miRNApath (version 1.32.0)

miRNApath-package: 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

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)