| Previous changeset 0:b22c453e4cf4 (2018-10-11) Next changeset 2:2173ad5e7750 (2019-10-16) |
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Commit message:
Uploaded |
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modified:
CorrTable/Corr.xml CorrTable/Corr_Script_samples_row.R CorrTable/Corr_wrap.r |
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added:
CorrTable/README.md CorrTable/test-data/input1_tab1.tabular CorrTable/test-data/input1_tab2.txt CorrTable/test-data/input2_dataMatrix_500.txt CorrTable/test-data/output1_CT_plot.pdf CorrTable/test-data/output1_CorrTable.tabular CorrTable/test-data/output2_CorrTable.tabular |
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| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/Corr.xml --- a/CorrTable/Corr.xml Thu Oct 11 05:35:55 2018 -0400 +++ b/CorrTable/Corr.xml Thu Aug 01 11:30:58 2019 -0400 |
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| b'@@ -1,4 +1,4 @@\n-<tool id="corrtable" name="Between-table Correlation" version="0.0.0">\r\n+<tool id="corrtable" name="Between-table Correlation" version="1.0.0">\r\n \t<description>Correlation table between two tables and graphic representation </description>\r\n \t <requirements>\r\n \t\t\t<requirement type="package" version="1.1_4">r-batch</requirement>\r\n@@ -33,18 +33,23 @@\n \t\t\r\n \t\treorder_var "$out_section.reorder_var"\r\n \t\t\r\n-\t\tcolor_heatmap "${out_section.heatmap_cond.color_heatmap}"\r\n-\t\t#if str($out_section.heatmap_cond.color_heatmap) == \'yes\' :\r\n-\t\t\ttype_classes "${out_section.heatmap_cond.typeclass_cond.type_classes}"\r\n-\t\t\t#if str($out_section.heatmap_cond.typeclass_cond.type_classes) == \'regular\' :\r\n-\t\t\t\treg_class_value "${out_section.heatmap_cond.typeclass_cond.reg_class_value}"\r\n-\t\t\t#elif str($out_section.heatmap_cond.typeclass_cond.type_classes) == \'irregular\' :\r\n-\t\t\t\tirreg_class_vect "${out_section.heatmap_cond.typeclass_cond.irreg_class_vect}"\r\n-\t\t\t#end if\r\n+\t\tplot_choice "$out_section.plot_cond.plot_choice"\r\n+\t\t#if str($out_section.plot_cond.plot_choice) == \'none\' :\r\n+\t\t\ttabcorr_out "$tabcorr_out"\r\n+\t\t#else:\r\n+\t\t\tcolor_heatmap "${out_section.plot_cond.heatmap_cond.color_heatmap}"\r\n+\t\t\t#if str($out_section.plot_cond.heatmap_cond.color_heatmap) == \'yes\' :\r\n+\t\t\t\ttype_classes "${out_section.plot_cond.heatmap_cond.typeclass_cond.type_classes}"\r\n+\t\t\t\t#if str($out_section.plot_cond.heatmap_cond.typeclass_cond.type_classes) == \'regular\' :\r\n+\t\t\t\t\treg_class_value "${out_section.plot_cond.heatmap_cond.typeclass_cond.reg_class_value}"\r\n+\t\t\t\t#elif str($out_section.plot_cond.heatmap_cond.typeclass_cond.type_classes) == \'irregular\' :\r\n+\t\t\t\t\tirreg_class_vect "${out_section.plot_cond.heatmap_cond.typeclass_cond.irreg_class_vect}"\r\n+\t\t\t\t#end if\r\n+\t\t\t#end if\t\r\n+\t\t\ttabcorr_out "$tabcorr_out"\r\n+\t\t\theatmap_out "$heatmap_out"\r\n \t\t#end if\t\r\n-\t\t\r\n-\t\ttabcorr_out "$tabcorr_out"\r\n-\t\theatmap_out "$heatmap_out"\r\n+\r\n \t\t\r\n \t</command>\r\n \t\r\n@@ -118,55 +123,142 @@\n \t\t\t</conditional>\t\t\r\n \t\t</section>\r\n \t\t\r\n-\t\t<section name="out_section" title="Graphical outputs" expanded="False">\r\n+\t\t<section name="out_section" title="Output options" expanded="False">\r\n \t\t\t<param name="reorder_var" label="Reorder variables (using Hierarchical Cluster Analysis)" type="select" display="radio" help="">\r\n \t\t\t\t<option value="no">No</option>\r\n \t\t\t\t<option value="yes">Yes</option>\r\n \t\t\t</param>\r\n \t\t\r\n-\t\t\t<conditional name="heatmap_cond">\r\n-\t\t\t\t<param name="color_heatmap" label="Colored correlation table strategy" type="select" display="radio" help="Standard corresponds to a scale with a smooth gradient between three colors: red, white and green (continuous case). Customized creates classes for the correlation coefficients - the scale has discrete values.">\r\n-\t\t\t\t\t<option value="no">Standard</option>\r\n-\t\t\t\t\t<option value="yes">Customized</option>\r\n+\t\t\t<conditional name="plot_cond">\r\n+\t\t\t\t<param name="plot_choice" label="PDF output" type="select" help="To determine whether a colored correlation table is plotted.">\r\n+\t\t\t\t\t<option value="auto">Default</option>\r\n+\t\t\t\t\t<option value="forced">Always plot a colored table</option>\r\n+\t\t\t\t\t<option value="none">No colored table</option>\r\n \t\t\t\t</param>\t\t\r\n \t\t\r\n-\t\t\t\t<when value="yes">\r\n-\t\t\t\t\t<conditional name="typeclass_cond">\r\n-\t\t\t\t\t\t<param name="type_classes" label="Choose the type of classes" type="select" display="radio" help="Regular means the classes have the same size. Irregular means it is possible to choose any intervals." >\r\n-\t\t\t\t\t\t\t<option value="regular">Regular classes</option>\r\n-\t\t\t\t\t\t\t<option value="irregular">Irregular classes</option>\r\n-\t\t\t\t\t\t</param>\r\n+\t\t\t\t<when value="auto">\r\n+\t\t\t\t\t<conditional name="heatmap_cond">\r\n+\t\t\t\t\t<param name="color_heatmap" label="Colored correlation table strategy" type="select" display="radio" help="Standard corresponds to a scale with a smooth gradient between three colors: red, white and green (continuous case). Customized creates classes for the correlation coefficients - the scale has discrete '..b"d on each input tables, with:\r\n+| - 1 - correlation coefficient, as distance\r\n+| - Ward as aggregation method.\r\n+| \r\n \r\n- \r\n+| **PDF output:** To determine whether a colored correlation table is plotted.\r\n+| \t- 'Default': generates a pdf file with a colored correlation table if the filtered table has no dimension above 1000 (number of lines or columns). \r\n+| \t- 'Always plot a colored table': used when you are not afraid of huge colored correlation table; to be used wisely.\r\n+| \t- 'No colored table': the module will generate the correlation table in tabular format only (no pdf file). \r\n+|\r\n+\t\r\n Colored correlation table strategy\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n-\t\t| Allows to create a colored correlation table. Variables of table 1 and variables of table 2 are related using colored rectangles. \r\n-\t\t| About the colors, the negative correlations are in red, more or less intense according to their position between -1 and 0, and the positive correlations in green, more or less intense according to their position between 0 and 1. The coefficients equal to 0 are in white.\r\n-\t\t| \t- 'Standard': the graphical representation has a scale with a smooth gradient between three colors: red, white and green.\r\n-\t\t| \t- 'Customized': the colored correlation table has coefficient classes. It is possible to create regular or irregular classes. The scale is discreet.\r\n-\t\t|\r\n-\t\t\r\n+ | *Only available when* **PDF output** *is set to 'Default' or 'Always plot a colored table'.*\r\n+ | Allows to create a colored correlation table. Variables of table 1 and variables of table 2 are related using colored rectangles. \r\n+ | About the colors, the negative correlations are in red, more or less intense according to their position between -1 and 0, and the positive correlations in green, more or less intense according to their position between 0 and 1. The coefficients equal to 0 are in white.\r\n+ | \t- 'Standard': the graphical representation has a scale with a smooth gradient between three colors: red, white and green.\r\n+ | \t- 'Customized': the colored correlation table has coefficient classes. It is possible to create regular or irregular classes. The scale is discreet.\r\n+ \r\n | **Choose the type of classes (only if colored correlation table strategy is 'Customized'):**\r\n+| \r\n \r\n | \t- 'Regular': classes are all (or almost) the same size. \t\r\n | To realize these intervals, we start from 1 to go to 0 by taking a step of the size chosen by the user, and we make the symmetry for -1 towards 0. If the last step does not fall on the 0 value, we create a class between this last value and 0, smaller in size than the others. It is important to specify that 0 represents a class on its own, which is assigned the color white for the heatmap.\t\r\n@@ -267,7 +364,6 @@\n | *Size of classes (if regular classes):* A value between 0 and 1.\r\n \r\n | Example: if the size is 0.4, classes are [-1;-0.6], ]-0.6;-0.2], ]-0.2;0[, 0, ]0;0.2], ]0.2;0.6] and ]0.6;1].\r\n-|\r\n \r\n | \t- 'Irregular': classes have variable lengths.\r\n | It is possible to do as many classes as you want, and of any size. There is not necessarily symmetry between -1 and 0, and 0 and 1. You can choose to have a white class with only 0, or an interval which contains the value 0.\r\n@@ -276,7 +372,7 @@\n \r\n | Example: if the vector is (-0.8,-0.5,-0.4,0,0.4,0.5,0.8), the classes are [-1;-0.8], ]-0.8;-0.5], ]-0.5;-0.4], ]-0.4;0[, 0, ]0;0.4], ]0.4;0.5], ]0.5;0.8] and ]0.8;1].\r\n |\r\n-\r\n+\t\r\n \t\r\n ------------\r\n Output files\r\n@@ -291,7 +387,7 @@\n Heatmap (colored correlation table)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n \t| Pdf output\r\n-\t| Colored representation of the correlation table. The coefficients are replaced by colors. A coefficient close to -1 is red, close to 0 white, and close to 1 in green.\r\n+\t| Colored representation of the correlation table. The coefficients are replaced by colors. A coefficient close to -1 is red, close to 0 white, and close to 1 green.\r\n \t|\r\n \t\r\n \t\r\n" |
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| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/Corr_Script_samples_row.R --- a/CorrTable/Corr_Script_samples_row.R Thu Oct 11 05:35:55 2018 -0400 +++ b/CorrTable/Corr_Script_samples_row.R Thu Aug 01 11:30:58 2019 -0400 |
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| b'@@ -2,7 +2,7 @@\n # CORRELATION TABLE #\r\n # #\r\n # #\r\n- # Input : 2 tables with common samples #\r\n+ # Input : 2 tables with shared samples #\r\n # Output : Correlation table ; Heatmap (pdf) #\r\n # #\r\n # Dependencies : Libraries "ggplot2" and "reshape2" #\r\n@@ -13,11 +13,8 @@\n # Parameters (for dev)\r\n if(FALSE){\r\n \r\n- rm(list = ls())\r\n- setwd(dir = "Y:/Developpement")\r\n-\r\n- tab1.name <- "Test/Ressources/Inputs/CT2_DM.tabular"\r\n- tab2.name <- "Test/Ressources/Inputs/CT2_base_Diapason_14ClinCES_PRIN.txt"\r\n+ tab1.name <- "test/ressources/inputs/CT/CT2_DM.tabular"\r\n+ tab2.name <- "test/ressources/inputs/CT/CT2_base_Diapason_14ClinCES_PRIN.txt"\r\n param1.samples <- "column"\r\n param2.samples <- "row"\r\n corr.method <- "pearson"\r\n@@ -28,6 +25,7 @@\n filters.choice <- "filters_0_thr"\r\n threshold <- 0.2\r\n reorder.var <- "yes"\r\n+\tplot.choice <- "auto"\r\n color.heatmap <- "yes"\r\n type.classes <-"irregular"\r\n reg.value <- 1/3\r\n@@ -40,8 +38,8 @@\n \r\n \r\n correlation.tab <- function(tab1.name, tab2.name, param1.samples, param2.samples, corr.method, test.corr, alpha, \r\n- multi.name, filter, filters.choice, threshold, reorder.var, color.heatmap, type.classes, \r\n- reg.value, irreg.vect, output1, output2){\r\n+ multi.name, filter, filters.choice, threshold, reorder.var, plot.choice, color.heatmap, \r\n+ type.classes, reg.value, irreg.vect, output1, output2){\r\n \r\n # This function allows to visualize the correlation between two tables\r\n # \r\n@@ -59,6 +57,7 @@\n # and threshold filter remove variables with all their correlation coefficients in abs < threshold\r\n # - threshold (value between 0 and 1): threshold for filter threshold\r\n # - reorder.var ("yes" or "no"): reorder variables in the correlation table thanks to the HCA\r\n+\t# - plot.choice ("auto", "forced" or "none"): determine whether a heatmap is plotted\r\n # - color.heatmap ("yes" or "no"): color the heatmap with classes defined by the user\r\n # - type.classes ("regular" or "irregular"): choose to color the heatmap with regular or irregular classes\r\n # - reg.value (value between 0 and 1): value for regular classes\r\n@@ -85,7 +84,9 @@\n tab1 <- tab1[order(rownames(tab1)),]\r\n tab2 <- tab2[order(rownames(tab2)),]\r\n \r\n- \r\n+\r\n+ # Checks ---------------------------------------------------------------------------------------------\r\n+\r\n # Check if the 2 datasets match regarding samples identifiers\r\n # Adapt from functions "check.err" and "match2", RcheckLibrary.R\r\n \r\n@@ -128,8 +129,20 @@\n }\r\n \r\n \r\n+ # Check whether tab1=tab2\r\n+ \r\n+ err.msg <- NULL\r\n+ \r\n+ if((ncol(tab1)==ncol(tab2))&&(sum(tab1!=tab2,na.rm=TRUE)==0)){\r\n+ autocor <- TRUE\r\n+\terr.msg <- c(err.msg, "\\nYou chose the same table for the two dataset inputs. \\nTo allow filtering options,",\r\n+\t "we will turn the diagonal to 0 in the correlation matrix during the filtering process.\\n")\r\n+ }else{\r\n+ autocor <- FALSE\r\n+ }\r\n+ \r\n+ \r\n # Check qualitative variables in each input tables\r\n- err.msg <- NULL\r\n \r\n var1.quali <- vector()\r\n var2.quali <- vector()\r\n@@ -167,16 +180,63 @@\n }\r\n \r\n if(length(var1.quali) != 0){\r\n- tab1 <- tab1[,-var1.quali]\r\n+ tab1 <- tab1[,-var1.quali,drop=FALSE]\r\n }\r\n if(length(var2.quali) !'..b'tab2 <- tab2[,-var2.cons,drop=FALSE]\r\n+ }\r\n+ \r\n+ \r\n+ # Print info message\r\n+ \r\n if(length(err.msg) != 0){ \r\n cat("\\n- - - - - - - - -\\n",err.msg,"\\n- - - - - - - - -\\n\\n")\r\n }\r\n \r\n+ rm(err.stock,var1.quali,var2.quali,var1.cons,var2.cons,err.msg) \r\n+ \r\n+ \r\n # Correlation table ---------------------------------------------------------------------------------\r\n \r\n tab.corr <- matrix(nrow = dim(tab2)[2], ncol = dim(tab1)[2])\r\n@@ -190,7 +250,6 @@\n rownames(tab.corr) <- colnames(tab2)\r\n \r\n \r\n- \r\n # Significance of correlation test ------------------------------------------------------------------\r\n \r\n if (test.corr == "yes"){\r\n@@ -218,6 +277,11 @@\n # Filter settings ------------------------------------------------------------------------------------\r\n \r\n if (filter == "yes"){\r\n+ \r\n+ # Turn diagonal from 1 to 0 if autocorrelation\r\n+\tif(autocor){\r\n+\t for(i in 1:(ncol(tab.corr))){ tab.corr[i,i] <- 0 }\r\n+\t}\r\n \r\n # Remove variables with all their correlation coefficients = 0 :\r\n if (filters.choice == "filter_0"){\r\n@@ -232,8 +296,8 @@\n }\r\n \r\n if (length(var2.thres) != 0){\r\n- tab.corr <- tab.corr[-var2.thres,]\r\n- tab2 <- tab2[, -var2.thres]\r\n+ tab.corr <- tab.corr[-var2.thres,,drop=FALSE]\r\n+ tab2 <- tab2[, -var2.thres,drop=FALSE]\r\n }\r\n \r\n var1.thres <- vector()\r\n@@ -244,9 +308,14 @@\n }\r\n \r\n if (length(var1.thres) != 0){\r\n- tab.corr <- tab.corr[,-var1.thres]\r\n- tab1 <- tab1[,-var1.thres]\r\n+ tab.corr <- tab.corr[,-var1.thres,drop=FALSE]\r\n+ tab1 <- tab1[,-var1.thres,drop=FALSE]\r\n }\r\n+\t\r\n+\t# Turn diagonal from 0 back to 1 if autocorrelation\r\n+\tif(autocor){\r\n+\t for(i in 1:(ncol(tab.corr))){ tab.corr[i,i] <- 1 }\r\n+\t}\r\n \r\n } \r\n \r\n@@ -258,17 +327,19 @@\n dist.tab2 <- as.dist(1 - cormat.tab2)\r\n hc.tab2 <- hclust(dist.tab2, method = "ward.D2")\r\n tab.corr <- tab.corr[hc.tab2$order,]\r\n+\trm(cormat.tab2)\r\n \r\n cormat.tab1 <- cor(tab1, method = corr.method, use = "pairwise.complete.obs")\r\n dist.tab1 <- as.dist(1 - cormat.tab1)\r\n hc.tab1 <- hclust(dist.tab1, method = "ward.D2")\r\n tab.corr <- tab.corr[,hc.tab1$order]\r\n+\trm(cormat.tab1)\r\n \r\n }\r\n \r\n \r\n+ # Output 1 : Correlation table -----------------------------------------------------------------------\r\n \r\n- # Output 1 : Correlation table -----------------------------------------------------------------------\r\n \r\n # Export correlation table\r\n write.table(x = data.frame(name = rownames(tab.corr), tab.corr), file = output1, sep = "\\t", quote = FALSE, row.names = FALSE)\r\n@@ -276,7 +347,9 @@\n \r\n \r\n # Create the heatmap ---------------------------------------------------------------------------------\r\n- \r\n+\r\n+ if(plot.choice != "none"){\r\n+ \r\n # A message if no variable kept\r\n if(length(tab.corr)==0){\r\n \tpdf(output2)\r\n@@ -285,6 +358,17 @@\n \tdev.off()\r\n } else {\r\n \r\n+ # A message if more than 1000 variable in auto mode\r\n+ if((plot.choice=="auto")&&(max(dim(tab.corr))>1000)){\r\n+\tpdf(output2)\r\n+\twar.msg <- paste0("In \'default\' mode, the colored table is not provided when\\none of the tables contains more than ",\r\n+\t "a thousand\\nvariables after the filter step.\\n\\nOne of your table still contains ",max(dim(tab.corr))," variables.\\n",\r\n+\t\t\t\t\t "Please consider more filtering, or use the \'Always plot a\\ncolored table\' mode to obtain your colored table.")\r\n+\tplot.new()\r\n+\tlegend("center",war.msg,adj=c(0.05,0.075))\r\n+\tdev.off()\r\n+ } else {\r\n+ \r\n \r\n library(ggplot2)\r\n library(reshape2)\r\n@@ -399,7 +483,10 @@\n ggsave(output2, device = "pdf", width = 10+0.075*dim(tab.corr)[2], height = 5+0.075*dim(tab.corr)[1], limitsize = FALSE)\r\n \r\n \r\n- } # End if(length(tab.corr)==0)else\r\n+ } # End of if((plot.choice=="auto")&&(max(dim(tab.corr))>1000))else\r\n+ } # End of if(length(tab.corr)==0)else\r\n+ } # End of if(plot.choice != "auto")\r\n+ \r\n \r\n } # End of correlation.tab\r\n \r\n' |
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| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/Corr_wrap.r --- a/CorrTable/Corr_wrap.r Thu Oct 11 05:35:55 2018 -0400 +++ b/CorrTable/Corr_wrap.r Thu Aug 01 11:30:58 2019 -0400 |
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| @@ -4,10 +4,12 @@ # WRAPPER FOR Corr_Script_samples_row.R (CORRELATION TABLE) # # # # Author: Ophelie BARBET # +# Maintainer: Melanie PETERA # # User: Galaxy # # Original data: used with Corr_Script_samples_row.R # # Starting date: # # V-1: First version of wrapper # +# V-2: Corresponds to XML version 1.0.0 # # # # # # Input files: 2 tables with common samples file # @@ -56,6 +58,7 @@ type_classes <- NULL reg_class_value <- NULL irreg_class_vect <- NULL +if(args$plot_choice == "none"){args$color_heatmap <- "no"; args$heatmap_out <- NULL} if(args$color_heatmap == "yes"){ type_classes <- args$type_classes if(type_classes == "regular"){ @@ -67,7 +70,7 @@ correlation.tab(args$tab1_in, args$tab2_in, args$tab1_samples, args$tab2_samples, args$corr_method, args$test_corr, risk_alpha, correct_multi, args$filter, filters_choice, threshold, -args$reorder_var, args$color_heatmap, type_classes, reg_class_value, irreg_class_vect, args$tabcorr_out, args$heatmap_out) +args$reorder_var, args$plot_choice, args$color_heatmap, type_classes, reg_class_value, irreg_class_vect, args$tabcorr_out, args$heatmap_out) cat('\n--------------------------------------------------------------------', |
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| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/README.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/CorrTable/README.md Thu Aug 01 11:30:58 2019 -0400 |
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| @@ -0,0 +1,45 @@ +Between-table Correlation +======= + +Metadata +----------- + + * **@name**: Between-table Correlation + * **@version**: 1.0.0 + * **@authors**: Original code: Ophelie Barbet (PFEM - INRA) - Maintainer: Melanie Petera (PFEM - INRA - MetaboHUB) + * **@init date**: 2018, May + * **@main usage**: This tool builds a correlation table from two seperate tables, with filtering options and graphical outputs. + + +Context +----------- + +This tool is provided as one of the [Workflow4Metabolomics](http://workflow4metabolomics.org) Galaxy instance statistical tools. +W4M is a French infrastructure providing software tools to process, analyse and annotate metabolomics data. + +User interface is based on the Galaxy platform (homepage: https://galaxyproject.org/). It is an open, web-based platform for data intensive biomedical research. +Whether on the free public server or your own instance, you can perform, reproduce, and share complete analyses. + + +Configuration +----------- + +### Requirement: + * R software: version > 3.0.0 recommended + * Specific R libraries: 'batch', 'ggplot2' and 'reshape2' + + +Technical description +----------- + +Main files: + +- Corr_Script_samples_row.R: R function (core script) +- Corr_wrap.R: R script to link the main R function to inputs +- Corr.xml: XML wrapper (interface for Galaxy) + + +Services provided +----------- + + * Help and support: support@workflow4metabolomics.org |
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| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/test-data/input1_tab1.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/CorrTable/test-data/input1_tab1.tabular Thu Aug 01 11:30:58 2019 -0400 |
| b |
| b'@@ -0,0 +1,525 @@\n+sample\tIndiv1\tIndiv2\tIndiv3\tIndiv4\tIndiv5\tIndiv6\tIndiv7\tIndiv8\tIndiv9\tIndiv10\tIndiv11\tIndiv12\tIndiv13\tIndiv14\tIndiv15\tIndiv16\tIndiv17\tIndiv18\tIndiv19\tIndiv20\tIndiv21\tIndiv22\tIndiv23\tIndiv24\tIndiv25\tIndiv26\tIndiv27\tIndiv28\tIndiv29\tIndiv30\tIndiv31\tIndiv32\tIndiv33\tIndiv34\tIndiv35\tIndiv36\tIndiv37\tIndiv38\tIndiv39\tIndiv40\tIndiv41\tIndiv42\tIndiv43\tIndiv44\tIndiv45\tIndiv46\tIndiv47\tIndiv48\tIndiv49\tIndiv50\tIndiv51\tIndiv52\tIndiv53\tIndiv54\tIndiv55\tIndiv56\tIndiv57\tIndiv58\tIndiv59\tIndiv60\tIndiv61\tIndiv62\tIndiv63\tIndiv64\tIndiv65\tIndiv66\tIndiv67\tIndiv68\tIndiv69\tIndiv70\tIndiv71\tIndiv72\tIndiv73\tIndiv74\tIndiv75\tIndiv76\tIndiv77\tIndiv78\tIndiv79\tIndiv80\tIndiv81\tIndiv82\tIndiv83\tIndiv84\tIndiv85\tIndiv86\tIndiv87\tIndiv88\tIndiv89\tIndiv90\tIndiv91\tIndiv92\tIndiv93\tIndiv94\tIndiv95\tIndiv96\tIndiv97\tIndiv98\tIndiv99\tIndiv100\tIndiv101\tIndiv102\tIndiv103\tIndiv104\tIndiv105\tIndiv106\tIndiv107\tIndiv108\tIndiv109\tIndiv110\tIndiv111\tIndiv112\r\n+M204T2154\t12158.5480464108\t3948.26482331735\t13807.565819203\t6088.3660822285\t16203.6704701065\t21108.684599305\t11200.2589540357\t9621.10139341939\t9086.65369615158\t4952.84629980204\t9783.26129826107\t2444.79725547056\t5410.59561615698\t7050.91629511324\t5704.36952347691\t14302.4142268832\t5819.56747315687\t16252.2326336332\t17815.0018362962\t14026.1557570775\t12244.0714131509\t3407.97198069867\t9816.49974224777\t10374.2866447838\t8380.84749492183\t9760.91170458692\t5992.57070215078\t21703.2136099479\t16269.1565389462\t4037.99845511157\t6725.51264895372\t10518.4887926202\t18887.090069026\t10003.0805484489\t9578.65657133647\t7569.8756659492\t5505.86090989463\t6174.59667140532\t9106.40055308225\t4216.95795342718\t15707.2298865979\t5351.3719826074\t9137.81864585599\t4534.81919375675\t5970.3137676552\t5060.78974475246\t15538.0303421451\t6521.90778398682\t17468.2625186123\t4477.70221893953\t10959.7092378231\t2212.71545536701\t7409.70896113792\t4961.52497598763\t5944.70565679152\t5694.14508775443\t7804.3340290191\t6335.53203512277\t5424.6405611073\t5755.89288320948\t18041.33432198\t9206.19624538925\t8817.01466700432\t7511.40238249132\t8901.40113636213\t5793.96466428861\t6500.42596523934\t5742.53734182676\t12001.2769175176\t14390.8857144342\t7255.18527845156\t6522.82864832645\t11840.8098717264\t16443.4142353566\t12088.7072341466\t9025.40209311234\t7638.46128197049\t7400.9199058512\t5965.816575308\t12633.8811234917\t6313.81095164914\t15396.2077915275\t10200.030405004\t10212.7126478637\t10713.0087069702\t6293.41032862731\t5431.77112433456\t6855.06713990132\t6771.78201633809\t20585.8913817171\t6356.96938439913\t5989.97996820724\t6267.32594785837\t6064.77054071668\t6590.08422537772\t8210.10225382492\t8616.59650305326\t10240.6979218276\t13690.9147501225\t6084.88061572707\t5499.30427444054\t7138.40709127029\t5455.75267380849\t5508.90627724534\t16205.9948935278\t6764.72464624835\t16228.0613819446\t6690.91673950934\t0\t4823.05203931214\t8607.54138719506\t7715.21565134782\r\n+M585T2299\t7978.10802970514\t8918.45296914733\t10154.3589334951\t9091.89371829086\t9539.64518056097\t9147.92709983446\t8187.25586110114\t7570.68963844934\t8385.1915837259\t7898.87951480212\t7788.67164161559\t8446.7324945395\t8448.90350410469\t8744.29908583362\t9165.32382024919\t9357.42754743372\t9624.59012761071\t10454.0994975157\t9384.6303565487\t8184.98657335045\t9701.82295332971\t9126.91235938458\t9253.61812736969\t7897.03988545037\t8514.53061445021\t8851.92604546519\t6847.21057805662\t7110.35214825187\t8198.74947020271\t9048.51698668732\t6908.14749781058\t7916.29302646463\t7394.4931492299\t9170.21402423337\t7557.81667772913\t8533.28446277329\t8055.81446738342\t9934.83114781312\t6578.78816043633\t7496.81851930682\t7315.64095582113\t6712.82923439771\t6711.72150590482\t7173.6546585011\t8040.6529583636\t7595.56093342482\t8761.9516258468\t7742.66461043421\t8373.02978173495\t7337.27302155683\t6725.2038901472\t7257.51706392961\t7377.23520353943\t7634.29439115851\t7531.95074825313\t7056.38507790497\t6594.687990498\t7656.93096859931\t7631.26110163409\t8994.14791411429\t7650.23817478053\t8090.97830338437\t8143.83715137816\t8905.49308902687\t8508.42255114216\t8078.01842326391\t7646.72880399568\t8111.85426911\t8814.18228362973\t8972.1187847547'..b'10.88074758364\t1558.97252445184\t1664.05152215179\t2086.63156919493\t2206.33899830286\t2251.64128878932\t2477.13620098669\t2262.65709564594\t1771.66283357557\t2522.93074606868\t2590.61344758547\t2085.68080864049\t2391.78529588616\t2217.89777279907\t1987.09844459516\t2162.33629258401\t1811.99674254253\t2028.84859476208\t2226.10709920786\t1472.25071465914\t260.004305548371\t2248.97962606851\t1930.60535470025\t1813.94941835845\r\n+M576T2190\t1453.82124842473\t586.237743080617\t983.076798172024\t940.029892744418\t930.449371584918\t915.609702477217\t1401.77416376134\t943.046052510407\t1021.37176806923\t537.85818961017\t1126.30556788888\t252.06644368081\t1193.58792511052\t968.248617314411\t961.768070734704\t1014.46417976319\t1028.86692477889\t1157.5233433793\t964.027117761122\t1102.41420771437\t1768.24637681094\t555.229103153595\t956.919349785562\t878.959652345086\t1113.74248700665\t1039.59583859354\t828.282448857341\t744.517232176917\t1143.09916598306\t1234.42433063303\t888.072614079023\t1183.39389433753\t1307.86043695478\t1570.97199926915\t832.298889323065\t1224.9917361346\t1180.49675531303\t1040.0257215\t1328.577704807\t1097.72613697208\t1433.50718109167\t1259.6080571775\t1099.14870350968\t1040.01238030635\t918.297211820033\t1403.49641303775\t1300.85285146116\t1838.8178323853\t1174.88134808257\t1441.04470844412\t1499.45406012315\t339.967306862801\t897.541464506499\t1047.03923641302\t1509.21178445199\t978.676860153681\t1432.06991453515\t1319.53249904266\t1574.49135674188\t1411.30360544331\t1649.5444651457\t1277.43300365848\t1125.45889621046\t984.868903420733\t1089.47646164384\t1165.87270707119\t1132.28736382639\t1544.35767676501\t1165.3951417504\t1182.15699274489\t1124.18666516056\t969.679830921962\t1643.64222225179\t976.456539647717\t1291.40852165349\t1171.07404318025\t1301.17254599044\t1297.92520063266\t1221.57898468148\t1448.09812340852\t1382.7051509726\t1160.41813534195\t1126.5518858531\t924.811234913435\t1348.61481884227\t1054.07412194179\t948.877760019369\t1215.80681842566\t1314.8383753514\t938.45340836158\t1072.01333327579\t1150.713405199\t1187.80884119873\t1214.21859873359\t1468.27692586152\t1386.38361453256\t1039.95370376022\t1765.51291446778\t1297.77807691381\t1209.78462831848\t1473.5581856029\t1282.61173385607\t1205.37462264013\t1417.69465484895\t965.021407276672\t870.74030352648\t1291.22878612794\t764.216122247279\t218.683935991291\t1488.89994065121\t1176.77863876084\t970.271475801416\r\n+M454T809\t4957.67787273571\t0\t4885.51290512716\t51.6673742906229\t0\t94.9721832903342\t4634.05940862683\t0\t5290.31412731346\t53.8782914504427\t7988.05047979269\t68.2269020080072\t105.93617400312\t5200.94284715022\t6546.14900842765\t7935.42212927884\t5026.3964452256\t5935.58808468031\t10250.9155736569\t0\t5561.40488027537\t65.053286748293\t4777.83834331028\t6603.54162656939\t19.9382232056842\t313.808044276454\t5932.64086082137\t0\t46.1685100466028\t5184.24778257925\t89.6183969836256\t5242.06207966819\t0\t4644.74479340483\t0\t121.03680436721\t5584.8810253864\t5903.74931137876\t0\t5079.52447019599\t5564.06679152922\t4632.73499455875\t4594.12822383482\t4686.40496251976\t5556.87007040396\t4684.82442126298\t5166.67816191013\t68.1878649410903\t7053.72264887033\t4752.94670550414\t4944.75980360357\t0\t5476.25592474938\t5090.94250956484\t5149.03104892852\t5556.35429364872\t5529.77141128224\t5438.48697675469\t5413.51029868354\t4473.94016314064\t5181.23413673816\t5471.61745633009\t6037.76522108162\t7014.10042693463\t6896.59289577718\t5599.60305516978\t4885.76249075701\t5917.2309609654\t5353.3350020477\t5885.78266840732\t5710.58009781068\t5797.00566370548\t5247.69939380372\t5185.96619976865\t6864.99766974347\t0\t5425.50248564042\t5515.48816913844\t4672.53635456749\t5578.58552979151\t4643.07267032419\t6091.79346518871\t5661.73025232617\t5465.71445426601\t0\t7604.60442806052\t5609.01434556314\t5720.51823528963\t6591.39848056219\t5854.77215709515\t5258.17544869862\t5848.24619735011\t6126.30371048397\t6432.35305712057\t4618.98614745306\t4989.42707700074\t5391.45216225178\t5930.23960552772\t6298.87758081783\t5157.20648789665\t5977.34939635895\t6371.95283149976\t5298.63096459205\t5153.42798286731\t0\t8418.98774555655\t5328.96920493349\t5612.29518892328\t0\t7475.22497398961\t125.355907908131\t0\r\n' |
| b |
| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/test-data/input1_tab2.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/CorrTable/test-data/input1_tab2.txt Thu Aug 01 11:30:58 2019 -0400 |
| b |
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|
| b |
| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/test-data/input2_dataMatrix_500.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/CorrTable/test-data/input2_dataMatrix_500.txt Thu Aug 01 11:30:58 2019 -0400 |
| b |
| b'@@ -0,0 +1,499 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| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/test-data/output1_CT_plot.pdf |
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| Binary file CorrTable/test-data/output1_CT_plot.pdf has changed |
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| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/test-data/output1_CorrTable.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/CorrTable/test-data/output1_CorrTable.tabular Thu Aug 01 11:30:58 2019 -0400 |
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| @@ -0,0 +1,6 @@ +name M200T2618 M108T2759 M253T1601 M562T393 M140T2817 M317T2417 M498T712 M897T2828 M879T105 M413T2867 M782T2391 M835T963 M324T1666 M79T1303 M567T390 M207T2257 M232T997 M997T2834 M295T842 M958T481 M150T423 M964T1077 M510T148 M454T809 +Meta01 -0.0381099621034071 -0.0308771792251864 0.335303270663461 0.340984721652527 -0.392239536011236 0.308531863601226 -0.38276470907866 0.192781243616334 0.316953616678778 0.153455904743321 -0.145045084107932 -0.364896936594001 0.320967727079532 -0.377104415194285 0.373071418741227 0.352800416562518 0.319463412900551 0.324078173113261 0.306620047017588 0.326582168629611 0.0172092079575711 0.304807387613331 0.316450509570112 0.376440463645976 +Meta09 -0.0767546658101883 -0.129626792377785 0.101928911670895 0.115483290871375 -0.0234245956756988 0.0904474725214124 -0.024461194882052 -0.121320087678368 0.130125017347287 0.308155645217747 0.0831122451923826 0.0506561387912013 0.117292511090864 -0.0123538963682085 0.0267655184532761 -0.0161389675179283 0.0993591777883074 0.107454649875267 0.136562668239795 0.10755851274508 0.254034021197444 0.130672738299945 0.11062439577984 0.0687702584250841 +Meta15 0.0272019471781163 0.0310419371165683 -0.136260855677388 -0.0630013735941506 -0.0545937867718303 -0.135844925260746 -0.00952598537394389 -0.0207933370871036 -0.0524941400856259 -0.0369194221976208 0.354806184152381 -0.0056322067135378 -0.108772516379943 -0.0277111328940591 -0.171696474385562 -0.158841609776784 -0.0384562204512248 -0.0881771505485084 -0.0210940775418372 -0.060166862529553 -0.00276367086310437 -0.0471787341544353 -0.0764277784424161 -0.0977299103436893 +Meta22 0.120785153157455 0.0749336584503277 -0.0270536732280649 -0.0536052973820058 -0.150016784385942 -0.0632347476656861 -0.132863877612795 0.303035673758439 -0.133995542749929 0.000998502460650215 -0.139343133878119 -0.116656852987757 -0.0993268136599691 -0.128198216041191 -0.0553520708094436 0.0371066253540359 -0.0465298462059366 -0.05907858722759 -0.0866948285493421 -0.0495558714805468 0.203647127547309 -0.0528204949117596 -0.071940864021075 0.0345675482658712 +Meta23 0.326485225265319 0.304169001191892 0.00115369881122672 0.0301042258910735 -0.103162606015147 0.0537101596905055 -0.0783558303219841 0.0274704272882075 0.114237409683009 -0.0967931551436498 0.0975083134993656 -0.102156817498172 0.00470481879378514 -0.116990014334093 -0.0211850133509491 -0.0299843961961325 -0.103705033450308 0.0145444087397323 0.0736370908927353 -0.0767582436873565 0.32405558752902 0.0067286262747507 -0.0126106752475632 -0.0716433234564737 |
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| diff -r b22c453e4cf4 -r 29ec7e3afdd4 CorrTable/test-data/output2_CorrTable.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/CorrTable/test-data/output2_CorrTable.tabular Thu Aug 01 11:30:58 2019 -0400 |
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