| 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)