piano (version 1.12.0)

GSAheatmap: Heatmap of top significant gene sets

Description

This function selects the top scoring (most significant) gene sets for each directionality class and produces a heatmap plot of the results.

Usage

GSAheatmap(gsaRes, cutoff=5, adjusted=FALSE, ncharLabel=25, cellnote="pvalue", columnnames="full", colorkey=TRUE, colorgrad=NULL, cex=NULL)

Arguments

gsaRes

an object of class GSAres, as returned from runGSA().

cutoff

an integer n, so that the top n gene sets (plus possible ties) in each directionality class will be included in the heatmap.

adjusted

a logical, whether to use adjusted p-values or not. Note that if runGSA was run with the argument adjMethod="none", the adjusted p-values will be equal to the original p-values.

ncharLabel

the number of characters to include in the row labels.

cellnote

a character string selecting the information to be printed inside each cell of the heatmap. Either "pvalue", "rank", "nGenes" or "none". Note that the actual heatmap will always be based on the gene set ranks.

columnnames

either "full" (default) or "abbr" to use full or abbreviated column labels. Will save some space for the heatmap if set to "abbr"

colorkey

a logical (default TRUE), whether or not to display the colorkey. Will save some space for the heatmap if turned off.

colorgrad

a character vector giving the color names to use in the heatmap.

cex

a numeric, to control the text size.

Value

A list, returned invisibly, containing the matrix of p-values (adjusted or non-adjusted depending on the settings) as represented in the heatmap as well as the matrix of corresponding ranks and the matrix of number of genes in each gene set (inlcuding the subset of up and down regulated genes for the mixed directional classes).

Details

This function selects the top significant gene sets in each directionality class and draws a heatmap of the results. It provides a quick summary alternative to the GSAsummaryTable function or the networkPlot.

See Also

piano, runGSA, GSAsummaryTable, networkPlot

Examples

   # Load example input data to GSA:
   data("gsa_input")
   
   # Load gene set collection:
   gsc <- loadGSC(gsa_input$gsc)
      
   # Run gene set analysis:
   gsares <- runGSA(geneLevelStats=gsa_input$pvals , directions=gsa_input$directions, 
                    gsc=gsc, nPerm=500)
                    
   # Make heatmap:
   dev.new(width=10,height=10)
   GSAheatmap(gsares)