gsea.kegg {phenoTest} | R Documentation |
The function obtains the GO or Kegg gene sets and perfomrs GSEA analysis
as implemented in the gsea
function.
gsea.go(x,species='Hs', ontologies='MF', logScale=TRUE, absVals=FALSE, averageRepeats=FALSE, B=1000, mc.cores=1, test="perm", p.adjust.method="none", pval.comp.method="original", pval.smooth.tail=TRUE,minGenes=10,maxGenes=500,center=FALSE) gsea.kegg(x,species='Hs', logScale=TRUE, absVals=FALSE, averageRepeats=FALSE, B=1000, mc.cores=1, test="perm", p.adjust.method="none", pval.comp.method="original", pval.smooth.tail=TRUE,minGenes=10,maxGenes=500,center=FALSE)
x |
|
species |
a single character value specifying the species: "Dm" ("Drosophila_ melanogaster"), "Hs" ("Homo_sapiens"), "Rn" ("Rattus_norvegicus"), "Mm" ("Mus_musculus") or "Ce" ("Caenorhabditis_elegans")). |
ontologies |
a single character value or a character vector specifying an ontology or multiple ontologies. The current version provides the following choices: "BP", "CC" and "MF" |
logScale |
if values should be log scaled. |
absVals |
if TRUE fold changes and hazard ratios that are negative will be turned into positive before starting the process. This is useful when genes can go in both directions. |
averageRepeats |
if x is of class numeric and has repeated names (several measures for some indivdual names) we can average the measures of the same names. |
B |
number of simulations to perform. |
mc.cores |
number of processors to use. |
test |
the test that will be used. 'perm' stands for the permutation based method, 'wilcox' stands for the wilcoxon test (this is the fastest one) and 'ttperm' stands for permutation t test. |
p.adjust.method |
p adjustment method to be used. Common options
are 'BH', 'BY', 'bonferroni' or 'none'. All available options and
their explanations can be found on the |
pval.comp.method |
the p value computation method. Has to be one of 'signed' or 'original'. The default one is 'original'. See details for more information. |
pval.smooth.tail |
if we want to estimate the tail of the ditribution where the pvalues will be generated. |
minGenes |
gene sets with less than minGenes genes will be removed from the analysis. |
maxGenes |
gene sets with more than maxGenes genes will be removed from the analysis. |
center |
if we want to center scores (fold changes or hazard ratios). The following is will be done: x = x-mean(x). |
This function relies on the following packages: GSEABase, GO.db.
For more information about how the gene sets are obtained see the man
page of the functions getGo
and/or getKegg
.
For more information about the implemented GSEA see the man page of th
function gsea
.
a list of gene sets, with names as GO pathway IDs. Each gene set is a character vector of Entrez gene identifiers.
Evarist Planet.
getGo
##load libs #library(KEGG.db) #library(org.Hs.eg.db) ##get data #data(eset.genelevel) #eset.genelevel ##prepare vars2test #survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE) #colnames(survival) <- c('event','time') #vars2test <- list(survival=survival,categorical='ER.Status') ##run ExpressionPhenoTest #epheno <- ExpressionPhenoTest(eset.genelevel,vars2test,p.adjust.method='none') #epheno ##run gsea with kegg gene sets. #gseaData <- gsea.kegg(epheno[,1],'Hs') #summary(gseaData) #plot(gseaData[[1]],gseaData[[2]],selGsets='hsa04062')