IGSAinput-class: IGSAinput S4 class implementation in R
In MIGSA: Massive and Integrative Gene Set Analysis

Description Slots See Also Examples

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.

ExprData-class

SEAparams-class

GSEAparams-class

IGSAinput-getterSetters

getDEGenes

MIGSA

summary

## 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)