miRNApath-package: miRNApath: Pathway Enrichment for miRNA Expression Data

In miRNApath: miRNApath: Pathway Enrichment for miRNA Expression Data

Description

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.

Details

Package:	miRNApath
Type:	Package
Version:	1.0
Date:	2008-04-02
License:	LGL-2.1, see COPYING.LIB

Author(s)

James M. Ward

Maintainer: James M. Ward <[e-mail]>

References

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.

See Also

loadmirnapath, filtermirnapath, loadmirnatogene, loadmirnapathways, runEnrichment

Examples

## 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)

miRNApath documentation built on Nov. 8, 2020, 4:52 p.m.