GSA.func: Gene set analysis without permutations

In GSA: Gene Set Analysis

Gene set analysis without permutations

Description

Determines the significance of pre-defined sets of genes with respect to an outcome variable, such as a group indicator, quantitative variable or survival time. This is the basic function called by GSA.

Usage

GSA.func(x,y, genesets, genenames,geneset.names=NULL,
 method=c("maxmean","mean","absmean"),
 resp.type=c("Quantitative","Two class unpaired","Survival","Multiclass",
             "Two class paired",  "tCorr", "taCorr" ),
censoring.status=NULL,
 first.time = TRUE, return.gene.ind = TRUE, 
ngenes = NULL, gs.mat =NULL, gs.ind = NULL,
 catalog = NULL, catalog.unique =NULL, 
s0 = NULL, s0.perc = NULL, minsize = 15, maxsize= 500, restand = TRUE,
restand.basis=c("catalog","data")) 

Arguments

x	Data x: p by n matrix of features, one observation per column (missing values allowed)
y	Vector of response values: 1,2 for two class problem, or 1,2,3 ... for multiclass problem, or real numbers for quantitative or survival problems
genesets	Gene set collection (a list)
genenames	Vector of genenames in expression dataset
geneset.names	Optional vector of gene set names
method	Method for summarizing a gene set: "maxmean" (default), "mean" or "absmean"
resp.type	Problem type: "quantitative" for a continuous parameter; "Two class unpaired" ; "Survival" for censored survival outcome; "Multiclass" : more than 2 groups; "Two class paired" for paired outcomes, coded -1,1 (first pair), -2,2 (second pair), etc
censoring.status	Vector of censoring status values for survival problems, 1 mean death or failure, 0 means censored)
first.time	internal use
return.gene.ind	internal use
ngenes	internal use
gs.mat	internal use
gs.ind	internal use
catalog	internal use
catalog.unique	internal use
s0	Exchangeability factor for denominator of test statistic; Default is automatic choice
s0.perc	Percentile of standard deviation values to use for s0; default is automatic choice; -1 means s0=0 (different from s0.perc=0, meaning s0=zeroeth percentile of standard deviation values= min of sd values
minsize	Minimum number of genes in genesets to be considered
maxsize	Maximum number of genes in genesets to be considered
restand	Should restandardization be done? Default TRUE
restand.basis	What should be used to do the restandardization? The set of genes in the genesets ("catalog", the default) or the genes in the data set ("data")

Details

Carries out a Gene set analysis, computing the gene set scores. This function does not do any permutations for estimation of false discovery rates. GSA calls this function to estimate FDRs.

Value

A list with components

scores

Gene set scores for each gene set

,

norm.scores

Gene set scores transformed by the inverse Gaussian cdf

,

mean	Means of gene expression values for each sample
sd	Standard deviation of gene expression values for each sample
gene.ind	List indicating whch genes in each positive gene set had positive individual scores, and similarly for negative gene sets
geneset.names	Names of the gene sets
nperms	Number of permutations used
gene.scores	Individual gene scores (eg t-statistics for two class problem)
s0	Computed exchangeability factor
s0.perc	Computed percentile of standard deviation values
stand.info	Information computed used in the restandardization process
method	Method used (from call to GSA.func)
call	The call to GSA

Author(s)

Robert Tibshirani

References

Efron, B. and Tibshirani, R. On testing the significance of sets of genes. Stanford tech report rep 2006. http://www-stat.stanford.edu/~tibs/ftp/GSA.pdf

Examples

######### two class unpaired comparison
# y must take values 1,2

set.seed(100)
x<-matrix(rnorm(1000*20),ncol=20)
dd<-sample(1:1000,size=100)

u<-matrix(2*rnorm(100),ncol=10,nrow=100)
x[dd,11:20]<-x[dd,11:20]+u
y<-c(rep(1,10),rep(2,10))


genenames=paste("g",1:1000,sep="")

#create some random gene sets
genesets=vector("list",50)
for(i in 1:50){
 genesets[[i]]=paste("g",sample(1:1000,size=30),sep="")
}
geneset.names=paste("set",as.character(1:50),sep="")

GSA.func.obj<-GSA.func(x,y, genenames=genenames, genesets=genesets,  resp.type="Two class unpaired")




#to use  "real" gene set collection, we read it in from a gmt file:
# 
# geneset.obj<- GSA.read.gmt("file.gmt")
# 
# where file.gmt is a gene set collection from GSEA collection or
#  or the website http://www-stat.stanford.edu/~tibs/GSA, or one
# that you have created yourself. Then

#   GSA.func.obj<-GSA.func(x,y, genenames=genenames,
#                          genesets=geneset.obj$genesets,
#                          resp.type="Two class unpaired")
#
#

GSA documentation built on May 29, 2024, 7:23 a.m.