## ---- echo=F, message=F, results="asis"---------------------------------- # this block is invisible BiocStyle::markdown() require(STATegRa) require(Biobase) require(gridExtra) require(ggplot2) g_legend<-function(a.gplot){ tmp <- ggplot_gtable(ggplot_build(a.gplot)) leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") legend <- tmp$grobs[[leg]] return(legend)} ## ---- eval=F------------------------------------------------------------- ## library(STATegRa) # Load STATegRa package ## ---- eval=F------------------------------------------------------------- ## help(package="STATegRa") ## Package help ## ?omicsCompAnalysis ## Specific function help ## ------------------------------------------------------------------------ data("STATegRa_S3") ls() ## ------------------------------------------------------------------------ # Block1 - gene expression data B1 <- createOmicsExpressionSet(Data=Block1.PCA, pData=ed.PCA, pDataDescr=c("classname")) # Block2 - miRNA expression data B2 <- createOmicsExpressionSet(Data=Block2.PCA, pData=ed.PCA, pDataDescr=c("classname")) ## ------------------------------------------------------------------------ cc <- selectCommonComps(X=Block1.PCA, Y=Block2.PCA, Rmax=3) cc$common grid.arrange(cc$pssq, cc$pratios, ncol=2) ## ------------------------------------------------------------------------ # Block 1 PCA.selection(Data=Block1.PCA, fac.sel="single%", varthreshold=0.03)$numComps # Block2 PCA.selection(Data=Block2.PCA, fac.sel="single%", varthreshold=0.03)$numComps ## ------------------------------------------------------------------------ ms <- modelSelection(Input=list(B1,B2), Rmax=4, fac.sel="single%", varthreshold=0.03) ms ## ------------------------------------------------------------------------ discoRes <- omicsCompAnalysis(Input=list(B1, B2), Names=c("expr", "mirna"), method="DISCOSCA", Rcommon=2, Rspecific=c(2, 2), center=TRUE, scale=TRUE, weight=TRUE) jiveRes <- omicsCompAnalysis(Input=list(B1, B2), Names=c("expr", "mirna"), method="JIVE", Rcommon=2, Rspecific=c(2, 2), center=TRUE, scale=TRUE, weight=TRUE) o2plsRes <- omicsCompAnalysis(Input=list(B1, B2),Names=c("expr", "mirna"), method="O2PLS", Rcommon=2, Rspecific=c(2, 2), center=TRUE, scale=TRUE, weight=TRUE) ## ------------------------------------------------------------------------ slotNames(discoRes) ## ---- results="hide"----------------------------------------------------- # Exploring DISCO-SCA (or JIVE) score structure getScores(discoRes, part="common") getScores(discoRes, part="distinctive", block="1") getScores(discoRes, part="distinctive", block="2") # Exploring O2PLS score structure getScores(o2plsRes, part="common", block="expr") getScores(o2plsRes, part="common", block="mirna") getScores(o2plsRes, part="distinctive", block="1") getScores(o2plsRes, part="distinctive", block="2") ## ------------------------------------------------------------------------ # DISCO-SCA plotVAF getVAF(discoRes) plotVAF(discoRes) # JIVE plotVAF getVAF(jiveRes) plotVAF(jiveRes) ## ------------------------------------------------------------------------ # Scatterplot of scores variables associated to common components # DISCO-SCA plotRes(object=discoRes, comps=c(1, 2), what="scores", type="common", combined=FALSE, block="", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) # JIVE plotRes(object=jiveRes, comps=c(1, 2), what="scores", type="common", combined=FALSE, block="", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) ## ------------------------------------------------------------------------ # O2PLS # Scatterplot of scores variables associated to common components # Associated to first block p1 <- plotRes(object=o2plsRes, comps=c(1, 2), what="scores", type="common", combined=FALSE, block="expr", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) # Associated to second block p2 <- plotRes(object=o2plsRes, comps=c(1, 2), what="scores", type="common", combined=FALSE, block="mirna", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) # Combine both plots # g_legend function from # https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs legend <- g_legend(p1) grid.arrange(arrangeGrob(p1+theme(legend.position="none"), p2+theme(legend.position="none"), nrow=1), legend, heights=c(6/7, 1/7)) # Combined plot of scores variables assocaited to common components plotRes(object=o2plsRes, comps=c(1, 1), what="scores", type="common", combined=TRUE, block="", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) ## ------------------------------------------------------------------------ # DISCO-SCA scores scatterplot associated to individual components # Associated to first block p1 <- plotRes(object=discoRes, comps=c(1, 2), what="scores", type="individual", combined=FALSE, block="expr", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) # Associated to second block p2 <- plotRes(object=discoRes, comps=c(1, 2), what="scores", type="individual", combined=FALSE, block="mirna", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) # Combine plots legend <- g_legend(p1) grid.arrange(arrangeGrob(p1+theme(legend.position="none"), p2+theme(legend.position="none"), nrow=1), legend, heights=c(6/7, 1/7)) # DISCO-SCA scores combined plot for individual components plotRes(object=discoRes, comps=c(1, 1), what="scores", type="individual", combined=TRUE, block="", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) ## ------------------------------------------------------------------------ # DISCO-SCA combined plot of scores for common and individual components p1 <- plotRes(object=discoRes, comps=c(1, 1), what="scores", type="both", combined=TRUE, block="expr", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) p2 <- plotRes(object=discoRes, comps=c(1, 1), what="scores", type="both", combined=TRUE, block="mirna", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) legend <- g_legend(p1) grid.arrange(arrangeGrob(p1+theme(legend.position="none"), p2+theme(legend.position="none"), nrow=1), legend, heights=c(6/7, 1/7)) # O2PLS combined plot of scores for common and individual components p1 <- plotRes(object=o2plsRes, comps=c(1, 1), what="scores", type="both", combined=TRUE, block="expr", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) p2 <- plotRes(object=o2plsRes, comps=c(1, 1), what="scores", type="both", combined=TRUE, block="mirna", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) legend <- g_legend(p1) grid.arrange(arrangeGrob(p1+theme(legend.position="none"), p2+theme(legend.position="none"), nrow=1), legend, heights=c(6/7, 1/7)) ## ------------------------------------------------------------------------ # Loadings plot for common components # Separately for each block p1 <- plotRes(object=discoRes, comps=c(1, 2), what="loadings", type="common", combined=FALSE, block="expr", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) p2 <- plotRes(object=discoRes, comps=c(1, 2), what="loadings", type="common", combined=FALSE, block="mirna", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) grid.arrange(arrangeGrob(p1+theme(legend.position="none"), p2+theme(legend.position="none"), nrow=1), heights=c(6/7, 1/7)) # Combined plot plotRes(object=discoRes, comps=c(1, 2), what="loadings", type="common", combined=TRUE, block="", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) ## ------------------------------------------------------------------------ # Loadings plot for individual components # Separately for each block p1 <- plotRes(object=discoRes, comps=c(1, 2), what="loadings", type="individual", combined=FALSE, block="expr", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) p2 <- plotRes(object=discoRes, comps=c(1, 2), what="loadings", type="individual", combined=FALSE, block="mirna", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) grid.arrange(arrangeGrob(p1+theme(legend.position="none"), p2+theme(legend.position="none"), nrow=1), heights=c(6/7, 1/7)) # Combined plot plotRes(object=discoRes, comps=c(1, 1), what="loadings", type="individual", combined=TRUE, block="", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) ## ------------------------------------------------------------------------ p1 <- plotRes(object=discoRes, comps=c(1, 1), what="loadings", type="both", combined=TRUE, block="expr", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) p2 <- plotRes(object=discoRes, comps=c(1, 1), what="loadings", type="both", combined=TRUE, block="mirna", color="classname", shape=NULL, labels=NULL, background=TRUE, palette=NULL, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) grid.arrange(arrangeGrob(p1+theme(legend.position="none"), p2+theme(legend.position="none"), nrow=1), heights=c(6/7, 1/7)) ## ---- warning=FALSE------------------------------------------------------ # Biplot common part. DISCO-SCA biplotRes(object=discoRes, type="common", comps=c(1, 2), block="", title=NULL, colorCol="classname", sizeValues=c(2, 4), shapeValues=c(17, 0), background=TRUE, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) # Biplot common part. O2PLS p1 <- biplotRes(object=o2plsRes, type="common", comps=c(1, 2), block="expr", title=NULL, colorCol="classname", sizeValues=c(2, 4), shapeValues=c(17, 0), background=TRUE, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) p2 <- biplotRes(object=o2plsRes, type="common", comps=c(1, 2), block="mirna", title=NULL, colorCol="classname", sizeValues=c(2, 4), shapeValues=c(17, 0), background=TRUE, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) legend <- g_legend(p1) grid.arrange(arrangeGrob(p1+theme(legend.position="none"), p2+theme(legend.position="none"), nrow=1), legend, heights=c(6/7, 1/7)) # Biplot distinctive part. O2PLS p1 <- biplotRes(object=discoRes, type="individual", comps=c(1, 2), block="expr", title=NULL, colorCol="classname", sizeValues=c(2, 4), shapeValues=c(17, 0), background=TRUE, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) p2 <- biplotRes(object=discoRes, type="individual", comps=c(1, 2), block="mirna", title=NULL, colorCol="classname", sizeValues=c(2, 4), shapeValues=c(17, 0), background=TRUE, pointSize=4, labelSize=NULL, axisSize=NULL, titleSize=NULL) legend <- g_legend(p1) grid.arrange(arrangeGrob(p1+theme(legend.position="none"), p2+theme(legend.position="none"), nrow=1), legend, heights=c(6/7, 1/7)) ## ---- echo=F, message=F-------------------------------------------------- # clear the environment before starting the omicsClustering example rm(list=ls()) ## ------------------------------------------------------------------------ data("STATegRa_S1") ls() ## ------------------------------------------------------------------------ # Block1 - Expression data mRNA.ds <- createOmicsExpressionSet(Data=Block1, pData=ed, pDataDescr=c("classname")) # Block2 - miRNA expression data miRNA.ds <- createOmicsExpressionSet(Data=Block2, pData=ed, pDataDescr=c("classname")) ## ------------------------------------------------------------------------ # Create Gene-gene distance computed through mRNA data bioDistmRNA <- bioDistclass(name="mRNAbymRNA", distance=cor(t(exprs(mRNA.ds)), method="spearman"), map.name="id", map.metadata=list(), params=list()) ## ------------------------------------------------------------------------ data(STATegRa_S2) ls() ## ------------------------------------------------------------------------ MAP.SYMBOL <- bioMap(name = "Symbol-miRNA", metadata = list(type_v1="Gene", type_v2="miRNA", source_database="targetscan.Hs.eg.db", data_extraction="July2014"), map=mapdata) ## ------------------------------------------------------------------------ bioDistmiRNA <- bioDist(referenceFeatures=rownames(Block1), reference="Var1", mapping=MAP.SYMBOL, surrogateData=miRNA.ds, referenceData=mRNA.ds, maxitems=2, selectionRule="sd", expfac=NULL, aggregation="sum", distance="spearman", noMappingDist=0, filtering=NULL, name="mRNAbymiRNA") ## ------------------------------------------------------------------------ bioDistList <- list(bioDistmRNA, bioDistmiRNA) ## ------------------------------------------------------------------------ sample.weights <- matrix(0, 4, 2) sample.weights[, 1] <- c(0, 0.33, 0.67, 1) sample.weights[, 2] <- c(1, 0.67, 0.33, 0) sample.weights ## ------------------------------------------------------------------------ bioDistWList <- bioDistW(referenceFeatures=rownames(Block1), bioDistList=bioDistList, weights=sample.weights) length(bioDistWList) ## ---- warning=F---------------------------------------------------------- bioDistWPlot(referenceFeatures=rownames(Block1), listDistW=bioDistWList, method.cor="spearman") ## ------------------------------------------------------------------------ IDH1.F <- bioDistFeature(Feature="IDH1", listDistW=bioDistWList, threshold.cor=0.7) ## ---- message=F---------------------------------------------------------- bioDistFeaturePlot(data=IDH1.F) ## ---- echo=F, message=F-------------------------------------------------- # clear the environment before starting the omicsNPC example rm(list=ls()) ## ------------------------------------------------------------------------ data("TCGA_BRCA_Batch_93") ls() ## ---- results='hide'----------------------------------------------------- exprs(TCGA_BRCA_Data$RNAseq) # displays the RNAseq data exprs(TCGA_BRCA_Data$RNAseqV2) # displays the RNAseqV2 data exprs(TCGA_BRCA_Data$Microarray) # displays the Exp-Gene data ## ---- results='hide'----------------------------------------------------- pData(TCGA_BRCA_Data$RNAseq) # class of RNAseq samples pData(TCGA_BRCA_Data$RNAseqV2) # class of RNAseqV2 samples pData(TCGA_BRCA_Data$Microarray) # class of Exp-Gene samples ## ------------------------------------------------------------------------ dataTypes <- c("count", "count", "continuous") ## ------------------------------------------------------------------------ combMethods <- c("Fisher", "Liptak", "Tippett") ## ------------------------------------------------------------------------ numPerms <- 1000 ## ------------------------------------------------------------------------ numCores <- 1 ## ------------------------------------------------------------------------ verbose <- TRUE ## ------------------------------------------------------------------------ results <- omicsNPC(dataInput=TCGA_BRCA_Data, dataTypes=dataTypes, combMethods=combMethods, numPerms=numPerms, numCores=numCores, verbose=verbose)