plotCLEANscore: Function to generate a diagnostic plot using the CLEAN score

In uc-bd2k/CLEAN: Clustering Enrichment Analysis

Function to generate a diagnostic plot using the CLEAN score

Description

This function generates a plot of the number of genes with CLEANscores <= a threshold against that threshold. These plots can be used, for example, to compare different clustering algorithms.

Usage

plotCLEANscore(fClustAnnotations, fCategoryName = "GO", maxCLEANscore = NULL, add = FALSE, ...)

Arguments

fClustAnnotations	A list of functional cluster annotations generated by the runCLEAN() function or the generateTreeViewFiles() function
fCategoryName	Name of the list of functional categories to be used for the comparison
maxCLEANscore	Maximal CLEANscore. If specified, this parameter is used to specify the xlim parameter in the plot() function.
add	If TRUE, plot will be added to existing graph.
...	Additional parameters to the plot() function

Author(s)

Johannes Freudenberg

See Also

generateTreeViewFiles, runCLEAN,plot, lines

Examples

## not run
#data(gimmOut)
#require(org.Rn.eg.db)
#allGenes <- unique(keys(org.Rn.egSYMBOL))
# plotCLEANscore is intended for larger datasets. Here we use the larger 
# background gene list to somewhat better demonstrate the 
# plotCLEANscore() function.  In real life, the background list should be
# the list of genes represented on the microarray
#fClustAnnotations <- runCLEAN(gimmOut, bkgList = allGenes, 
#	functionalCategories="CpGislands",species = "Rn", 
#	maxGenesInCategory=10000)
#plotCLEANscore(fClustAnnotations, fCategoryName="CpGislands")

uc-bd2k/CLEAN documentation built on Sept. 22, 2022, 4:12 a.m.