HTGM (version 1.1)

HTGM: HTGM

Description

driver to invoke GoMiner for multiple studies, and integrate the results in a categories versus study hyperlinked heatmap

Usage

HTGM(
  title = NULL,
  dir = tempdir(),
  sampleLists,
  GOGOA3,
  ONT,
  enrichThresh = 2,
  countThresh = 5,
  fdrThresh = 0.1,
  nrand = 100,
  verbose = TRUE
)

Value

returns the matrix of significant categories versus study

Arguments

title

character string descriptive title

dir

character string full pathname to the directory acting as result repository

sampleLists

list of character vector of user-supplied genes of interest

GOGOA3

return value of subsetGOGOA()

ONT

c("molecular_function","cellular_component","biological_process")

enrichThresh

numerical acceptance threshold for enrichment passed to GoMiner

countThresh

numerical acceptance threshold for gene count passed to GoMiner

fdrThresh

numerical acceptance threshold for fdr passed to GoMiner

nrand

integer number of randomizations passed to GoMiner

verbose

Boolean if TRUE, GoMiner will message some helpful diagnostics

Examples

if (FALSE) {
# this example takes too long to run, and
# GOGOA3.RData is too large to include in the R package
# so I need to load it from a file that is not in the package.
# Since this is in a file in my own file system, I could not
# include this as a regular example in the package.
# This example is given in full detail in the package vignette.
# You can generate GOGOA3.RData using the package 'minimalistGODB'
# or you can retrieve it from https://github.com/barryzee/GO

# load("data/Housekeeping_Genes.RData")
# load("~/GODB_RDATA/GOGOA3.RData")
sampleList<-unique(as.matrix(Housekeeping_Genes[,"Gene.name"]))
n<-nrow(sampleList)
sampleLists<-list()
# test the effect of random sampling of the entire gene set
# this can give an idea of the quality of the GoMiner results
# when the complete gene set is yet to be determined
sampleLists[["1"]]<-sampleList[sample(n,n/2)]
sampleLists[["2"]]<-sampleList[sample(n,n/2)]
sampleLists[["3"]]<-sampleList[sample(n,n/2)]
sampleLists[["4"]]<-sampleList[sample(n,n/2)]
sampleLists[["5"]]<-sampleList[sample(n,n/2)]
sampleLists[["ALL"]]<-sampleList
m<-HTGM(title=NULL,dir=tempdir(),sampleLists,GOGOA3,ONT="biological_process",
 enrichThresh=2,countThresh=5,fdrThresh=0.10,nrand=100,verbose=TRUE)
}