### R code from vignette source 'miRNApath.Rnw' ################################################### ### code chunk number 1: miRNApath.Rnw:61-62 ################################################### library("miRNApath") ################################################### ### code chunk number 2: miRNApath.Rnw:79-94 ################################################### ## Start with miRNA data from this package data(mirnaobj); ## Write a file as example of required input write.table(mirnaobj@mirnaTable, file="mirnaTable.txt", quote=FALSE, row.names=FALSE, col.names=TRUE, na="", sep="\t"); ## Now essentially load it back, but assign column headers mirnaobj <- loadmirnapath( mirnafile="mirnaTable.txt", pvaluecol="P-value", groupcol="GROUP", mirnacol="miRNA Name", assayidcol="ASSAYID" ); ## Display summary information for the resulting object mirnaobj; ################################################### ### code chunk number 3: miRNApath.Rnw:103-104 ################################################### mirnaobj@columns["pvaluecol"] <- "P-value"; ################################################### ### code chunk number 4: miRNApath.Rnw:111-113 ################################################### mirnaobj <- filtermirnapath( mirnaobj, pvalue=0.05, expression=NA, foldchange=NA ); ################################################### ### code chunk number 5: miRNApath.Rnw:122-133 ################################################### ## Again we load data from the package data(mirnaobj); ## Write a file as example of required miRNA-gene input write.table(mirnaobj@mirnaGene, file="mirnaGene.txt", quote=FALSE, row.names=FALSE, col.names=TRUE, na="", sep="\t"); ## For consistency to a new user's workflow, remove pathways mirnaobj@pathwaycount = 0; mirnaobj@mirnaPathways = data.frame(); ################################################### ### code chunk number 6: miRNApath.Rnw:135-143 ################################################### ## Load the miRNA to gene associations mirnaobj <- loadmirnatogene( mirnafile="mirnaGene.txt", mirnaobj=mirnaobj, mirnacol="miRNA Name", genecol="Entrez Gene ID", columns=c(assayidcol="ASSAYID") ); ## Display summary, noting the miRNA-gene predictions mirnaobj; ################################################### ### code chunk number 7: miRNApath.Rnw:154-161 ################################################### ## Again we load data from the package data(mirnaobj); ## Write a file as example of required input write.table(mirnaobj@mirnaPathways, file="mirnaPathways.txt", quote=FALSE, row.names=FALSE, col.names=TRUE, na="", sep="\t"); ################################################### ### code chunk number 8: miRNApath.Rnw:163-170 ################################################### ## Load the gene to pathway associations mirnaobj <- loadmirnapathways( mirnaobj=mirnaobj, pathwayfile="mirnaPathways.txt", pathwaycol="Pathway Name", genecol="Entrez Gene ID"); ## Display summary, noting the number of pathways reported mirnaobj; ################################################### ### code chunk number 9: miRNApath.Rnw:179-183 ################################################### Groups = unique(mirnaobj@mirnaTable[, mirnaobj@columns["groupcol"] ]); mirnaobj <- runEnrichment( mirnaobj=mirnaobj, Composite=TRUE, groups=Groups[grep("^AD.+(UP|DOWN)",Groups)], permutations=0 ); ################################################### ### code chunk number 10: miRNApath.Rnw:196-202 ################################################### finaltable <- mirnaTable( mirnaobj, groups=NULL, format="Tall", Significance=0.1, pvalueTypes=c("pvalues","permpvalues"), maxStringLength=42 ); ## Display only the first few rows of the best P-values... finaltable[sort(finaltable[,"pvalues"], index.return=TRUE)$ix,][1:5,]; ################################################### ### code chunk number 11: heatmap ################################################### ## Example which calls heatmap function on the resulting data widetable <- mirnaTable( mirnaobj, groups=NULL, format="Wide", Significance=0.3, na.char=NA, pvalueTypes=c("pvalues") ); ## Assign 1 to NA values, assuming they're all equally ## non-significant widetable[is.na(widetable)] <- 1; ## Display a heatmap of the result across sample groups pathwaycol <- mirnaobj@columns["pathwaycol"]; pathwayidcol <- mirnaobj@columns["pathwayidcol"]; rownames(widetable) <- apply(widetable[,c(pathwaycol, pathwayidcol)], 1, function(i)paste(i, collapse="-")); wt <- as.matrix(widetable[3:dim(widetable)[2]], mode="numeric"); pathwaySubset = apply(wt, 1, function(i)length(i[i<0.2])>1) ## Print out a heatmap par(ps="8"); heatmap(log2(wt[pathwaySubset,]), scale="row", cexRow=0.9, margins=c(15,12)); ################################################### ### code chunk number 12: miRNApath.Rnw:245-247 (eval = FALSE) ################################################### ## grid.newpage() ##