| Title: | Gene Set Analysis |
|---|---|
| Description: | Gene Set Analysis. |
| Authors: | Brad Efron and R. Tibshirani |
| Maintainer: | Rob Tibshirani <[email protected]> |
| License: | LGPL |
| Version: | 1.03.3 |
| Built: | 2024-12-17 06:51:07 UTC |
| Source: | CRAN |
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
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)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)
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 |
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
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 |
Robert Tibshirani
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.
######### 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$genesets, # resp.type="Two class unpaired", nperms=100) # ########## two class unpaired comparison # y must take values 1,2 set.seed(100) x<-matrix(rnorm(1000*20),ncol=20) dd<-sample(1:1000,size=100) u<-matrix(2*rnorm(100),ncol=10,nrow=100) x[dd,11:20]<-x[dd,11:20]+u y<-c(rep(1,10),rep(2,10)) genenames=paste("g",1:1000,sep="") #create some random gene sets genesets=vector("list",50) for(i in 1:50){ genesets[[i]]=paste("g",sample(1:1000,size=30),sep="") } geneset.names=paste("set",as.character(1:50),sep="") GSA.obj<-GSA(x,y, genenames=genenames, genesets=genesets, resp.type="Two class unpaired", nperms=100) GSA.listsets(GSA.obj, geneset.names=geneset.names,FDRcut=.5) #to use "real" gene set collection, we read it in from a gmt file: # # geneset.obj<- GSA.read.gmt("file.gmt") # # where file.gmt is a gene set collection from GSEA collection or # or the website http://www-stat.stanford.edu/~tibs/GSA, or one # that you have created yourself. Then # GSA.obj<-GSA(x,y, genenames=genenames, genesets=geneset.obj$genesets, # resp.type="Two class unpaired", nperms=100) # #
"Correlates" a gene set collection with a given list of gene names. Gives info on the overlap between the collection and the list of genes
GSA.correlate(GSA.genesets.obj, genenames)GSA.correlate(GSA.genesets.obj, genenames)
GSA.genesets.obj |
Gene set collection, created for example by GSA.read.gmt |
genenames |
Vector of gene names in expression daatset |
Gives info on the overlap between a gene set collection and the list of gene names. This is for information purposes, to find out, for example, how many genes in the list of genes appear in the gene set collection.
Robert Tibshirani
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
######### 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.correlate(genesets, genenames)######### 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.correlate(genesets, genenames)
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.
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"))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"))
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") |
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.
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 |
Robert Tibshirani
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
######### 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$genesets, # resp.type="Two class unpaired") # ########## two class unpaired comparison # y must take values 1,2 set.seed(100) x<-matrix(rnorm(1000*20),ncol=20) dd<-sample(1:1000,size=100) u<-matrix(2*rnorm(100),ncol=10,nrow=100) x[dd,11:20]<-x[dd,11:20]+u y<-c(rep(1,10),rep(2,10)) genenames=paste("g",1:1000,sep="") #create some random gene sets genesets=vector("list",50) for(i in 1:50){ genesets[[i]]=paste("g",sample(1:1000,size=30),sep="") } geneset.names=paste("set",as.character(1:50),sep="") GSA.func.obj<-GSA.func(x,y, genenames=genenames, genesets=genesets, resp.type="Two class unpaired") #to use "real" gene set collection, we read it in from a gmt file: # # geneset.obj<- GSA.read.gmt("file.gmt") # # where file.gmt is a gene set collection from GSEA collection or # or the website http://www-stat.stanford.edu/~tibs/GSA, or one # that you have created yourself. Then # GSA.func.obj<-GSA.func(x,y, genenames=genenames, # genesets=geneset.obj$genesets, # resp.type="Two class unpaired") # #
Compute individual gene scores from a gene set analysis
GSA.genescores(geneset.number, genesets, GSA.obj, genenames, negfirst=FALSE)GSA.genescores(geneset.number, genesets, GSA.obj, genenames, negfirst=FALSE)
geneset.number |
Number indicating which gene set is to examined |
genesets |
The gene set collection |
GSA.obj |
Object returned by function GSA |
genenames |
Vector of gene names for gene in expression dataset |
negfirst |
Should negative genes be listed first? Default FALSE |
Compute individual gene scores from a gene set analysis. Useful for looking “inside” a gene set that has been called significant by GSA.
A list with components
res |
Matrix of gene names and gene scores (eg t-statistics) for each gene in the gene set |
,
Robert Tibshirani
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
######### 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) # look at 10th gene set GSA.genescores(10, genesets, GSA.obj, genenames)######### 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) # look at 10th gene set GSA.genescores(10, genesets, GSA.obj, genenames)
List the results from a call to GSA (Gene set analysis)
GSA.listsets(GSA.obj, geneset.names = NULL, maxchar = 20, FDRcut = 0.2)GSA.listsets(GSA.obj, geneset.names = NULL, maxchar = 20, FDRcut = 0.2)
GSA.obj |
Object returned by GSA function |
.
geneset.names |
Optional vector of names for the gene sets |
maxchar |
Maximum number of characters in printed output |
FDRcut |
False discovery rate cutpoint for listed sets. A value of 1 will cause all sets to be listed |
.
This function list the sigificant gene sets, based on a call to the GSA (Gene set analysis) function.
A list with components
FDRcut |
The false discovery rate threshold used. |
negative |
A table of the negative gene sets. "Negative" means that lower expression of most genes in the gene set correlates with higher values of the phenotype y. Eg for two classes coded 1,2, lower expression correlates with class 2. For survival data, lower expression correlates with higher risk, i.e shorter survival (Be careful, this can be confusing!) |
positive |
A table of the positive gene sets. "Positive" means that higher expression of most genes in the gene set correlates with higher values of the phenotype y. See "negative" above for more info. |
nsets.neg |
Number of negative gene sets |
nsets.pos |
Number of positive gene sets |
Robert Tibshirani
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
######### 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 radnom 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)######### 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 radnom 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)
Creates features from a GSA analysis that can be used in other procedures, for example, sample classification.
GSA.make.features(GSA.func.obj, x, genesets, genenames)GSA.make.features(GSA.func.obj, x, genesets, genenames)
GSA.func.obj |
Object returned by GSA.func |
x |
Expression dataset from which the features are to be created |
genesets |
Gene set collection |
genenames |
Vector of gene names in expression dataset |
Creates features from a GSA analysis that can be used in other procedures, for example, sample classification. For example, suppose the GSA analysis computes a maxmean score for gene set 1 that is positive, based on the mean of the positive part of the scores in that gene set. Call the subset of genes with positive scores "A". Then we compute a new feature for this geneset, for each sample, by computing the mean of the scores for genes in A, setting other gene scores to zero.
Robert Tibshirani
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
######### 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") GSA.make.features(GSA.func.obj, x, genesets, genenames)######### 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") GSA.make.features(GSA.func.obj, x, genesets, genenames)
Plots the results from a call to GSA (Gene set analysis)
GSA.plot(GSA.obj, fac=1, FDRcut = 1)GSA.plot(GSA.obj, fac=1, FDRcut = 1)
GSA.obj |
Object returned by GSA function |
.
fac |
value for jittering points in plot ("factor" in called to jitter() |
FDRcut |
False discovery rate cutpoint for sets to be plotted. A value of 1 (the default) will cause all sets to be plotted |
.
This function makes a plot of the significant gene sets, based on a call to the GSA (Gene set analysis) function.
Robert Tibshirani
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
######### 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 radnom 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) GSA.plot(GSA.obj)######### 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 radnom 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) GSA.plot(GSA.obj)
Read in a gene set collection from a .gmt file
GSA.read.gmt(filename)GSA.read.gmt(filename)
filename |
The name of a file to read data values from. Should be a tab-separated text file, with one row per gene set. Column 1 has gene set names (identifiers), column 2 has gene set descriptions, remaining columns are gene ids for genes in that geneset |
.
This function reads in a geneset collection from a .gmt text file, and creates an R object that can be used as input into GSA. We use UniGene symbols for our gene set names in our .gmt files and expression datasets, to match the two. However the user is free to use other identifiers, as long as the same ones are used in the gene set collections and expression datasets.
A list with components
genesets |
List of gene names (identifiers) in each gene set |
,
geneset.names |
Vector of gene set names (identifiers) |
,
geneset.descriptions |
Vector of gene set descriptions |
Robert Tibshirani
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
# read in functional pathways gene set file from Broad institute GSEA website # http://www.broad.mit.edu/gsea/msigdb/msigdb_index.html # You have to register first and then download the file C2.gmt from # their site #GSA.read.gmt(C2.gmt)# read in functional pathways gene set file from Broad institute GSEA website # http://www.broad.mit.edu/gsea/msigdb/msigdb_index.html # You have to register first and then download the file C2.gmt from # their site #GSA.read.gmt(C2.gmt)