miRNApath-package {miRNApath} | R Documentation |
This package provides methods for assessing the statistical over-representation of miRNA effects on gene sets, using supplied miRNA-to-gene associations. Because these associations are notably many-to-many (one miRNA to many genes; one gene affected by many miRNAs) the assessment is complex and warrants perhaps different approaches than are classically performed on differential gene expression datasets.
Package: | miRNApath |
Type: | Package |
Version: | 1.0 |
Date: | 2008-04-02 |
License: | LGL-2.1, see COPYING.LIB |
James M. Ward
Maintainer: James M. Ward <jmw86069@gmail.com>
John Cogswell (2008) Identification of miRNA changes in Alzheimer's disease brain and CSF yields putative biomarkers and insights into disease pathways, Journal of Alzheimer's Disease 14, 27-41.
loadmirnapath
,
filtermirnapath
,
loadmirnatogene
,
loadmirnapathways
,
runEnrichment
## Not run: ## 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" ); ## Start with miRNA data from this package data(mirnaobj); ## Write a file as example of required input write.table(mirnaobj@mirnaGene, file = "mirnaGene.txt", quote = FALSE, row.names = FALSE, col.names = TRUE, na = "", sep = "\t"); ## Load the miRNA to gene associations mirnaobj <- loadmirnatogene( mirnafile = "mirnaGene.txt", mirnaobj = mirnaobj, mirnacol = "miRNA Name", genecol = "Entrez Gene ID", columns = c(assayidcol = "ASSAYID") ); ## 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"); ## Load the gene to pathway associations mirnaobj <- loadmirnapathways( mirnaobj = mirnaobj, pathwayfile = "mirnaPathways.txt", pathwaycol = "Pathway Name", genecol = "Entrez Gene ID"); ## Annotate hits by filtering by P-value 0.05 mirnaobj <- filtermirnapath( mirnaobj, pvalue = 0.05, expression = NA, foldchange = NA ); ## Now run enrichment test mirnaobj <- runEnrichment( mirnaobj=mirnaobj, Composite=TRUE, groups=NULL, permutations=0 ); ## Print out a summary table of significant results finaltable <- mirnaTable( mirnaobj, groups=NULL, format="Tall", Significance=0.1, pvalueTypes=c("pvalues") ); finaltable[1:4,]; ## Example which calls heatmap function on the resulting data widetable <- mirnaTable( mirnaobj, groups=NULL, format="Wide", Significance=0.1, 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") heatmap(wt, scale="col"); ## Show results where pathways are shared in four or more ## sample groups pathwaySubset <- apply(wt, 1, function(i) { length(i[i < 1]) >= 4; } ) heatmap(wt[pathwaySubset,], scale="row"); ## End(Not run)