miRNApath (version 1.32.0)

mirnaTable: Create miRNA Enrichment Summary Table as data.frame

Description

This function takes an miRNApath object which has been evaluated by runEnrichment(), and provides a data.frame summary.

Usage

mirnaTable(mirnaobj, groups=NULL, format="Tall",  Significance=0.2, na.char=NA, pvalueTypes=c("pvalues", "permpvalues"), maxStringLength=NA)

Arguments

mirnaobj

An object of type mirnapath containing data resulting from the loadmirnapath method.

groups

List of groups to include in the data.frame, or NULL to include all groups in the miRNApath object.

format

This parameter tells the method to return "Tall", "SuperTall", or "Wide" data. See details below for a description of each format.

Significance

A numerical value specifying the P-value cutoff to use to subset the data returned in the data.frame. To avoid subsetting the data, provide a value of 1.

na.char

Value to use for NA instead of leaving NA as-is, potentially useful for text output.

pvalueTypes

Defines which P-value columns should be returned, more useful for the Wide format which could otherwise have two sets of P-value columns if permutation adjustment were used.

maxStringLength

Defines the maximum length per character string, after being determined by nchar. Strings and column headers are both truncated to this length.

Value

data.frameFor Tall data, the columns contain P-values and other values useful for discriminating potential hits, the rows contain each miRNA-group combination tested which meets the P-value cutoff. The miRNAs and genes contributing to the enrichment results are concatenated to be summarized in one row and can be rather large.For SuperTall data, the Tall table as described above is returned, except that the concatenated miRNA-gene values are separated to one row each. Every individual miRNA and gene value is represented on its own row, which can facilitate some summary views or data filtering techniques (e.g. Excel or Spotfire.)For Wide data, the columns contain the group names, the rows contain the pathway name, and the cells contain the P-value. Note that the column names will have the P-value column header prepended to the column name, e.g. "pvalue.GroupName".An important note when supplying string na.char values, be sure to convert the data to a numeric matrix before calling functions such as heatmap, taking care to remove string values or convert strings to 1.0 beforehand.

Details

This function simply combines the various results from the runEnrichment method into one data.frame suitable for plotting or printing in a table. Due to potentially large data volume, the subset feature even when used liberally can substantially reduce the returned dataset size.

The maxStringLength value is particularly useful, often critical, for displaying a summary table in text format, since pathway names sample group names can be quite long. Although there is no default, a recommended value of 50 seems to fit the appropriate balance of being short enough to fit within a table, and yet be long enough to describe the pathway. The Wide format will contain sample group names as column headers, and a value of 50 should not in theory affect the name, except where it wouldn't be readable in a table anyway.

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

## Start with miRNA data from this package
data(mirnaobj);

## 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[1:20,];

## 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");