HTGM: HTGM

In HTGM: High Throughput 'GoMiner'

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
)

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

Value

returns the matrix of significant categories versus study

Examples

## Not run: 
# 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)

## End(Not run)

HTGM documentation built on April 12, 2025, 1:30 a.m.