GSA: Gene set analysis

In GSA: Gene Set Analysis

Gene set analysis

Description

Determines the significance of pre-defined sets of genes with respect to an outcome variable, such as a group indicator, a quantitative variable or a survival time

Usage

GSA(x,y, genesets, genenames,
method=c("maxmean","mean","absmean"),
resp.type=c("Quantitative","Two class unpaired","Survival","Multiclass",
            "Two class paired", "tCorr", "taCorr"),
censoring.status=NULL,random.seed=NULL,  knn.neighbors=10,
s0=NULL, s0.perc=NULL,minsize=15,maxsize=500,
restand=TRUE,restand.basis=c("catalog","data"),
 nperms=200, 
xl.mode=c("regular","firsttime","next20","lasttime"), 
xl.time=NULL, xl.prevfit=NULL)

Arguments

x	Data x: p by n matrix of features (expression values), one observation per column (missing values allowed); y: n-vector of outcome measurements
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
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, coded 1,2,3...; "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
random.seed	Optional initial seed for random number generator (integer)
knn.neighbors	Number of nearest neighbors to use for imputation of missing features values
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")
nperms	Number of permutations used to estimate false discovery rates
xl.mode	Used by Excel interface
xl.time	Used by Excel interface
xl.prevfit	Used by Excel interface

Details

Carries out a Gene set analysis, as described in the paper by Efron and Tibshirani (2006). It differs from a Gene Set Enrichment Analysis (Subramanian et al 2006) in its use of the "maxmean" statistic: this is the mean of the positive or negative part of gene scores in the gene set, whichever is large in absolute values. Efron and Tibshirani shows that this is often more powerful than the modified KS statistic used in GSEA. GSA also does "restandardization" of the genes (rows), on top of the permutation of columns (done in GSEA). Gene set analysis is applicable to microarray data and other data with a large number of features. This is also the R package that is called by the "official" SAM Excel package v3.0. The format of the response vector y and the calling sequence is illustrated in the examples below. A more complete description is given in the SAM manual at http://www-stat.stanford.edu/~tibs/SAM

Value

A list with components

GSA.scores	Gene set scores for each gene set
GSA.scores.perm	Matrix of Gene set scores from permutions, one column per permutation
fdr.lo	Estimated false discovery rates for negative gene sets (negative means lower expression correlates with class 2 in two sample problems, lower expression correlates with increased y for quantitative problems, lower expression correlates with higher risk for survival problems)
fdr.hi	Estimated false discovery rates for positive gene sets; positive is opposite of negative, as defined above
pvalues.lo	P-values for negative gene sets
pvalues.hi	P-values for positive gene sets
stand.info	Information from restandardization process
stand.info.star	Information from restandardization process in permutations
ngenes	Number of genes in union of 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. s0= s0.perc percentile of the gene standard deviations
call	The call to GSA
x	For internal use
y	For internal use
genesets	For internal use
genenames	For internal use
r.obs	For internal use
r.star	For internal use
gs.mat	For internal use
gs.ind	For internal use
catalog	For internal use
catalog.unique	For internal use

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

Subramanian, A. and Tamayo, P. Mootha, V. K. and Mukherjee, S. and Ebert, B. L. and Gillette, M. A. and Paulovich, A. and Pomeroy, S. L. and Golub, T. R. and Lander, E. S. and Mesirov, J. P. (2005) A knowledge-based approach for interpreting genome-wide expression profiles. PNAS. 102, pg 15545-15550.

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.obj<-GSA(x,y, genenames=genenames, genesets=genesets,
             resp.type="Two class unpaired", nperms=100)


GSA.listsets(GSA.obj, geneset.names=geneset.names,FDRcut=.5)



#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.obj<-GSA(x,y, genenames=genenames, genesets=geneset.obj$genesets,
#                resp.type="Two class unpaired", nperms=100)
#
#

GSA documentation built on March 19, 2022, 5:05 p.m.