| Next changeset 1:a3147e3d66e2 (2022-05-16) |
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Commit message:
planemo upload for repository https://github.com/workflow4metabolomics/mixmodel4repeated_measures commit 6ea32b3182383c19e5333201d2385a61d8da3d50 |
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added:
diagmfl.R mixmodel.xml mixmodel_script.R mixmodel_wrapper.R test-data/demo1_Samples.txt test-data/demo1_matrix.txt test-data/demo1_variables.txt test-data/mixmodel_demo1_variables.txt test-data/test_raw_variable_out.txt |
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| diff -r 000000000000 -r 1422de181204 diagmfl.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/diagmfl.R Wed Oct 10 05:18:42 2018 -0400 |
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| b'@@ -0,0 +1,568 @@\n+###############################################################################################\n+## Diagnostics graphics pour les mod\xe8les lin\xe9aires mixtes de type "mfl" ##\n+############################################################################################### ##\n+## Input: ##\n+## mfl, un mod\xe8le lin\xe9aire mixte g\xe9n\xe9r\xe9 par le module lmixedm de JF Martin (fonction ##\n+## lmRepeated2FF), i.e. : un mod\xe8le lin\xe9aire mixte de type lmer cr\xe9\xe9 par la formule ##\n+## mfl <- lmer( vd ~ time + fixfact + time:fixfact + (1| subject), ids) ##\n+## => noms de colonnes importants ##\n+## => 1 seul effet al\xe9atoire sur l\'intercept ##\n+###############################################################################################\n+## Output : ##\n+## Les graphics, les calculs associ\xe9s et les notations* utilis\xe9es dans le script suivent ##\n+## l\'article de Singer et al (2016) Graphical Tools for detedcting departures from linear ##\n+## mixed model assumptions and some remedial measures, International Statistical Review ##\n+## (doi:10.1111/insr.12178) ##\n+## * ajout d\'une ou 2 lettres de typage de la variables ##\n+##\n+## Script adapt\xe9 de http://www.ime.unicamp.br/~cnaber/residdiag_nlme_v22.R pour fonction- ##\n+## ner avec un mod\xe8le lmer (et non lme), des sujets avec des identifiants non num\xe9riques, ##\n+## et des observations non ordonn\xe9es sujet par sujet (dernier point \xe0 v\xe9rifier.) ##\n+## ############################################################################################\n+## Remarques sur les calculs num\xe9riques ##\n+## - l\'inverse d\'une matrice est calcul\xe9e \xe0 partir de la fonction ginv du package MASS ##\n+## (Moore- Penrose generalized inverse) au lieu de la fonction "solve" ##\n+## - la racine carr\xe9e des matrices sont calcul\xe9es gr\xe2ce \xe0 une d\xe9omposition SVD (car ##\n+## s\'applique normalement ici uniquement \xe0 des matrices sym\xe9triques positives). A ##\n+## remplacer par la fonction sqrtm{expm} si des erreurs apparaissent ??? ##\n+###############################################################################################\n+\n+\n+library(ggplot2)\n+library(gridExtra)\n+library(grid)\n+\n+\n+##-------------------------------------------------------------------------------------------------##\n+## Helpers\n+##-------------------------------------------------------------------------------------------------##\n+\n+## square root of a matrix\n+## http://www.cs.toronto.edu/~jepson/csc420/notes/introSVD.pdf (page 6)\n+## (for matMN a n x n matrix that symetric and non-negative definite)\n+\n+sqrtmF<-function(matMN){\n+ matMN <- as.matrix(matMN)\n+ # ## check that matMN is symetric\n+ # if(!all(t(matMN==matMN)))\n+ # stop("matMN must be symetric.")\n+ svd_dec <- svd(matMN)\n+ invisible(svd_dec$u%*%sqrt(diag(svd_dec$d))%*%t(svd_dec$v))\n+}\n+\n+\n+## qqplotF\n+## adapted from https://gist.github.com/rentrop/d39a8406ad8af2a1066c\n+\n+\n+qqplotF <- function(x, \n+ distribution = "norm", ..., \n+ line.estimate = NULL, \n+ conf = 0.95,\n+ labels = names(x)){\n+ q.function <- eval(parse(text = paste0("q", distribution)))\n+ d.function <- eval(parse(text = paste0("d", distribution)))\n+ x <- na.omit(x)\n+ ord <- order(x)\n+ n <- length(x)\n+ P <- ppoints(length(x))\n+ daf <- data.frame(ord.x = x[ord], z = q.function('..b' ncol=7, nrow=6, byrow=TRUE),\n+ heights= c(0.1/3, 0.3, 0.1/3, 0.3, 0.1/3, 0.3 ),\n+ widths = c(0.22, 0.04, 0.22,0.04 , 0.22, 0.04, 0.22))\n+ \n+ invisible(NULL)\n+\n+}\n+\n+#diagmflF(mfl, title = "", outlier.limit = 3, least.confounded =TRUE)\n+\n+plot.res.Lmixed <- function(mfl, df, title = "", pvalCutof = 0.05) {\n+\n+ nameVar <- colnames(df)[4]\n+ fflab <- colnames(df)[1]\n+ ## Individual time-course\n+ rawPlot <- \n+ ggplot(data = df, aes(x=df[[2]], y=df[[4]], colour=df[[1]], group=df[[3]])) +\n+ geom_line() + ggtitle("Individual time-courses (raw data)")+ \n+ ylab(nameVar) +\n+ xlab(label = colnames(df)[2])+\n+ theme(legend.title = element_blank() , legend.position="none", plot.title = element_text(size = rel(1.2), face = "bold"))\n+ \n+ ## Boxplot of fixed factor\n+ bPlot <- \n+ ggplot(data = df, aes(y=df[[4]], x=df[[1]], color=df[[1]]))+\n+ geom_boxplot(outlier.colour="red", outlier.shape=8, outlier.size=4)+\n+ ggtitle(paste("Boxplot by ",fflab,sep=""))+xlab("")+ylab("")+\n+ theme(legend.title = element_blank(), plot.title = element_text(size = rel(1.2), face = "bold"))\n+ \n+ ## Post-hoc estimates\n+ \n+ ddlsm1 <- mfl\n+ ddlsm1$name <- rownames(ddlsm1)\n+ # ddlsm1$name <- sapply(rownames(ddlsm1),\n+ # function(nam){\n+ # strsplit(nam, split = " ", fixed =TRUE)[[1]][1]\n+ # })\n+ # ddlsm1$detail <- sapply(rownames(ddlsm1),\n+ # function(nam){\n+ # paste(strsplit(nam, split = " ", fixed =TRUE)[[1]][-1],\n+ # collapse= "")\n+ # })\n+ # \n+ #colnames(ddlsm1)<- make.names(colnames(ddlsm1))\n+ ddlsm1$Significance <- rep("NS", nrow(ddlsm1))\n+ \n+ ## modif JF pour tenir compte du seuil de pvalues defini par le user \n+ options("scipen"=100, "digits"=5)\n+ pvalCutof <- as.numeric(pvalCutof)\n+ bs=0.05; bm=0.01; bi=0.005\n+ if (pvalCutof >bm) {bs <- pvalCutof} else\n+ if (pvalCutof <bm & pvalCutof >bi) {bm <- pvalCutof; bs <- pvalCutof} else\n+ if (pvalCutof < bi) {bi <- pvalCutof; bm <- pvalCutof; bs <- pvalCutof}\n+ lbs <- paste("p-value < ",bs, sep="")\n+ lbm <- paste("p-value < ",bm, sep="")\n+ lbi <- paste("p-value < ",bi, sep="")\n+ \n+ cols <- paste("p-value < ",bs, sep="")\n+ colm <- paste("p-value < ",bm, sep="")\n+ coli <- paste("p-value < ",bi, sep="")\n+ valcol <- c("grey","yellow","orange","red")\n+ names (valcol) <- c("NS",lbs,lbm,lbi)\n+ ddlsm1$Significance[which(ddlsm1$p.value<= bs)] <- lbs \n+ ddlsm1$Significance[which(ddlsm1$p.value<bs & ddlsm1$p.value>= bm)] <- lbm\n+ ddlsm1$Significance[which(ddlsm1$p.value<bi)] <- lbi\n+\n+ phPlot <- \n+ ggplot(ddlsm1, aes(x = levels, y = Estimate))+\n+ facet_grid(facets = ~term, ddlsm1,scales = "free", space = "free")+\n+ geom_bar( aes(fill = Significance), stat="identity")+\n+ theme(axis.text.x = element_text(angle = 90, hjust = 1))+\n+ scale_fill_manual(\n+ # values = c("NS" = "grey", "p-value < threshold" = "yellow","p-value < 0.01" = "orange","p-value < 0.005" = "red"))+\n+ # values = c("NS" = \'grey\', "pvalue < 0.05 "= \'yellow\',"p-value < 0.01" = \'orange\',"p-value < 0.005" = \'red\'))+\n+ # values = c("NS = grey", "p-value < threshold = yellow","p-value < 0.01 = orange","p-value < 0.005 = red"))+\n+ values = valcol )+ \n+ geom_errorbar(aes(ymin = Lower.CI, ymax =Upper.CI ), width=0.25)+\n+ # ggtitle(paste("Post-hoc estimates with p-value threshold = ",pvalCutof,sep=""))+xlab("")+\n+ ggtitle("Post-hoc estimates ")+xlab("")+\n+ theme(plot.title = element_text(size = rel(1.2), face = "bold"))\n+ \n+ ## Final plotting\n+ grid.arrange(arrangeGrob(rawPlot,bPlot,ncol=2),\n+ phPlot,nrow=2,\n+ top = textGrob(title,gp=gpar(fontsize=32,font=4))\n+ )\n+ \n+} \n\\ No newline at end of file\n' |
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| diff -r 000000000000 -r 1422de181204 mixmodel.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/mixmodel.xml Wed Oct 10 05:18:42 2018 -0400 |
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| b'@@ -0,0 +1,211 @@\n+<tool id="mixmodel" name="mixmodel" version="1.0.0">\n+ <description>ANOVA for repeated measures statistics</description>\n+\n+ <requirements>\n+ <requirement type="package" version="1.1_4">r-batch</requirement>\n+ <requirement type="package" version="1.1_13">r-lme4</requirement>\n+ <requirement type="package" version="3.0_1">r-lmertest</requirement>\n+ <requirement type="package" version="2.34.0">bioconductor-multtest</requirement>\n+ <requirement type="package" version="2.3">r-gridextra</requirement>\n+ </requirements>\n+\n+ <stdio>\n+ <exit_code range="1:" level="fatal" />\n+ </stdio>\n+\n+ <command><![CDATA[\n+ Rscript $__tool_directory__/mixmodel_wrapper.R\n+\n+ dataMatrix_in "$dataMatrix_in"\n+ sampleMetadata_in "$sampleMetadata_in"\n+ variableMetadata_in "$variableMetadata_in"\n+\n+ fixfact "$fixfact"\n+ time "$time"\n+ subject "$subject"\n+ adjC "$adjC"\n+ trf "$trf"\n+ thrN "$thrN"\n+ diaR "$diaR"\n+\n+ variableMetadata_out "$variableMetadata_out"\n+ out_graph_pdf "$out_graph_pdf"\n+\t\tout_estim_pdf "$out_estim_pdf"\n+ information "$information"\n+\n+ ]]></command>\n+\n+ <inputs>\n+ <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="variable x sample, decimal: \'.\', missing: NA, mode: numerical, sep: tabular" />\n+ <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="sample x metadata, decimal: \'.\', missing: NA, mode: character and numerical, sep: tabular" />\n+ <param name="variableMetadata_in" label="Variable metadata file" type="data" format="tabular" help="variable x metadata, decimal: \'.\', missing: NA, mode: character and numerical, sep: tabular" />\n+ <param name="fixfact" label="Fixed Factor of interest" type="text" help="Name of the column of the sample metadata table corresponding to the fixed factor"/>\n+ <param name="time" label="Repeated factor (time)" type="text" help="Name of the column of the sample metadata table corresponding to the repeated factor"/>\n+ <param name="subject" label="Subject factor" type="text" help="Name of the column of the sample metadata table corresponding to the subject factor"/>\n+ <param name="adjC" label="Method for multiple testing correction" type="select" help="">\n+ <option value="fdr">fdr</option>\n+ <option value="BH">BH</option>\n+ <option value="bonferroni">bonferroni</option>\n+ <option value="BY">BY</option>\n+ <option value="hochberg">hochberg</option>\n+ <option value="holm">holm</option>\n+ <option value="hommel">hommel</option>\n+ <option value="none">none</option>\n+ </param>\n+ <param name="trf" label="Log transform of raw data" type="select" help="Transformation of raw data">\n+ <option value="none">none</option>\n+ <option value="log10">log10</option>\n+ <option value="log2">log2</option>\n+ </param>\n+ <param name="thrN" type="float" value="0.05" label="(Corrected) p-value significance threshold" help="Must be between 0 and 1"/>\n+ <param name="diaR" label="Perform diagnostic of the residuals" type="select"\n+ help=" Used to assess the quality of models considering distribution of residuals ">\n+ <option value="yes">yes</option>\n+ <option value="no"></option>\n+ </param>\n+\n+ </inputs>\n+\n+ <outputs>\n+ <data name="variableMetadata_out" label="${tool.name}_${variableMetadata_in.name}" format="tabular"/>\n+ <data name="information" label="${tool.name}_information.txt" format="txt"/>\n+ <data name="out_graph_pdf" label="${tool.name}_diagResiduals" format="pdf"/>\n+ <data name="out_estim_pdf" label="${tool.name}_Estimates" format="pdf"/>\n+\n+ </outputs>\n+\n+ <tests>\n+ <test>\n+ '..b' the table must not contain metadata apart from row and column names; the row and column names must be identical to the rownames of the sample and variable metadata, respectively (see below)\n+|\n+\n+Sample metadata file\n+| sample x metadata **sampleMetadata** tabular separated file of the numeric and/or character sample metadata, with . as decimal and NA for missing values\n+|\n+\n+Variable metadata file\n+| variable x metadata **variableMetadata** tabular separated file of the numeric and/or character variable metadata, with . as decimal and NA for missing values\n+|\n+\n+\n+Treatment\n+| Name of the fixed factor in the sample metadata file\n+|\n+\n+Time\n+| Name of the repeated (time) factor in the sample metadata\tfile\n+|\n+\n+Subject\n+| Name of the subject (on which the repeated measurement id done) in the sample metadata file\n+|\n+\n+Method for multiple testing correction\n+| The 7 methods implemented in the \'p.adjust\' R function are available and documented as follows:\n+| "The adjustment methods include the Bonferroni correction ("bonferroni") in which the p-values are multiplied by the number of comparisons. Less conservative corrections are also included by Holm (1979) ("holm"), Hochberg (1988) ("hochberg"), Hommel (1988) ("hommel"), Benjamini and Hochberg (1995) ("BH" or its alias "fdr"), and Benjamini and Yekutieli (2001) ("BY"), respectively. A pass-through option ("none") is also included. The set of methods are contained in the p.adjust.methods vector for the benefit of methods that need to have the method as an option and pass it on to p.adjust. The first four methods are designed to give strong control of the family-wise error rate. There seems no reason to use the unmodified Bonferroni correction because it is dominated by Holm\'s method, which is also valid under arbitrary assumptions. Hochberg\'s and Hommel\'s methods are valid when the hypothesis tests are independent or when they are non-negatively associated (Sarkar, 1998; Sarkar and Chang, 1997). Hommel\'s method is more powerful than Hochberg\'s, but the difference is usually small and the Hochberg p-values are faster to compute. The "BH" (aka "fdr") and "BY" method of Benjamini, Hochberg, and Yekutieli control the false discovery rate, the expected proportion of false discoveries amongst the rejected hypotheses. The false discovery rate is a less stringent condition than the family-wise error rate, so these methods are more powerful than the others."\n+\n+\n+(Corrected) p-value significance threshold\n+|\n+|\n+\n+------------\n+Output files\n+------------\n+\n+variableMetadata_out.tabular\n+| **variableMetadata** file identical to the file given as argument plus\n+| pvalue of Shapiro normality test of the residuals\n+| pvalues of the main effects and interaction\n+| PostHoc test with difference between levels and pvalues of these difference\n+|\n+\n+mixedmodel_diagResiduals\n+| if Perform diagnostic of the residuals" is set to yes(default) a pdf file is created with a graphical\n+| representation of differences among levels of factors with a color code for significance and an error bar\n+| Then a serie of graphics are output in order to assess the distribution of residuals to check the adjustment.\n+\n+information.txt\n+| File with all messages and warnings generated during the computation\n+| The list of variables with name and a tag if it is significant for at least fixed or repeated factor.\n+\n+\n+ ]]></help>\n+\n+ <citations>\n+ <citation type="doi">10.18637/jss.v082.i13.</citation>\n+ <citation type="bibtex">@ARTICLE{fisher,\n+ author = {Benjamini Y. and Hochberg Y.,\n+ title = {Controlling the false discovery rate: a practical and powerful approach for multiple testing. Journal of the Royal Statistical Society},\n+ journal = {Series B (Methodological)},\n+ year = {1995},\n+ volume = {57},\n+ pages = {289-300}\n+ }</citation>\n+ <citation type="doi">10.1093/bioinformatics/btu813</citation>\n+ </citations>\n+\n+</tool>\n' |
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| diff -r 000000000000 -r 1422de181204 mixmodel_script.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/mixmodel_script.R Wed Oct 10 05:18:42 2018 -0400 |
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| b'@@ -0,0 +1,456 @@\n+####### R functions to perform linear mixed model for repeated measures \n+####### on a multi var dataset using 3 files as used in W4M\n+##############################################################################################################\n+lmRepeated2FF <- function(ids, ifixfact, itime, isubject, ivd, ndim, nameVar=colnames(ids)[[ivd]], \n+ pvalCutof=0.05,dffOption, visu , tit = "", least.confounded = FALSE, outlier.limit =3) \n+ {\n+ ### function to perform linear mixed model with 1 Fixed factor + Time + random factor subject\n+ ### based on lmerTest package providing functions giving the same results as SAS proc mixed\n+ options(scipen = 50, digits = 5)\n+ \n+ if (!is.numeric(ids[[ivd]])) {stop("Dependant variable is not numeric")}\n+ if (!is.factor(ids[[ifixfact]])) {stop("fixed factor is not a factor")}\n+ if (!is.factor(ids[[itime]])) {stop("Repeated factor is not a factor")}\n+ if (!is.factor(ids[[isubject]])) {stop("Random factor is not a factor")}\n+ # a ce stade, il faudrait pr?voir des tests sur la validit? du plan d\'exp?rience\n+ \n+ time <- ids[[itime]]\n+ fixfact <- ids[[ifixfact]]\n+ subject <- ids[[isubject]]\n+ vd <- ids[[ivd]]\n+ \n+ # argument of the function instead of re re-running ndim <- defColRes(ids,ifixfact,itime)\n+ # nfp : number of main factors + model infos (REML, varSubject) + normality test \n+ nfp <- ndim[1];\n+ # ncff number of comparison of the fixed factor\n+ nlff <- ndim[2]; ncff <- ndim[3]\n+ # nct number of comparison of the time factor\n+ nlt <- ndim[4] ; nct <- ndim[5]\n+ # nci number of comparison of the interaction\n+ nli <- ndim[6]; nci <- ndim[7]\n+ # number of all lmer results\n+ nresT <- ncff+nct+nci\n+ ## initialization of the result vector (1 line)\n+ ## 4 * because nresf for : pvalues + Etimates + lower CI + Upper CI\n+ res <- data.frame(array(rep(NA,(nfp + 4 * nresT))))\n+ colnames(res)[1] <- "resultLM"\n+ \n+ ### if at least one subject have data for only 1 time, mixed model is not possible and variable must be skip\n+ ### after excluding NA, table function is used to seek subjects with only 1 data\n+ ids <- ids[!is.na(ids[[ivd]]),]\n+ skip <- length(which(table(ids[[isubject]])==1))\n+ \n+ if (skip==0) {\n+ \n+ mfl <- lmer( vd ~ time + fixfact + time:fixfact + (1| subject), ids) # lmer remix\n+ \n+ # ## NL add \n+ # ### DEPLACE APRES CALCUL PVALUES AJUSTEES ET NE FAIRE QUE SI AU MOINS 1 FACTEUR SIGNIFICATIF\n+ # if(visu) diagmflF(mfl, title = tit, least.confounded = least.confounded, outlier.limit = outlier.limit)\n+ # ## end of NL add\n+ \n+ rsum <- summary(mfl,ddf = dffOption)\n+ ## test Shapiro Wilks on the residus of the model \n+ rShapiro <- shapiro.test(rsum$residuals)\n+ raov <- anova(mfl,ddf = dffOption)\n+ dlsm1 <- data.frame(difflsmeans(mfl,test.effs=NULL))\n+ ddlsm1 <- dlsm1\n+ ## save rownames and factor names\n+ rn <- rownames(ddlsm1)\n+ fn <- ddlsm1[,c(1,2)]\n+ ## writing the results on a single line\n+ namesFactEstim <- paste("estimate ",rownames(ddlsm1)[c(1:(nct+ncff))],sep="")\n+ namesFactPval <- paste("pvalue ",rownames(ddlsm1)[c(1:(nct+ncff))],sep="")\n+ namesInter <- rownames(ddlsm1)[-c(1:(nct+ncff))]\n+ #ncI <- nchar(namesInter)\n+ namesEstimate <- paste("estimate ",namesInter)\n+ namespvalues <- paste("pvalue ",namesInter)\n+ namesFactprinc <- c("pval_time","pval_trt","pval_inter")\n+ namesFactEstim <- paste("estimate ",rownames(ddlsm1)[c(1:(nct+ncff))],sep="")\n+ \n+ namesFactLowerCI <- paste("lowerCI ",rownames(ddlsm1)[c(1:(nct+ncff))],sep="")\n+ namesLowerCI <- paste("lowerCI ",namesInter,sep="")\n+ \n+ namesFactUpperCI <- paste("UpperCI ",rownames(ddlsm1)[c(1:(nct+ncff))],sep="")\n+ namesUpperCI <- paste("UpperCI ",namesInter,sep="")\n+ \n+ \n+ ### lmer results on 1 vector row\n+ # pvalue of shapiro Wilks '..b'of NL modif\n+ resLM[i-firstvar+1,] <- reslmer[[1]]\n+ }, error=function(e){cat("ERROR : ",conditionMessage(e), "\\n");})\n+ if (i==firstvar) {\n+ colnames(resLM) <- colnames(reslmer[[1]])\n+ labelRow <- reslmer[[2]]\n+ factorRow <- reslmer[[3]]\n+ }\n+ }\n+ ## for debug : ifixfact=1;itime=2;isubject=3; ivd=4;tit = colnames(dslm)[i]; ids <- subds\n+\n+ \n+ ## NL add\n+ if(visu == "yes") dev.off()\n+ ## end of NL add\n+ \n+ ## pvalue correction with p.adjust library multtest\n+ ## Possible methods of pvalue correction\n+ AdjustMeth <- c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr","none")\n+ if (length(which(pvalcorMeth == AdjustMeth))==0) pvalcorMeth <- "none"\n+\n+ if (pvalcorMeth !="none") {\n+ for (k in firstpval:lastpval){\n+ resLM[[k]]=p.adjust(resLM[[k]], method=pvalcorMeth, n=dim(resLM[k])[[1]])\n+ \n+ }\n+ }\n+\n+ ## for each variables, set pvalues to NA and estimates = 0 when pvalue of factor > pvalCutof value define by user\n+ if (ifixfact>0) {\n+ ## time effect\n+ resLM[which(resLM[,firstpval]> pvalCutof),c((lastpval+1):(lastpval+ndim[5]))] <- 0\n+ resLM[which(resLM[,firstpval]> pvalCutof),c((ndim[1]+1):(ndim[1]+ndim[5]))] <- NA\n+ ## treatment effect\n+ resLM[which(resLM[,firstpval+1]> pvalCutof),c((lastpval+ndim[5]+1):(lastpval+ndim[5]+ndim[3]))] <- 0\n+ resLM[which(resLM[,firstpval+1]> pvalCutof),c((ndim[1]+ndim[5]+1):(ndim[1]+ndim[5]+ndim[3]))] <- NA\n+ ## interaction effect\n+ resLM[which(resLM[,firstpval+2]> pvalCutof),c((lastpval+ndim[5]+ndim[3]+1):(lastpval+ndim[5]+ndim[3]+ndim[7]))] <- 0\n+ resLM[which(resLM[,firstpval+2]> pvalCutof),c((ndim[1]+ndim[5]+ndim[3]+1):(ndim[1]+ndim[5]+ndim[3]+ndim[7]))] <- NA\n+ } else {\n+ ## time effect only\n+ resLM[which(resLM[,firstpval]> pvalCutof),c((lastpval+1):(lastpval+ndim[5]))] <- 0\n+ resLM[which(resLM[,firstpval]> pvalCutof),c((firstpval+1):(firstpval+ndim[5]))] <- NA\n+ }\n+ \n+ ## for each variable, estimates plots are performed if at least one factor is significant after p-value correction\n+ pdf(pdfE, onefile=TRUE, height = 15, width = 30)\n+ #par(mfrow=c(2,2))\n+ \n+ ## for each variable (in row) \n+ for (i in 1:nrow(resLM)) {\n+ #cat("\\n",rownames(resLM)[i])\n+ ## if any main factor after p-value correction is significant -> plot estimates and time course\n+ if (length(which(resLM[i,c(4:6)]<pvalCutof))>0) {\n+ \n+ ## Plot of time course by fixfact : data prep with factors and quantitative var to be plot\n+ subv <- dslm[,colnames(dslm)==rownames(resLM)[i]]\n+ subds <- data.frame(dslm[[ifixfact]],dslm[[itime]], dslm[[isubject]],subv)\n+ #colnames(subds) <- c(colnames(dslm)[ifixfact],colnames(dslm)[itime],colnames(dslm)[isubject],rownames(resLM)[i] <- rownames(resLM)[i] )\n+ libvar <- c(fixfact,time,subject)\n+ colnames(subds) <- c(libvar,rownames(resLM)[i])\n+ \n+ ## Plot of estimates with error bars for all fixed factors and interaction\n+ rddlsm1 <- t(resLM[i,])\n+ pval <- rddlsm1[substr(rownames(rddlsm1),1,6)=="pvalue"]\n+ esti <- rddlsm1[substr(rownames(rddlsm1),1,6)=="estima"]\n+ loci <- rddlsm1[substr(rownames(rddlsm1),1,6)=="lowerC"]\n+ upci <- rddlsm1[substr(rownames(rddlsm1),1,6)=="UpperC"]\n+ rddlsm1 <- data.frame(pval,esti,loci,upci,factorRow)\n+ colnames(rddlsm1) <- c("p.value","Estimate","Lower.CI","Upper.CI",colnames(factorRow))\n+ rownames(rddlsm1) <- labelRow\n+\n+ ## function for plotting these 2 graphs\n+ plot.res.Lmixed(rddlsm1, subds, title = rownames(resLM)[i], pvalCutof = pvalCutof)\n+ \n+ }\n+ }\n+ dev.off()\n+ \n+ ## return result file with pvalues and estimates (exclude confidence interval used for plotting)\n+ iCI <- which(substr(colnames(resLM),4,7)=="erCI")\n+ resLM <- resLM[,-iCI] \n+ resLM <- cbind(varids,resLM)\n+ return(resLM)\n+}\n+\n+\n' |
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| diff -r 000000000000 -r 1422de181204 mixmodel_wrapper.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/mixmodel_wrapper.R Wed Oct 10 05:18:42 2018 -0400 |
| [ |
| @@ -0,0 +1,164 @@ +#!/usr/bin/env Rscript + +library(batch) ## parseCommandArgs + +library(lme4) ## mixed model computing +library(Matrix) +library(lmerTest) ## computing pvalue and lsmeans from results of lme4 package +library(multtest) ## multiple testing + + +source_local <- function(fname){ + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep="/")) +} + +#source_local("univariate_script.R") +source_local("mixmodel_script.R") +source_local("diagmfl.R") + +argVc <- unlist(parseCommandArgs(evaluate=FALSE)) + +##------------------------------ +## Initializing +##------------------------------ + +## options +##-------- + +strAsFacL <- options()$stringsAsFactors +options(stringsAsFactors = FALSE) + +## constants +##---------- + +modNamC <- "mixmodel" ## module name + +topEnvC <- environment() +flagC <- "\n" + +## functions +##---------- + +flgF <- function(tesC, + envC = topEnvC, + txtC = NA) { ## management of warning and error messages + + tesL <- eval(parse(text = tesC), envir = envC) + + if(!tesL) { + + sink(NULL) + stpTxtC <- ifelse(is.na(txtC), + paste0(tesC, " is FALSE"), + txtC) + + stop(stpTxtC, + call. = FALSE) + + } + +} ## flgF + +## log file +##--------- + +sink(argVc["information"]) + +cat("\nStart of the '", modNamC, "' Galaxy module call: ", format(Sys.time(), "%a %d %b %Y %X"), "\n", sep="") + +## loading +##-------- + +datMN <- t(as.matrix(read.table(argVc["dataMatrix_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t"))) + +samDF <- read.table(argVc["sampleMetadata_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t") + +varDF <- read.table(argVc["variableMetadata_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t") + + +## checking +##--------- + +flgF("identical(rownames(datMN), rownames(samDF))", txtC = "Column names of the dataMatrix are not identical to the row names of the sampleMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") +flgF("identical(colnames(datMN), rownames(varDF))", txtC = "Row names of the dataMatrix are not identical to the row names of the variableMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") + +flgF("argVc['fixfact'] %in% colnames(samDF)", txtC = paste0("Required fixed factor '" , argVc['fixfact'], "' could not be found in the column names of the sampleMetadata")) +flgF("argVc['time'] %in% colnames(samDF)", txtC = paste0("Required time factor '" , argVc['time'] , "' could not be found in the column names of the sampleMetadata")) +flgF("argVc['subject'] %in% colnames(samDF)", txtC = paste0("Required subject factor '", argVc['subject'], "' could not be found in the column names of the sampleMetadata")) + +flgF("mode(samDF[, argVc['fixfact']]) %in% c('character', 'numeric')", txtC = paste0("The '", argVc['fixfact'], "' column of the sampleMetadata should contain either number only, or character only")) +flgF("mode(samDF[, argVc['time']]) %in% c('character', 'numeric')", txtC = paste0("The '", argVc['time'] , "' column of the sampleMetadata should contain either number only, or character only")) +flgF("mode(samDF[, argVc['subject']]) %in% c('character', 'numeric')", txtC = paste0("The '", argVc['subject'], "' column of the sampleMetadata should contain either number only, or character only")) + +flgF("argVc['adjC'] %in% c('holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr', 'none')") +flgF("argVc['trf'] %in% c('none', 'log10', 'log2')") + +flgF("0 <= as.numeric(argVc['thrN']) && as.numeric(argVc['thrN']) <= 1", txtC = "(corrected) p-value threshold must be between 0 and 1") +flgF("argVc['diaR'] %in% c('no', 'yes')") + + +##------------------------------ +## Computation +##------------------------------ + + +varDF <- lmixedm(datMN = datMN, + samDF = samDF, + varDF = varDF, + fixfact = argVc["fixfact"], + time = argVc["time"], + subject = argVc["subject"], + logtr = argVc['trf'], + pvalCutof = argVc['thrN'], + pvalcorMeth= argVc["adjC"], + dffOption = "Satterthwaite", + visu = argVc["diaR"], + least.confounded = FALSE, + outlier.limit = 3, + pdfC = argVc["out_graph_pdf"], + pdfE = argVc["out_estim_pdf"] + ) + + +##------------------------------ +## Ending +##------------------------------ + + +## saving +##-------- + +varDF <- cbind.data.frame(variableMetadata = rownames(varDF), + varDF) + +write.table(varDF, + file = argVc["variableMetadata_out"], + quote = FALSE, + row.names = FALSE, + sep = "\t") + +## closing +##-------- + +cat("\nEnd of '", modNamC, "' Galaxy module call: ", + as.character(Sys.time()), "\n", sep = "") + +sink() + +options(stringsAsFactors = strAsFacL) + +rm(list = ls()) |
| b |
| diff -r 000000000000 -r 1422de181204 test-data/demo1_Samples.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/demo1_Samples.txt Wed Oct 10 05:18:42 2018 -0400 |
| b |
| @@ -0,0 +1,25 @@ +idsample idsujet treatment time +s1AT1 s1 A T1 +s2AT1 s2 A T1 +s3AT1 s3 A T1 +s4AT1 s4 A T1 +s5BT1 s5 B T1 +s6BT1 s6 B T1 +s7BT1 s7 B T1 +s8BT1 s8 B T1 +s1AT2 s1 A T2 +s2AT2 s2 A T2 +s3AT2 s3 A T2 +s4AT2 s4 A T2 +s5BT2 s5 B T2 +s6BT2 s6 B T2 +s7BT2 s7 B T2 +s8BT2 s8 B T2 +s1AT3 s1 A T3 +s2AT3 s2 A T3 +s3AT3 s3 A T3 +s4AT3 s4 A T3 +s5BT3 s5 B T3 +s6BT3 s6 B T3 +s7BT3 s7 B T3 +s8BT3 s8 B T3 |
| b |
| diff -r 000000000000 -r 1422de181204 test-data/demo1_matrix.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/demo1_matrix.txt Wed Oct 10 05:18:42 2018 -0400 |
| b |
| b'@@ -0,0 +1,41 @@\n+var\ts1AT1\ts2AT1\ts3AT1\ts4AT1\ts5BT1\ts6BT1\ts7BT1\ts8BT1\ts1AT2\ts2AT2\ts3AT2\ts4AT2\ts5BT2\ts6BT2\ts7BT2\ts8BT2\ts1AT3\ts2AT3\ts3AT3\ts4AT3\ts5BT3\ts6BT3\ts7BT3\ts8BT3\n+var1\t2.944804979\t49.75484579\t25.89657977\t31.21956172\t645.7733673\t207.8199481\t993.1398444\t771.2810657\t33.8574053\t10.27450194\t42.27358423\t0.259314085\t727.698667\t975.6024581\t501.887448\t101.55269\t26.00199056\t42.81100563\t24.51317508\t4.306905516\t255.7178467\t844.9974405\t590.486975\t586.4456671\n+var2\t3.667579463\t305.5106362\t689.8621337\t960.0407436\t515.7640893\t925.1943751\t414.9315685\t285.7495409\t948.0050691\t821.9239079\t686.8984302\t254.7828577\t866.587021\t520.530133\t835.3730004\t966.3284721\t165.9697054\t726.3462446\t72.57517711\t370.4785168\t349.7503837\t368.2889788\t592.7939878\t502.4401631\n+var3\t172.8780668\t522.9435356\t60.99162384\t981.0661058\t838.0443428\t245.4660659\t320.52462\t402.9176733\tNA\tNA\t924.6445422\t109.0789884\tNA\t885.9453218\t821.7969874\t677.6235027\tNA\tNA\t573.2824613\t809.8096851\tNA\t989.3551375\t438.6697135\t99.02984484\n+var4\t476.6964656\t877.5340214\t873.7968183\t260.3147842\t48.1446747\t49.99827418\t15.0500212\t2.454515607\t655.4373272\t158.3886794\t533.7596174\t75.0229305\t21.36363224\t7.115041484\t6.141056371\t43.55349275\t287.4224888\t833.7583586\t60.74044966\t63.68551611\t47.30457162\t33.36978463\t42.5284983\t32.03159121\n+var5\t947.6929416\t453.2570661\t815.904467\t347.4032396\t24.53297914\t787.2949824\t241.7110583\t538.24695\t506.6958296\t266.0208725\t69.09085633\t5.974715233\t337.6606509\t259.2015248\t849.6838592\t87.20042666\t426.9964488\t698.8356796\t339.6737985\t156.684501\t86.32011891\t30.19974119\t978.5537816\t199.8844321\n+var6\t31.82849796\t44.69044713\t38.31707386\t18.51302603\t214.3392118\t712.6254117\t505.3932955\t707.9672445\t46.62706509\t43.77479739\t8.943157968\t39.26259778\t662.8065096\t808.9399247\t352.764633\t205.7874917\t3.766673404\t5.418740715\t37.59618013\t24.94170082\t516.9694331\t599.1699534\t951.1241327\t531.0029822\n+var7\t48.58915493\t9.737940091\t3.070545533\t13.24393267\t35.71840252\t38.19313066\t13.71876739\t37.84443501\t219.4206035\t529.5333088\t53.1798515\t720.0361187\t752.6900262\t884.0021377\t30.21174379\t639.3453975\t240.6978432\t287.5449343\t548.0445628\t269.850493\t742.9348096\t278.06316\t486.3017402\t805.5343043\n+var8\t680.6370973\t71.8828146\t981.710732\t221.6863381\t12.03643502\t18.67598209\t34.62782874\t47.00794697\t160.3053655\t566.2693544\t829.457417\t499.9228264\t2.015472805\t30.21909563\t11.46036299\t13.81338086\t227.484243\t99.45633839\t681.8675495\t973.652002\t41.87877455\t19.80605008\t43.40502059\t44.99090783\n+var9\t635.471832\t144.3056969\t334.1090387\t194.5727886\t405.5066688\t413.3236386\t237.899457\t996.7447354\t394.5598965\t519.4043987\t334.3332952\t866.3076014\t70.92464897\t689.4803042\t641.9404835\t453.5002735\t946.3136083\t557.380772\t725.8870537\t496.8042416\t251.7627825\t818.34199\t427.2466856\t193.6539535\n+var10\t255.5452475\t349.8598576\t617.453113\t707.2373216\t370.3060574\t469.7181814\t950.858553\t16.30868366\t806.6413776\t533.2787143\t6.329383006\t474.5446616\t462.0279273\t635.0181573\t651.8771692\t230.5368272\t222.9092267\t933.4693531\t548.8575506\t930.0440301\t265.5413981\t108.8758062\t68.08971196\t51.93824899\n+var11\t838.9985259\t745.5941668\t654.6952748\t235.1477422\t596.3175073\t844.6677532\t605.1399495\t432.9177807\t222.7906951\t873.972494\t822.0492133\t36.99986055\t537.8279339\t822.7241457\t5.419705705\t722.8959106\t881.2207647\t635.6996394\t677.2407238\t714.8925607\t389.7490318\t807.083624\t436.4182718\t458.9015398\n+var12\t892.7197353\t335.7591691\t536.4785811\t74.7070765\t576.9435273\t849.4743011\t401.1719511\t956.6287203\t40.56447695\t20.80947493\t1.140173277\t13.15588959\t30.28143383\t41.51846899\t25.38649045\t20.38415994\t793.7861762\t747.996181\t964.4964041\t786.1129855\t923.5151221\t394.6684835\t826.0571324\t213.7310158\n+var13\t499.4591238\t575.155097\t743.3397119\t27.23255924\t127.2970324\t640.1411168\t139.5822564\t642.8816939\t505.788513\t749.2321969\t691.3797399\t885.1521409\t60.46414235\t591.4272376\t94.30606951\t970.3055793\t755.4305079\t932.7703718\t653.9442423\t940.2452863\t661.9597309\t255.5623762\t72.89694711\t37.33840067\n+var14\t764.8635745\t186.441139\t403.7151201\t560.2602192\t833.8914349\t191.505535\t44.84334646\t467.5189404\t7'..b'327\t0.075892853\t3.35103701\t970.023551\t322.2403265\t1099.36841\t74.43502979\t1785.51484\t1459.638754\t990.5479732\t622.4609113\t109.2705883\t195.858416\t1059.939364\t1169.690758\t279.4441535\t511.1997851\n+var28\t433.1795515\t659.3852008\t304.342599\t2.420033492\t602.8680774\t1663.739933\t1022.569927\t342.8899009\t396.0198013\t788.9557203\t944.3908309\t314.6700035\t782.0471554\t309.1982655\t420.9024191\t592.3394811\t912.6321664\t414.073431\t482.1836798\t300.8122585\t689.3589282\t407.5987977\t1970.506461\t1274.174077\n+var29\t75.1274556\t667.9528257\t949.5909143\t332.3614352\t797.9255526\t1759.324563\t1697.283532\t1353.092656\t190.6264431\t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|
| b |
| diff -r 000000000000 -r 1422de181204 test-data/demo1_variables.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/demo1_variables.txt Wed Oct 10 05:18:42 2018 -0400 |
| b |
| @@ -0,0 +1,41 @@ +var withNA +var1 0 +var2 0 +var3 1 +var4 0 +var5 0 +var6 0 +var7 0 +var8 0 +var9 0 +var10 0 +var11 0 +var12 0 +var13 0 +var14 0 +var15 0 +var16 0 +var17 0 +var18 0 +var19 0 +var20 0 +var21 0 +var22 0 +var23 0 +var24 0 +var25 0 +var26 0 +var27 0 +var28 0 +var29 0 +var30 0 +var31 0 +var32 0 +var33 0 +var34 0 +var35 0 +var36 0 +var37 0 +var38 0 +var39 0 +var40 0 |
| b |
| diff -r 000000000000 -r 1422de181204 test-data/mixmodel_demo1_variables.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mixmodel_demo1_variables.txt Wed Oct 10 05:18:42 2018 -0400 |
| b |
| b'@@ -0,0 +1,41 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|
| b |
| diff -r 000000000000 -r 1422de181204 test-data/test_raw_variable_out.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test_raw_variable_out.txt Wed Oct 10 05:18:42 2018 -0400 |
| b |
| b'@@ -0,0 +1,41 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