, y = new_dataframe[, c(3, which(colnames(new_dataframe) == "suppress"))], by.x = 1, by.y = 1, sort = FALSE)\n+ output_tsv_vm <- output_tsv_vm[order(output_tsv_vm$ori.or), ][, -c(which(colnames(output_tsv_vm) == "ori.or"))]\n+ write.table(output_tsv_vm, sep = "\\t", quote = FALSE, col.names = TRUE, row.names = FALSE, file = "sorted_table.tsv")\n+\n+}\n+\n+\n+corr_matrix_user <- function(user_matrix_corr, param_cytoscape, corr_method) {\n+ #read the input table\n+ input_tsv <- read.csv(user_matrix_corr, header = T, sep = "\\t", dec = ".", check.names = FALSE)\n+ n <- input_tsv$HD\n+ #transpose all but the first column (name)\n+ statmatrix_corr <- as.data.frame(t(input_tsv[, -1]))\n+ #Rename the columns of the dataframe "statmatrix"\n+ colnames(statmatrix_corr) <- n\n+ #Do the cor step, with the transposed statmatrix.\n+ corr_transpo <- cor(t(as.matrix(statmatrix_corr)), method = corr_method)\n+ #Write the matrix to a tsv file\n+ write.table(corr_transpo, sep <- "\\t", quote = FALSE, col.names = NA, file = "correlation_matrix.tsv")\n+ #Export to siff table format for visualization in cytoscape for the selected conditions\n+ siff_table <- melt(corr_transpo)\n+ #Remove the values equal to 1 (correlation between two metabolite identical)\n+ siff_table <- siff_table[siff_table$value != 1, ]\n+ #Keep only the values corresponding to the param_cytoscape\n+ siff_table <- siff_table[siff_table$value >= param_cytoscape, ]\n+ #Change the order of the columns\n+ siff_table <- cbind(Node1 = siff_table["X1"], interaction = siff_table["value"], Node2 = siff_table["X2"])\n+ #Write the siff table\n+ write.table(siff_table, sep = "\\t", quote = FALSE, col.names = FALSE, row.names = FALSE, file = "siff_table.tsv")\n+\n+}\n+do.call(main_function, list_arguments)\n+\n+\n+cat("\\n--------------------------------------------------------------------",\n+ "\\nInformation about R (version, Operating System, attached or loaded packages):\\n\\n")\n+sessionInfo()\n+cat("--------------------------------------------------------------------\\n",\n+ "\\nJob ending time:\\n", format(Sys.time(), "%a %d %b %Y %X"))\n' |
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| Binary file static/images/MetaboAnalysisCorrelation_workflow.png has changed |
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| diff -r 000000000000 -r 58997c28b268 test-data/in_DM1.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/in_DM1.tabular Tue Jan 19 16:41:47 2021 +0000 |
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|
| b |
| diff -r 000000000000 -r 58997c28b268 test-data/in_SM1.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/in_SM1.tabular Tue Jan 19 16:41:47 2021 +0000 |
| b |
| @@ -0,0 +1,50 @@ +sampleMetadata batch injectionOrder sampleType Time Group +Sample1 s1 2 sample 1 A +Sample2 s1 3 sample 2 B +Sample3 s1 17 sample 4 C +Sample4 s1 20 sample 3 B +Sample5 s1 47 sample 1 A +Sample6 s1 48 sample 2 B +Sample7 s2 59 sample 4 A +Sample8 s2 64 sample 2 C +Sample9 s2 86 sample 4 C +Sample10 s2 87 sample 1 A +Sample11 s2 96 sample 3 B +Sample12 s2 97 sample 1 C +Sample13 s3 105 sample 2 B +Sample14 s3 106 sample 4 A +Sample15 s3 107 sample 3 D +Sample16 s3 113 sample 1 A +Sample17 s3 114 sample 2 C +Sample18 s3 120 sample 1 B +Sample19 s3 126 sample 4 A +Sample20 s4 155 sample 2 B +Sample21 s4 158 sample 1 C +Sample22 s4 159 sample 3 A +Sample23 s4 171 sample 4 B +Sample24 s4 172 sample 2 A +Sample25 s4 173 sample 1 C +Sample26 s4 177 sample 4 F +Sample27 s4 178 sample 1 F +Sample28 s4 181 sample 2 F +Sample29 s4 182 sample 3 F +Sample30 s2 52 pool 5 E +Sample31 s2 63 pool 5 E +Sample32 s2 83 pool 5 E +Sample33 s2 93 pool 5 E +Sample34 s2 102 pool 5 E +Sample35 s3 103 pool 5 E +Sample36 s3 112 pool 5 E +Sample37 s3 132 pool 5 E +Sample38 s3 143 pool 5 E +Sample39 s3 152 pool 5 E +Sample40 s4 163 pool 5 E +Sample41 s4 174 pool 5 E +Sample42 s4 185 pool 5 E +Sample43 s4 196 pool 5 E +Sample44 s1 1 pool 5 E +Sample45 s1 11 pool 5 E +Sample46 s1 22 pool 5 E +Sample47 s1 28 pool 5 E +Sample48 s1 39 pool 5 E +Sample49 s1 51 pool 5 E |
| b |
| diff -r 000000000000 -r 58997c28b268 test-data/in_VM1.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/in_VM1.tabular Tue Jan 19 16:41:47 2021 +0000 |
| b |
| b'@@ -0,0 +1,746 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|
| b |
| diff -r 000000000000 -r 58997c28b268 test-data/out_VM1.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out_VM1.tabular Tue Jan 19 16:41:47 2021 +0000 |
| b |
| b'@@ -0,0 +1,746 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|
| b |
| diff -r 000000000000 -r 58997c28b268 test-data/out_corr1.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out_corr1.tabular Tue Jan 19 16:41:47 2021 +0000 |
| b |
| b'@@ -0,0 +1,278 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|
| b |
| diff -r 000000000000 -r 58997c28b268 test-data/out_sif1.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out_sif1.tabular Tue Jan 19 16:41:47 2021 +0000 |
| b |
| b'@@ -0,0 +1,3370 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|
