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Commit message:
planemo upload for repository https://github.com/workflow4metabolomics/ascaw4m commit 7ea9b0f8abc5a60c2c04fd2098788497f14766b6 |
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added:
ASCA.Calculate_w4m.R ASCA.PlotScoresPerLevel_w4m.R asca_w4m.R asca_wrapper.R asca_xml.xml ssq.R static/images/BDAGroup_ASCA_figure.png static/images/BDAGroup_ASCA_figure.tif static/images/Thumbs.db test-data/ASCA_choo_samplemetadata.tsv test-data/ASCA_choo_variablemetadata.tsv test-data/choo_datamatrix.txt test-data/choo_samplemetadata.txt test-data/choo_variablemetadata.txt |
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| diff -r 000000000000 -r 93312041f1d5 ASCA.Calculate_w4m.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ASCA.Calculate_w4m.R Fri Sep 21 05:51:14 2018 -0400 |
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| @@ -0,0 +1,84 @@ +ASCA.Calculate_w4m <- function (data, levels, equation.elements = "", scaling, only.means.matrix = FALSE, use.previous.asca = NULL) +{ + ASCA.GetEquationElement <- function(asca, evaluation, previous.asca) { + s <- list() + s$factors.evaluated <- evaluation + if (!is.null(previous.asca)) { + s$level.combinations <- previous.asca[[paste(evaluation, + collapse = "")]]$level.combinations + } + else { + s$level.combinations <- ASCA.GetRowRepeats(asca$levels[, + s$factors.evaluated, drop = FALSE]) + } + s$means.matrix <- matrix(nrow = dim(asca$data)[1], ncol = dim(asca$data)[2]) + for (p in 1:dim(s$level.combinations$row.patterns)[1]) { + mean.for.this.level.combination <- colMeans(asca$data[s$level.combinations$indices.per.pattern[[p]], + , drop = FALSE]) + for (i in s$level.combinations$indices.per.pattern[[p]]) { + s$means.matrix[i, ] <- mean.for.this.level.combination + } + } + s + } + s <- list() + dataAdjusted <- MetStaT.ScalePip(data, center = FALSE, scale = FALSE, + quietly = TRUE) + s$ssq.mean <- sum(rep(dataAdjusted$center.vector/dataAdjusted$scale.vector, + nrow(data))^2) + s$ssq <- sum(data^2) + s$data <- dataAdjusted$data + if (!is.numeric(levels)) { + stop("The supplied levels are not numeric.") + } + s$levels <- levels + if (!only.means.matrix) { + s$svd <- PCA.Calculate(s$data) + } + s$ee.names <- c() + if (identical(equation.elements, "")) { + equation.elements <- ASCA.GetPowerSet(c(1:dim(s$levels)[2]), + exclude.empty.set = TRUE) + } + if (is.character(equation.elements)) + equation.elements <- lapply(strsplit(strsplit(equation.elements, + split = ",")[[1]], split = ""), as.numeric) + for (ee in equation.elements) { + for (f in ee) if (f > dim(levels)[2] || f < 1) { + stop(paste("Factor ", f, " is beyond scope of study-design", + sep = "")) + } + } + if (dim(data)[1] != dim(levels)[1]) { + stop(paste("Number of rows in data (", dim(data)[1], + ") and study design (", dim(levels)[1], ") do not match", + sep = "")) + } + order.to.evaluate.ee <- sort(as.numeric(unlist(lapply(equation.elements, + paste, collapse = ""))), index.return = TRUE)$ix + s$remainder <- s$data + for (ee in order.to.evaluate.ee) { + new.equation.element <- ASCA.GetEquationElement(s, equation.elements[[ee]], + use.previous.asca) + reductions <- ASCA.GetPowerSet(equation.elements[[ee]], + exclude.empty.set = TRUE, exclude.complete.set = TRUE) + for (r in reductions) { + new.equation.element$means.matrix <- new.equation.element$means.matrix - + s[[c(paste(r, collapse = ""))]]$means.matrix + } + new.equation.element$ssq <- sum(new.equation.element$means.matrix^2) + if (!only.means.matrix) { + s$remainder <- s$remainder - new.equation.element$means.matrix + new.equation.element$reduced.matrix <- s$remainder + new.equation.element$svd <- PCA.Calculate(new.equation.element$means.matrix) + } + ee.name <- paste(equation.elements[[ee]], collapse = "") + s$ee.names <- c(s$ee.names, ee.name) + s[[ee.name]] <- new.equation.element + } + s$ssq.remainder <- sum(s$remainder^2) + if (!only.means.matrix) + asca.summary <- ASCA.GetSummary(s, quietly = TRUE) + return(list(s, asca.summary)) +} + \ No newline at end of file |
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| diff -r 000000000000 -r 93312041f1d5 ASCA.PlotScoresPerLevel_w4m.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ASCA.PlotScoresPerLevel_w4m.R Fri Sep 21 05:51:14 2018 -0400 |
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| @@ -0,0 +1,64 @@ +ASCA.PlotScoresPerLevel_w4m <- function (asca, ee, pcs="1,2", interaction=0, factorName="", factorModalite) +{ + pcs <- as.numeric(strsplit(pcs, split=",")[[1]]) + y <- (asca[[ee]]$means.matrix + asca$remainder) %*% asca[[ee]]$svd$v + t.list.x <- list() + t.list.y <- list() + list.color.pattern <- list() + color.per.variable <- rep(0, dim(asca$data)[1]) + pattern.per.variable <- rep(0, dim(asca$data)[1]) + kColOptions <- c(24, 552, 254, 26, 84, 51, 652, 68, 76, 96, + 10, 60, 33, 245, 147, 12, 26, 164, 181, 52, 512, 344, + 201, 111) + kPointOptions <- 1:30 + for (p in 1:dim(asca[[ee]]$level.combinations$row.pattern)[1]) { + if (length(asca[[ee]]$level.combinations$row.pattern[p, + ]) == 1) { + list.color.pattern[[p]] <- c(kColOptions[p], kPointOptions[p]) + } + else if (length(asca[[ee]]$level.combinations$row.pattern[p, + ]) == 2) { + list.color.pattern[[p]] <- c(kColOptions[asca[[ee]]$level.combinations$row.pattern[p, + 1]], kPointOptions[asca[[ee]]$level.combinations$row.pattern[p, + 2]]) + } + else { + list.color.pattern[[p]] <- c(kColOptions[asca[[ee]]$level.combinations$row.pattern[p, + 1]]%%9, floor(kPointOptions[asca[[ee]]$level.combinations$row.pattern[p, + 2]]/9)) + } + color.per.variable[asca[[ee]]$level.combinations$indices.per.pattern[[p]]] <- list.color.pattern[[p]][1] + pattern.per.variable[asca[[ee]]$level.combinations$indices.per.pattern[[p]]] <- list.color.pattern[[p]][2] + t.list.x[[p]] <- y[asca[[ee]]$level.combinations$indices.per.pattern[[p]], + pcs[1]] + t.list.y[[p]] <- y[asca[[ee]]$level.combinations$indices.per.pattern[[p]], + pcs[2]] + } + legend.colors.patterns <- do.call(rbind, list.color.pattern) + if (interaction != 1){ + titre <- paste("PC", pcs[1], " vs PC", pcs[2], " - Factor ", factorName, sep="") + }else { + titre <- paste("PC", pcs[1], " vs PC", pcs[2], " - Interaction", sep="") + } + plot(asca[[ee]]$svd$t[, pcs[1]], asca[[ee]]$svd$t[, pcs[2]], + xlim=range(c(min(unlist(t.list.x)), max(unlist(t.list.x)))), + ylim=range(c(min(unlist(t.list.y)), max(unlist(t.list.y)))), + main=titre, + xlab=paste("PC", pcs[1], " (", formatC(asca[[ee]]$svd$var.explained[pcs[1]] * 100, digits=2, format="f"), "%)", sep=""), + ylab=paste("PC", pcs[2], " (", formatC(asca[[ee]]$svd$var.explained[pcs[2]] * 100, digits=2, format="f"), "%)", sep=""), + cex=1.5, lwd=3, col=colors()[color.per.variable], + pch=pattern.per.variable) +# if (interaction != 1){ + legend(x="bottomright", legend=factorModalite, + cex=0.8, col=colors()[legend.colors.patterns[, 1]], pch=legend.colors.patterns[, 2]) +# } +# else { +# legend(x="bottomright", apply(asca[[ee]]$level.combinations$row.patterns, 1, paste, collapse=" "), +# cex=0.8, col=colors()[legend.colors.patterns[, 1]], pch=legend.colors.patterns[, 2]) +# } + + for (p in 1:length(t.list.x)) { + points(t.list.x[[p]], t.list.y[[p]], col=colors()[list.color.pattern[[p]][1]], + pch=list.color.pattern[[p]][2]) + } +} |
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| diff -r 000000000000 -r 93312041f1d5 asca_w4m.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/asca_w4m.R Fri Sep 21 05:51:14 2018 -0400 |
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| @@ -0,0 +1,82 @@ +asca_w4m <- function(datamatrix, samplemetadata, factors, variablemetadata, threshold, scaling="none", nPerm) +{ + ## Transpose +# datamatrix <- t(datamatrix) + + # Check sample ID's + rownames(datamatrix) <- make.names(rownames(datamatrix), unique = TRUE) + colnames(datamatrix) <- make.names(colnames(datamatrix), unique = TRUE) + rownames(samplemetadata) <- make.names(rownames(samplemetadata), unique = TRUE) + rownames(variablemetadata) <- make.names(rownames(variablemetadata), unique = TRUE) + + if(!identical(rownames(datamatrix), rownames(samplemetadata))) + { + if(identical(sort(rownames(datamatrix)), sort(rownames(samplemetadata)))) + { + cat("\n\nMessage: Re-ordering dataMatrix sample names to match sampleMetadata\n") + datamatrix <- datamatrix[rownames(samplemetadata), , drop = FALSE] + stopifnot(identical(sort(rownames(datamatrix)), sort(rownames(samplemetadata)))) + }else { + + cat("\n\nStop: The sample names of dataMatrix and sampleMetadata do not match:\n") + print(cbind.data.frame(indice = 1:nrow(datamatrix), + dataMatrix=rownames(datamatrix), + sampleMetadata=rownames(samplemetadata))[rownames(datamatrix) != rownames(samplemetadata), , drop = FALSE]) + } + } + + # Check feature ID's + if(!identical(colnames(datamatrix), rownames(variablemetadata))) + { + if(identical(sort(colnames(datamatrix)), sort(rownames(variablemetadata)))) + { + cat("\n\nMessage: Re-ordering dataMatrix variable names to match variableMetadata\n") + datamatrix <- datamatrix[, rownames(variablemetadata), drop = FALSE] + stopifnot(identical(sort(colnames(datamatrix)), sort(rownames(variablemetadata)))) + }else { + cat("\n\nStop: The variable names of dataMatrix and variableMetadata do not match:\n") + print(cbind.data.frame(indice = 1:ncol(datamatrix), + dataMatrix=colnames(datamatrix), + variableMetadata=rownames(variablemetadata))[colnames(datamatrix) != rownames(variablemetadata), , drop = FALSE]) + } + } + + # Design + design <- data.matrix(samplemetadata[, colnames(samplemetadata) %in% factors]) + + # Scaling if scaling!=none + datamatrix <- prep(datamatrix, scaling) + + # Computation of the A-SCA model + data.asca <- ASCA.Calculate_w4m(datamatrix, design, scaling=scaling) + + # Permutation test + data.asca.permutation <- ASCA.DoPermutationTest(data.asca[[1]], perm=nPerm) + p <- c(data.asca.permutation, 0) + + + # % of explained variance + ssq <- (data.asca[[2]]$summary.ssq) + ssq <- cbind(round(rbind(ssq[2], ssq[3],ssq[4],ssq[5])*100, 2), p) + rownames(ssq) <- c(factors[1], factors[2], "Interaction", "Residuals") + colnames(ssq) <- c("% of explained variance", "Permutation p-value") + + # Add Scores and loadings at the end of meatadata files + noms <- colnames(samplemetadata) + samplemetadata <- cbind(samplemetadata, (data.asca[[1]]$'1'$means.matrix + data.asca[[1]]$remainder) %*% data.asca[[1]]$'1'$svd$v[, 1:2], + (data.asca[[1]]$'2'$means.matrix + data.asca[[1]]$remainder) %*% data.asca[[1]]$'2'$svd$v[, 1:2], + (data.asca[[1]]$'12'$means.matrix + data.asca[[1]]$remainder) %*% data.asca[[1]]$'12'$svd$v[, 1:2]) + colnames(samplemetadata) <- c(noms, paste(factors[1],"XSCOR-p1", sep="_"), paste(factors[1],"XSCOR-p2", sep="_"), + paste(factors[2],"XSCOR-p1", sep="_"), paste(factors[2],"XSCOR-p2", sep="_"), + "Interact_XSCOR-p1", "Interact_XSCOR-p2") + + noms <- colnames(variablemetadata) + variablemetadata <- cbind(variablemetadata, data.asca[[1]]$'1'$svd$v[, 1:2], data.asca[[1]]$'2'$svd$v[, 1:2], data.asca[[1]]$'12'$svd$v[, 1:2]) + colnames(variablemetadata) <- c(noms, paste(factors[1],"XLOAD-p1", sep="_"), paste(factors[1],"XLOAD-p2", sep="_"), + paste(factors[2],"XLOAD-p1", sep="_"), paste(factors[2],"XLOAD-p2", sep="_"), + "Interact_XLOAD-p1", "Interact_XLOAD-p2") + + l <- list(data.asca[[1]], data.asca.permutation, ssq, samplemetadata, variablemetadata) + names(l) <- c("ASCA","p-values", "ssq", "samplemetadata", "variablemetadata") + return(l) +} |
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| diff -r 000000000000 -r 93312041f1d5 asca_wrapper.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/asca_wrapper.R Fri Sep 21 05:51:14 2018 -0400 |
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| b'@@ -0,0 +1,237 @@\n+#!/usr/bin/env Rscript\n+\n+###################################################################################################\n+#\n+# MetStaT ASCA.calculate function\n+#\n+#\n+# R-Package: MetStaT\n+#\n+# Version: 1.0\n+#\n+# Author (asca.calculate): Tim Dorscheidt\n+# Author (wrapper & .r adaptation for workflow4metabolomics.org): M. Tremblay-Franco & Y. Guitton #\n+# \n+# Expected parameters from the commandline\n+# input files:\n+# dataMatrix\n+# sampleMetadata\n+# variableMetadata\n+# params:\n+# Factors (Factor1 & Factor2)\n+# scaling\n+# Number of permutations\n+# Significance threshold\n+# output files:\n+# sampleMetadata\n+# variableMetadata\n+#\t\t\t Graphical outputs\n+#\t\t\t Information text\n+###################################################################################################\n+pkgs=c("MetStaT","batch","pcaMethods")\n+for(pkg in pkgs) {\n+ suppressPackageStartupMessages( stopifnot( library(pkg, quietly=TRUE, logical.return=TRUE, character.only=TRUE)))\n+ cat(pkg,"\\t",as.character(packageVersion(pkg)),"\\n",sep="")\n+}\n+\n+\n+listArguments = parseCommandArgs(evaluate=FALSE) #interpretation of arguments given in command line as an R list of objects\n+\n+#Redirect all stdout to the log file\n+sink(listArguments$information)\n+\n+# ----- PACKAGE -----\n+cat("\\tPACKAGE INFO\\n")\n+sessionInfo()\n+\n+source_local <- function(fname) {\n+ argv <- commandArgs(trailingOnly = FALSE)\n+ base_dir <- dirname(substring(argv[grep("--file=", argv)], 8))\n+ source(paste(base_dir, fname, sep="/"))\n+}\n+\n+#load asca_w4m function\n+source_local("asca_w4m.R")\n+source_local("ASCA.Calculate_w4m.R")\n+source_local("ASCA.PlotScoresPerLevel_w4m.R")\n+print("first loadings OK")\n+\n+## libraries\n+##----------\n+\n+cat(\'\\n\\nRunning ASCA.calculate\\n\');\n+options(warn=-1);\n+#remove rgl warning\n+options(rgl.useNULL = TRUE);\n+\n+\n+## constants\n+##----------\n+\n+modNamC <- "asca" ## module name\n+\n+topEnvC <- environment()\n+flgC <- "\\n"\n+\n+## functions\n+##----------For manual input of function\n+##--end function\n+\n+flgF <- function(tesC,\n+ envC = topEnvC,\n+ txtC = NA) { ## management of warning and error messages\n+\n+ tesL <- eval(parse(text = tesC), envir = envC)\n+\n+ if(!tesL) {\n+\n+ sink(NULL)\n+ stpTxtC <- ifelse(is.na(txtC),\n+ paste0(tesC, " is FALSE"),\n+ txtC)\n+\n+ stop(stpTxtC,\n+ call. = FALSE)\n+\n+ }\n+\n+} ## flgF\n+\n+\n+## log file\n+##---------\n+cat("\\nStart of the \'", modNamC, "\' Galaxy module call: ",\n+ format(Sys.time(), "%a %d %b %Y %X"), "\\n", sep="")\n+\n+\n+## arguments\n+##----------\n+## loading files and checks\n+xMN <- t(as.matrix(read.table(listArguments[["dataMatrix_in"]],\n+ check.names = FALSE,\n+ header = TRUE,\n+ row.names = 1,\n+ sep = "\\t")))\n+varIdDM <- rownames(xMN)\n+\n+samDF <- read.table(listArguments[["sampleMetadata_in"]],\n+ check.names = FALSE,\n+ header = TRUE,\n+ row.names = 1,\n+\t\t\t\t\t sep = "\\t")\n+obsIdSMD <- rownames(samDF)\n+\n+varDF <- read.table(listArguments[["variableMetadata_in"]],\n+ check.names = FALSE,\n+ header = TRUE,\n+ row.names = 1,\n+\t\t\t\t\t sep = "\\t")\n+varIdVDM <- rownames(varDF)\n+\n+result <- asca_w4m(xMN, samDF, c(listArguments[["factor1"]],listArguments[["factor2"]]), varDF, as.numeric(listArguments[["threshold"]]), \n+ scaling=listArguments[["scaling"]], listArguments[["nPerm"]])\n+\n+\n+##saving\n+\n+if (exists("result")) {\n+\t## writing output files\n+\tcat("\\n\\nWriting output files\\n\\n");\n+\twrite.table(data.frame(cbind(obsIdSMD, result[[4]])),\n+ \t\t\tfile = listArguments$sampleMetadata_out,\n+ \t\t\tquote = FALSE,\n+ \t\t\trow.names = FALSE,\n'..b'"]])))\n+\t{\n+\t\tdata.asca.permutation <- result[[2]]\n+\t\tdesign <- data.matrix(samDF[, colnames(samDF) %in% c(listArguments[["factor1"]],listArguments[["factor2"]])])\n+\t\t\n+\t\tpdf(listArguments$figure, onefile=TRUE)\n+\t\tpar(mfrow=c(1,3))\n+\t\tif (data.asca.permutation[1] < as.numeric(listArguments[["threshold"]]))\n+\t\t{\n+\t\t\teigenvalues <- data.frame(1:length(unique(design[,1])), result[[1]]$\'1\'$svd$var.explained[1:length(unique(design[,1]))])\n+\t\t\tcolnames(eigenvalues) <- c("PC", "explainedVariance")\n+\t\t\tbarplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component")\n+\t\t\tnoms <- levels(as.factor(samDF[, listArguments$factor1]))\n+\t\t\tASCA.PlotScoresPerLevel_w4m(result[[1]], ee="1", interaction=0, factorName=listArguments$factor1, factorModalite=noms)\n+\n+\t\t\tv1 <- paste(listArguments[["factor1"]],"_XLOAD-p1", sep="")\n+\t\t\tv2 <- paste(listArguments[["factor1"]],"_XLOAD-p2", sep="")\n+\t\t\tf1.loadings <- data.matrix(result[[5]][,c(v1, v2)])\n+\t\t\tf1.loadings.leverage <- diag(f1.loadings%*%t(f1.loadings))\n+\t\t\tnames(f1.loadings.leverage) <- colnames(xMN)\n+\t\t\tf1.loadings.leverage <- sort(f1.loadings.leverage, decreasing=TRUE)\n+\t\t\tbarplot(f1.loadings.leverage[f1.loadings.leverage > 0.001], main="Leverage values")\n+\t\t}\n+\t\tif (data.asca.permutation[2] < as.numeric(listArguments[["threshold"]]))\n+\t\t{\n+\t\t\teigenvalues <- data.frame(1:length(unique(design[,2])), result[[1]]$\'2\'$svd$var.explained[1:length(unique(design[,2]))])\n+\t\t\tcolnames(eigenvalues) <- c("PC", "explainedVariance")\n+\t\t\tbarplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component") \n+\t\t\tnoms <- levels(as.factor(samDF[, listArguments$factor2]))\n+\t\t\tASCA.PlotScoresPerLevel_w4m(result[[1]], ee="2", interaction=0, factorName=listArguments$factor2, factorModalite=noms)\n+\n+\t\t\tv1 <- paste(listArguments[["factor2"]],"_XLOAD-p1", sep="")\n+\t\t\tv2 <- paste(listArguments[["factor2"]],"_XLOAD-p2", sep="")\n+\t\t\tf2.loadings <- data.matrix(result[[5]][,c(v1, v2)])\n+\t\t\tf2.loadings.leverage <- diag(f2.loadings%*%t(f2.loadings))\n+\t\t\tnames(f2.loadings.leverage) <- colnames(xMN)\n+\t\t\tf2.loadings.leverage <- sort(f2.loadings.leverage, decreasing=TRUE)\n+\t\t\tbarplot(f2.loadings.leverage[f2.loadings.leverage > 0.001], main="Leverage values")\n+\t\t}\n+ \tif (data.asca.permutation[3] < as.numeric(listArguments[["threshold"]]))\n+ \t{\n+ \t eigenvalues <- data.frame(1:(length(unique(design[,1]))*length(unique(design[,2]))), result[[1]]$\'12\'$svd$var.explained[1:(length(unique(design[,1]))*length(unique(design[,2])))])\n+ \t colnames(eigenvalues) <- c("PC", "explainedVariance")\n+ \t barplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component")\n+ \t noms1 <- data.matrix(levels(as.factor(samDF[, listArguments$factor1])))\n+ \t noms2 <- data.matrix(levels(as.factor(samDF[, listArguments$factor2])))\n+ \t noms <- apply(noms1, 1, FUN=function(x){paste(x, "-", noms2, sep="")})\n+ \t noms <- apply(noms, 1, FUN=function(x){c(noms)})\n+ \t ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="12", interaction=1, factorModalite=noms[,1])\n+ \t \n+ \t v1 <- "Interact_XLOAD-p1"\n+ \t v2 <- "Interact_XLOAD-p2"\n+ \t f1f2.loadings <- data.matrix(result[[5]][, c(v1, v2)])\n+ \t f1f2.loadings.leverage <- diag(f1f2.loadings%*%t(f1f2.loadings))\n+ \t names(f1f2.loadings.leverage) <- colnames(xMN)\n+ \t f1f2.loadings.leverage <- sort(f1f2.loadings.leverage, decreasing=TRUE)\n+ \t barplot(f1f2.loadings.leverage[f1f2.loadings.leverage > 0.001], main="Leverage values")\n+ \t}\n+ dev.off()\n+\t}\n+\n+\ttryCatch({\n+\tsave(result, file="asca.RData");\n+\t}, warning = function(w) {\n+\tprint(paste("Warning: ", w));\n+\t}, error = function(err) {\n+\tstop(paste("ERROR saving result RData object:", err));\n+\t});\n+}\n+\n+## ending\n+##-------\n+\n+cat("\\nEnd of the \'", modNamC, "\' Galaxy module call: ",\n+ format(Sys.time(), "%a %d %b %Y %X"), "\\n", sep = "")\n+\n+sink()\n+\n+# options(stringsAsFactors = strAsFacL)\n+\n+\n+rm(list = ls())\n' |
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| diff -r 000000000000 -r 93312041f1d5 asca_xml.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/asca_xml.xml Fri Sep 21 05:51:14 2018 -0400 |
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| b'@@ -0,0 +1,330 @@\n+<tool id="asca" name="ASCA" version="1.0.0">\n+ <description>Splitting of the total variance into independent blocks according to the experimental factors and multivariate analysis (SCA) of each block</description>\n+ \n+ <requirements>\n+ <requirement type="package" version="1.1_4">r-batch</requirement>\n+ <requirement type="package" version="1.0">r-MetStaT</requirement>\n+ <requirement type="package" version="1.70.0">bioconductor-pcamethods</requirement>\n+ </requirements>\n+\n+ <stdio>\n+ <exit_code range="1:" level="fatal" />\n+ </stdio>\n+ \n+ <command><![CDATA[\n+ Rscript $__tool_directory__/asca_wrapper.R\n+ dataMatrix_in "$dataMatrix_in"\n+ sampleMetadata_in "$sampleMetadata_in"\n+ variableMetadata_in "$variableMetadata_in"\n+ factor1 "$factor1"\n+ factor2 "$factor2"\n+ scaling "$scaling"\n+ nPerm "$nPerm"\n+ threshold "$threshold"\n+\n+\n+ sampleMetadata_out "$sampleMetadata_out"\n+ variableMetadata_out "$variableMetadata_out"\n+ figure "$figure"\n+ information "$information"\n+ ]]></command> \n+ \n+ <inputs>\n+ <param name="dataMatrix_in" type="data" label="Data matrix file" help="" format="tabular" />\n+ <param name="sampleMetadata_in" type="data" label="Sample metadata file" help="" format="tabular" />\n+ <param name="variableMetadata_in" type="data" label="Variable metadata file" help="" format="tabular" />\n+ <param name="factor1" label="Name of the sampleMetadata column containing the 1st factor for A-SCA" type="text" value="none" help=""/>\n+ <param name="factor2" label="Name of the sampleMetadata column containing the 2nd factor for A-SCA" type="text" value="none" help=""/>\n+ <param name="scaling" label="Scaling to apply to dataMatrix" type="select" help="">\n+\t <option value="none" selected="true">None</option>\n+ <option value="pareto">pareto</option>\n+ <option value="uv">uv</option>\n+\t</param>\n+\t<param name="nPerm" label="Number of permutation to perform to compute factor significance" type="select" help="">\n+\t <option value="100" selected="true">100</option>\n+ <option value="500">500</option>\n+ <option value="1000">1000</option>\n+\t</param>\n+\t<param name="threshold" type="float" value="0.05" label="Threshold for factor significance (permutation test)" help="Must be between 0 and 1"/>\n+ </inputs>\n+ \n+ <outputs>\n+ <data name="sampleMetadata_out" label="${tool.name}_${sampleMetadata_in.name}" format="tabular" ></data>\n+ <data name="variableMetadata_out" label="${tool.name}_${variableMetadata_in.name}" format="tabular" ></data>\n+\t<data name="figure" label="${tool.name}_figure.pdf" format="pdf"/>\n+\t<data name="information" label="${tool.name}_information.txt" format="txt"/>\n+ </outputs>\n+\n+ <tests>\n+ <test>\n+ <param name="dataMatrix_in" value="choo_datamatrix.txt"/>\n+ <param name="sampleMetadata_in" value="choo_samplemetadata.txt"/>\n+ <param name="variableMetadata_in" value="choo_variablemetadata.txt"/>\n+\t <param name="factor1" value="Date"/>\n+ <param name="factor2" value="Treatment"/>\n+ <param name="scaling" value="pareto"/>\n+ <param name="threshold" value="0.05"/>\n+ <param name="nPerm" value="1000"/>\n+\t \n+ <output name="sampleMetadata_out" file="ASCA_choo_samplemetadata.tsv" lines_diff="6"/>\n+ </test>\n+ </tests>\n+\n+ \n+ <help>\n+ \n+.. class:: infomark\n+ \n+**Tool updates**\n+ \n+See the **NEWS** section at the bottom of this page\n+ \n+---------------------------------------------------\n+\n+.. class:: infomark\n+ \n+**Authors** Marie Tremblay-Franco (W4M Core Development Team, MetaboHUB Toulouse, AXIOM) and Yann Guitton (W4M Core Development Team, Laberca, UM1329)\n+ \n+---------------------------------------------------\n+ \n+.. class:: infomark\n+\n+**References**\n+| R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (http://www.r-project.org)\n+| Tim Dorscheidt (2013). MetStaT: Statistical metabolomics'..b'f a ASCA_BDAGroup_ASCA_samplemetadata.tsv: tsv file** including PC1 and PC2 scores from F1 PCA, F2 PCA and F1xF2 PCA \n+\n+\n+\t\n++--------------------+----------+----------+-------------+-------------+-------------+-------------+-------------------+-------------------+\n+| sampleMetadata | F1 | F2 | F1_XSCOR-p1 | F1_XSCOR-p2 | F2_XSCOR-p1 | F2_XSCOR-p2 | Interact_XSCOR-p1 | Interact_XSCOR-p1 |\n++====================+==========+==========+=============+=============+=============+=============+===================+===================+\n+| Ind1 | 1 | 1 | -2.66136390 | 0.307505352 | 0.986520075 | -0.25138715 | -0.31885686 | -0.77109078 |\n++--------------------+----------+----------+-------------+-------------+-------------+-------------+-------------------+-------------------+\n+| Ind2 | 1 | 1 | -0.74779084 | -0.30750535 | -0.99758505 | 0.070057773 | 0.719240017 | 0.950058502 |\n++--------------------+----------+----------+-------------+-------------+-------------+-------------+-------------------+-------------------+\n+| Ind3 | 1 | 2 | -1.22618411 | -0.15375267 | -0.24288670 | 0.124191016 | -0.00883820 | 0.465391498 |\n++--------------------+----------+----------+-------------+-------------+-------------+-------------+-------------------+-------------------+\n+\n+ \n+\n+ | **2) Example of a ASCA_BDAGroup_ASCA_variablemetadata.tsv: tsv file** including PC1 and PC2 loadings from F1 PCA, F2 PCA and F1xF2 PCA \n+\n+\t\n++--------------------+----------+-------------+-------------+-------------+-------------+-------------------+--------------------+\n+| variableMetadata | Number | F1_XLOAD-p1 | F1_XLOAD-p2 | F2_XLOAD-p1 | F2_XLOAD-p2 | Interact_XLOAD-p1 | Interact_XLOAD-p1 |\n++====================+==========+=============+=============+=============+=============+===================+====================+\n+| V1 | 1 | 0.977759467 | -0.20972940 | -0.99814337 | 0.060908126 | 0.428703939 | 0.903445035 |\n++--------------------+----------+-------------+-------------+-------------+-------------+-------------------+--------------------+\n+| V2 | 2 | 0.977759467 | -0.30750535 | -0.06090812 | -0.99814337 | -0.90344503 | 0.428703939 |\n++--------------------+----------+-------------+-------------+-------------+-------------+-------------------+--------------------+\n+\n+\n+\n+ | **3) Example of a ASCA_information.txt: txt file** including % of explained variance and p-value of permutation test \n+\n+\n++----------------------+-------------------------+---------------------+\n+| ASCA_information.txt | % of explained variance | Permutation p-value |\n++======================+=========================+=====================+\n+| F1 | 81.71 | 0.004 |\n++----------------------+-------------------------+---------------------+\n+| F2 | 1.29 | 0.880 |\n++----------------------+-------------------------+---------------------+\n+| Interaction | 1.33 | 0.962 |\n++----------------------+-------------------------+---------------------+\n+| Residuals | 15.67 | - |\n++----------------------+-------------------------+---------------------+\n+\n+\n+\t| **4) Example of ASCA_figure.pdf: pdf file** including Scree, Score plot and barplot of leverage values only for significant factor(s)/interaction**\n+\t| Leverage: importance of a variable in the PCA model (Nueda et al. 2007)\n+\t\n+\t\n+.. image:: BDAGroup_ASCA_figure.tif\n+ :width: 600\n+\n+----\n+NEWS\n+----\n+\n+ \n+</help>\n+ \n+ <citations>\n+ <citation type="doi">10.1093/bioinformatics/bti476</citation>\n+ <citation type="doi">10.1002/cem.952</citation>\n+ <citation type="doi">10.1093/bioinformatics/btm251</citation>\n+ </citations>\n+ \n+</tool>\n' |
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| diff -r 000000000000 -r 93312041f1d5 ssq.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ssq.R Fri Sep 21 05:51:14 2018 -0400 |
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| @@ -0,0 +1,58 @@ +library(lmdme) +library(MetStaT) + +## Data : attention standardiser au prealable +data <- read.table("E:/PROJETS/Asca_W4M/Test_Matlab_data.txt", sep="\t", dec=",", header=TRUE, row.names=1) +design <- read.table("E:/PROJETS/Asca_W4M/Test_Matlab_design.txt", sep="\t", dec=",", header=TRUE) +design[,1] <- as.factor(design[,1]) +design[,2] <- as.factor(design[,2]) + +## Verifier noms + + +fit <- lmdme(model=~F1 + F2 + F1:F2, data=data, design=design) + +permuted <- permutation(model=~F1*F2, data=data, design=design, NPermutations=100, nCpus=3) + +decomposition(fit, decomposition = "pca", scale="none", type="coefficient") + + + + +ssq <- function(fit) +{ + Overall_means <- sum(sum(fitted.values(fit)$'(Intercept)'^2))/sum(sum(data^2)) + Factors <- c(sum(sum(fitted.values(fit)$'F1'^2))/sum(sum(data^2)), sum(sum(fitted.values(fit)$'F2'^2))/sum(sum(data^2))) + Interactions <- sum(sum(fitted.values(fit)$'F1:F2'^2))/sum(sum(data^2)) + Residuals <- 1 - Overall_means - Factors[1] - Factors[2] - Interactions + + return(list(Overall_means, Factors, Interactions, Residuals)) +} + + +par(mfrow=c(2,2)) +biplot(fit, xlabs="o", mfcol=NULL) +##Just the term of interest +biplot(fit, xlabs="o", term="F1") +##In separate graphics +biplot(fit, xlabs="o", term=c("F1", "F2"), mfcol=c(1,1)) +##All terms in the same graphic +biplot(fit, xlabs="o", mfcol=c(1,3)) + +test <-lapply(permuted, FUN = ssq) +test1 <- matrix(unlist(test), ncol=5, byrow=TRUE) +test[2:101] + +apply(apply(test1, 2, FUN = function(x){x > x[1]})[-1,], 2 , sum) / (length(test)-1) + +score_moyen <- data.frame(fit@components$F1$rotation) + +score <- data.frame(cbind(design, t(fit@residuals$'F1:F2')%*%fit@components$F1$rotation)) + +pc <- fit@components$F1$sdev / sum(fit@components$F1$sdev) + +sp <- ggplot(score_moyen, aes(x=PC1, y=PC2)) +sp + geom_point(size=2) + xlab(paste("PC1", round(pc[1]*100,1), "%")) + ylab(paste("PC2", round(pc[2]*100,1), "%")) + + geom_text(data=score, aes(PC1, PC2, label=Ind, col=F1), size=4, hjust=0, nudge_x=0.05, vjust=0, nudge_y=0.5) + + |
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| diff -r 000000000000 -r 93312041f1d5 test-data/ASCA_choo_samplemetadata.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ASCA_choo_samplemetadata.tsv Fri Sep 21 05:51:14 2018 -0400 |
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| @@ -0,0 +1,47 @@ +Individu Treatment Date Groupe Date_XSCOR-p1 Date_XSCOR-p2 Treatment_XSCOR-p1 Treatment_XSCOR-p2 Interact_XSCOR-p1 Interact_XSCOR-p2 +T1_G1R1 G1R1 Control 1 T1_Control -0.263776615764101 -0.0227162948089953 -0.352068615277719 0.0501951723654677 -0.0841900461426222 -0.138883652959945 +T1_G1R2 G1R2 Control 1 T1_Control -0.112700381563171 -0.0100902751699422 -0.427012056462084 -0.0742747720664629 -0.100149495758465 -0.0800527605007484 +T1_G1R3 G1R3 Control 1 T1_Control -0.135450434817638 0.00901692866615921 -0.36507151893165 -0.0399670319951867 -0.117942328419898 -0.121275176947659 +T1_G1R4 G1R4 Control 1 T1_Control -0.103492110974998 -0.00156421781950379 -0.18445388901378 -0.112974133305033 -0.294272186361302 -0.265918147170269 +T1_G1R5 G1R5 Control 1 T1_Control -0.247214981877366 -0.0222262447237016 -0.628807325586428 0.0913878072072312 0.00627742251172139 0.00694742305672699 +T1_G1R6 G1R6 Control 1 T1_Control -0.0414801312818897 0.0247316419539704 -0.484807214756766 -0.00587670295465007 -0.0162369740653528 -0.00526940835244719 +T1_G1R7 G1R7 Control 1 T1_Control -0.117483936569149 0.0271222965747283 -0.559089501755822 -0.01718165601941 0.0140537921432078 -0.168001306939547 +T1_G1R8 G1R8 Control 1 T1_Control -0.100117441294851 -0.00427383467271438 -0.292147579575381 -0.0641195966233711 -0.204587035271549 -0.134436423257723 +T1_G3R1 G3R1 Vanco-Imi 1 T1_Vancomycin 0.207632414681034 -0.00558069642413273 -0.361972725679242 -0.0290556556891379 0.0490330814847755 0.160890927117024 +T1_G3R2 G3R2 Vanco-Imi 1 T1_Vancomycin 0.111702541008661 0.00407471178967939 -0.242442758239047 -0.069308901844156 0.0292756738089185 -0.0219743090831489 +T1_G3R3 G3R3 Vanco-Imi 1 T1_Vancomycin 0.181865996862185 -0.00340611940953461 -0.226005498729343 0.0474548303398726 0.0439257010549963 0.0497408808137225 +T1_G3R4 G3R4 Vanco-Imi 1 T1_Vancomycin 0.313740853745976 0.0136318536068662 -0.431486609189272 -0.0102223842835381 0.105736347159205 0.1959043084753 +T1_G3R5 G3R5 Vanco-Imi 1 T1_Vancomycin 0.213591741115782 -0.00674876656602194 -0.3301390976278 -0.0716809079452157 0.0540499889724698 0.192749844228548 +T1_G3R6 G3R6 Vanco-Imi 1 T1_Vancomycin 0.114850645934237 -0.00994420827924761 -0.448122173193104 0.043205526427199 0.130318027270869 0.148273357071658 +T1_G3R7 G3R7 Vanco-Imi 1 T1_Vancomycin 0.0137695208363559 -0.00420685353229555 -0.542825525749072 -0.0382438795386586 0.178840693577092 0.0569055934074114 +T1_G3R8 G3R8 Vanco-Imi 1 T1_Vancomycin 0.0665365048810402 0.0121800788146848 -0.710463312952751 -0.0449595408577803 0.250711222093968 0.166485158282372 +T2_G1R1 G1R1 Control 2 T2_Control -0.11791287165745 0.0234060478147082 0.0207915964917011 -0.142203890112268 0.376276784557908 0.0432407395052124 +T2_G1R2 G1R2 Control 2 T2_Control -0.14706342371869 0.0201752048458604 0.279576491622367 -0.207604656638314 0.387148468266929 0.00033569331420482 +T2_G1R3 G1R3 Control 2 T2_Control -0.191231344886553 0.0168236862860295 0.0654811065928573 -0.29362095360222 0.39583073275468 -0.018293656047174 +T2_G1R4 G1R4 Control 2 T2_Control -0.167414454566213 0.000758843790372174 0.383356706826779 -0.273898802503071 0.294189823195077 0.0353933576827112 +T2_G1R5 G1R5 Control 2 T2_Control -0.0739889553965559 0.0230154641640148 0.131774358825217 -0.233970634845879 0.261220107532913 -0.019356532198424 +T2_G1R6 G1R6 Control 2 T2_Control -0.0188552646959605 -0.00616856186076241 0.336927125116385 -0.133058380230685 0.247824549912035 -0.0121154950141325 +T2_G1R7 G1R7 Control 2 T2_Control -0.167666414912584 -0.0504083049475882 0.48281679033623 -0.101932297787931 0.0576442721169115 0.0543613922471621 +T2_G1R8 G1R8 Control 2 T2_Control -0.237583304309157 -0.027602380092634 0.341345074247496 -0.151454116588796 0.146131098990804 0.062668131437162 +T2_G3R2 G3R2 Vanco-Imi 2 T2_Vancomycin 0.233980958791463 0.0281015766173231 0.189466024822911 -0.273962140308022 -0.30154329810821 -0.0214573936114664 +T2_G3R3 G3R3 Vanco-Imi 2 T2_Vancomycin 0.128133952583074 -0.0250334973610056 0.19992288581292 -0.206567745959379 -0.193928038486439 -0.0325919537180273 +T2_G3R4 G3R4 Vanco-Imi 2 T2_Vancomycin 0.235510061921675 0.0137986007269723 0.259056963507356 -0.25988568476301 -0.359255821127826 -0.07486984036718 +T2_G3R5 G3R5 Vanco-Imi 2 T2_Vancomycin 0.112545287653429 -0.0206585220717317 0.308344939822194 -0.151632144209085 -0.400497432808368 0.00500295086456895 +T2_G3R6 G3R6 Vanco-Imi 2 T2_Vancomycin 0.124239473021999 -0.0129696165790924 0.303917829209721 -0.164878664484114 -0.355347208138659 0.00150097469342654 +T2_G3R7 G3R7 Vanco-Imi 2 T2_Vancomycin 0.081052915460548 -0.00919259612377862 0.297498691625735 -0.142957177716538 -0.33482008264185 0.00625468766729662 +T2_G3R8 G3R8 Vanco-Imi 2 T2_Vancomycin 0.155266292249923 0.0259540547913112 0.228603259000817 -0.145642208330371 -0.243295898044924 -0.0511162100759775 +T3_G1R1 G1R1 Control 3 T3_Control -0.164364864760678 -0.0200729407459455 0.251932164953624 0.178467776811174 -0.247498539157728 0.127897278221848 +T3_G1R2 G1R2 Control 3 T3_Control -0.0983676788090374 0.0259287104462602 0.203193523597202 0.198209780205972 -0.154491907029615 0.0579935114802987 +T3_G1R3 G1R3 Control 3 T3_Control -0.129211355460194 0.0222651813416259 0.079700683525491 0.325718019549065 0.00427322401160234 0.130017268988483 +T3_G1R4 G1R4 Control 3 T3_Control -0.0511561485767939 0.035305218092631 -0.0108970815880194 0.0296656817125971 -0.161400899558209 0.0400905919795229 +T3_G1R5 G1R5 Control 3 T3_Control -0.138821464964014 0.0211235419842184 0.154397804185189 0.301394851523335 -0.0190060922081248 0.0683367042619386 +T3_G1R6 G1R6 Control 3 T3_Control -0.249473956080939 -0.0233288190419852 0.233896218849 0.282968521658481 -0.190931918046832 0.143684699664077 +T3_G1R7 G1R7 Control 3 T3_Control -0.0553810294785773 0.0066348955230046 0.196432876593008 0.145713526261075 -0.188138564991111 0.115155512113687 +T3_G1R8 G1R8 Control 3 T3_Control -0.234939536012931 -0.0678557875998089 0.362296107942414 0.259937215538307 -0.308739798540474 0.145624435578448 +T3_G3R1 G3R1 Vanco-Imi 3 T3_Vancomycin 0.114092897886039 0.00298675435180912 0.193385899651988 0.221587929382518 0.198849152389625 -0.149941602089303 +T3_G3R2 G3R2 Vanco-Imi 3 T3_Vancomycin 0.216806860284924 0.00767577362452252 0.182156610702705 0.285140408498848 0.227615504152754 -0.156753935999413 +T3_G3R3 G3R3 Vanco-Imi 3 T3_Vancomycin 0.205428214190469 0.00798770210985579 0.203464405078008 0.105420996421592 0.127853732721118 -0.119941910497864 +T3_G3R4 G3R4 Vanco-Imi 3 T3_Vancomycin 0.202812954111873 0.0410074697989515 0.0288388597085911 0.369708657910417 0.437950449487773 -0.225103150733399 +T3_G3R5 G3R5 Vanco-Imi 3 T3_Vancomycin 0.110942520215847 -0.00483886802464264 0.186235595144823 0.127799944218548 0.108910302075133 -0.0771223618550159 +T3_G3R7 G3R7 Vanco-Imi 3 T3_Vancomycin 0.156171112974063 0.00468985755059507 0.171102679222868 0.166832974247297 0.107949475579789 -0.0786993721217933 +T3_G3R8 G3R8 Vanco-Imi 3 T3_Vancomycin 0.0644743820188965 -0.059508689411093 0.321899211291687 0.230325040923284 0.0343839370852807 -0.0422808226121511 |
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| diff -r 000000000000 -r 93312041f1d5 test-data/ASCA_choo_variablemetadata.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ASCA_choo_variablemetadata.tsv Fri Sep 21 05:51:14 2018 -0400 |
| b |
| b'@@ -0,0 +1,1453 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|
| b |
| diff -r 000000000000 -r 93312041f1d5 test-data/choo_datamatrix.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/choo_datamatrix.txt Fri Sep 21 05:51:14 2018 -0400 |
| b |
| b'@@ -0,0 +1,1453 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|
| b |
| diff -r 000000000000 -r 93312041f1d5 test-data/choo_samplemetadata.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/choo_samplemetadata.txt Fri Sep 21 05:51:14 2018 -0400 |
| b |
| @@ -0,0 +1,47 @@ +Sample Individu Treatment Date Groupe +T1_G1R1 G1R1 Control 1 T1_Control +T1_G1R2 G1R2 Control 1 T1_Control +T1_G1R3 G1R3 Control 1 T1_Control +T1_G1R4 G1R4 Control 1 T1_Control +T1_G1R5 G1R5 Control 1 T1_Control +T1_G1R6 G1R6 Control 1 T1_Control +T1_G1R7 G1R7 Control 1 T1_Control +T1_G1R8 G1R8 Control 1 T1_Control +T1_G3R1 G3R1 Vanco-Imi 1 T1_Vancomycin +T1_G3R2 G3R2 Vanco-Imi 1 T1_Vancomycin +T1_G3R3 G3R3 Vanco-Imi 1 T1_Vancomycin +T1_G3R4 G3R4 Vanco-Imi 1 T1_Vancomycin +T1_G3R5 G3R5 Vanco-Imi 1 T1_Vancomycin +T1_G3R6 G3R6 Vanco-Imi 1 T1_Vancomycin +T1_G3R7 G3R7 Vanco-Imi 1 T1_Vancomycin +T1_G3R8 G3R8 Vanco-Imi 1 T1_Vancomycin +T2_G1R1 G1R1 Control 2 T2_Control +T2_G1R2 G1R2 Control 2 T2_Control +T2_G1R3 G1R3 Control 2 T2_Control +T2_G1R4 G1R4 Control 2 T2_Control +T2_G1R5 G1R5 Control 2 T2_Control +T2_G1R6 G1R6 Control 2 T2_Control +T2_G1R7 G1R7 Control 2 T2_Control +T2_G1R8 G1R8 Control 2 T2_Control +T2_G3R2 G3R2 Vanco-Imi 2 T2_Vancomycin +T2_G3R3 G3R3 Vanco-Imi 2 T2_Vancomycin +T2_G3R4 G3R4 Vanco-Imi 2 T2_Vancomycin +T2_G3R5 G3R5 Vanco-Imi 2 T2_Vancomycin +T2_G3R6 G3R6 Vanco-Imi 2 T2_Vancomycin +T2_G3R7 G3R7 Vanco-Imi 2 T2_Vancomycin +T2_G3R8 G3R8 Vanco-Imi 2 T2_Vancomycin +T3_G1R1 G1R1 Control 3 T3_Control +T3_G1R2 G1R2 Control 3 T3_Control +T3_G1R3 G1R3 Control 3 T3_Control +T3_G1R4 G1R4 Control 3 T3_Control +T3_G1R5 G1R5 Control 3 T3_Control +T3_G1R6 G1R6 Control 3 T3_Control +T3_G1R7 G1R7 Control 3 T3_Control +T3_G1R8 G1R8 Control 3 T3_Control +T3_G3R1 G3R1 Vanco-Imi 3 T3_Vancomycin +T3_G3R2 G3R2 Vanco-Imi 3 T3_Vancomycin +T3_G3R3 G3R3 Vanco-Imi 3 T3_Vancomycin +T3_G3R4 G3R4 Vanco-Imi 3 T3_Vancomycin +T3_G3R5 G3R5 Vanco-Imi 3 T3_Vancomycin +T3_G3R7 G3R7 Vanco-Imi 3 T3_Vancomycin +T3_G3R8 G3R8 Vanco-Imi 3 T3_Vancomycin |
| b |
| diff -r 000000000000 -r 93312041f1d5 test-data/choo_variablemetadata.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/choo_variablemetadata.txt Fri Sep 21 05:51:14 2018 -0400 |
| b |
| b'@@ -0,0 +1,1453 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