IGSAinput-class {MIGSA} | R Documentation |
This S4 class contains all the necessary inputs to execute a functional analysis (SEA and GSEA) on one experiment. Important: Make sure that gene IDs are concordant between the expression matrix and the provided gene sets.
name
character indicating the name of this experiment.
expr_data
ExprData object with the expression data (MicroArray or RNAseq). Note: expr_data can be a 0x0 matrix only if gsea_params=NULL and de_genes and br slots from sea_params are correctly set (vectors of gene names), in this case only SEA will be run.
fit_options
FitOptions object with the parameters to be used when fitting the model.
gene_sets_list
named list of GeneSetCollection objects to be tested for enrichment (names must be unique).
sea_params
SEAparams object with the parameters to be used by SEA, if NULL then SEA wont be run.
gsea_params
GSEAparams object with the parameters to be used by GSEA, if NULL then GSEA wont be run.
## Lets create a basic IGSAinput object. ## First create a expression matrix. maData <- matrix(rnorm(10000),ncol=4); rownames(maData) <- 1:nrow(maData); # It must have rownames (gene names). maExprData <- new("MAList",list(M=maData)); ## Now lets create the FitOptions object. myFOpts <- FitOptions(c("Cond1", "Cond1", "Cond2", "Cond2")); ## Finally lets create the Genesets to test for enrichment. library(GSEABase); myGs1 <- GeneSet(as.character(1:10), setIdentifier="fakeId1", setName="fakeName1"); myGs2 <- GeneSet(as.character(7:15), setIdentifier="fakeId2", setName="fakeName2"); myGSs <- GeneSetCollection(list(myGs1, myGs2)); ## And now we can create our IGSAinput ready for MIGSA. igsaInput <- IGSAinput(name="igsaInput", expr_data=maExprData, fit_options=myFOpts, gene_sets_list=list(myGSs=myGSs)); ## Valid IGSAinput object with no expr_data (only run SEA). igsaInput <- IGSAinput(name="igsaInput", gene_sets_list=list(myGSs=myGSs), gsea_params=NULL, sea_params=SEAparams(de_genes=rownames(maExprData)[1:10], br=rownames(maExprData))); validObject(igsaInput);