inst/unitTests/test_inputdatacontainers.R

In rcastelo/GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data

test_inputdatacontainers <- function() {
    message("Running unit tests for input data containers.")

    p <- 10 ## number of genes
    n <- 30 ## number of samples
    nGrp1 <- 15 ## number of samples in group 1
    nGrp2 <- n - nGrp1 ## number of samples in group 2

    ## consider three disjoint gene sets
    geneSets <- list(set1=paste("g", 1:3, sep=""),
                     set2=paste("g", 4:6, sep=""),
                     set3=paste("g", 7:10, sep=""))

    ## sample data from a normal distribution with mean 0 and st.dev. 1
    ## seeding the random number generator for the purpose of this test
    set.seed(123)
    y <- matrix(rnorm(n*p), nrow=p, ncol=n,
                dimnames=list(paste("g", 1:p, sep="") , paste("s", 1:n, sep="")))

    ## genes in set1 are expressed at higher levels in the last 'nGrp1+1' to 'n' samples
    y[geneSets$set1, (nGrp1+1):n] <- y[geneSets$set1, (nGrp1+1):n] + 2

    ## estimate GSVA enrichment scores with input as a matrix
    es.mat <- gsva(gsvaParam(y, geneSets), verbose=FALSE)

    ## estimate GSVA enrichment scores with input as an ExpressionSet object
    y2 <- y
    rownames(y2) <- NULL
    eset <- Biobase::ExpressionSet(assayData=y2,
                                   phenoData=as(data.frame(dummy=1:ncol(y),
                                                           row.names=colnames(y)),
                                                "AnnotatedDataFrame"),
                                   featureData=as(data.frame(dummy=1:nrow(y),
                                                             row.names=rownames(y)),
                                                  "AnnotatedDataFrame"))
    es.eset <- gsva(gsvaParam(eset, geneSets), verbose=FALSE)

    ## as of 1.51.9, gene sets will be returned as attributes for containers not
    ## inheriting from SummarizedExperiment and interfere with the check
    es.mat2 <- es.mat
    attr(es.mat2, "geneSets") <- NULL
    attr(es.eset, "geneSets") <- NULL
    checkTrue(identical(es.mat2, Biobase::exprs(es.eset)))

    ## estimate GSVA enrichment scores with input as a SummarizedExperiment object
    se <- SummarizedExperiment::SummarizedExperiment(assay=list(counts=y2),
                                                     rowData=S4Vectors::DataFrame(data.frame(dummy=1:nrow(y),
                                                                                             row.names=rownames(y))),
                                                     colData=S4Vectors::DataFrame(data.frame(dummy=1:ncol(y),
                                                                                             row.names=colnames(y))))
    es.se <- gsva(gsvaParam(se, geneSets), verbose=FALSE)

    checkTrue(identical(es.mat2, SummarizedExperiment::assays(es.se)[[1]]))

    ## estimate GSVA enrichment scores with input as a dgCMatrix object
    yMat <- Matrix::Matrix(y, sparse=TRUE)

    ## as of 1.51.x, this will actually return a dgCMatrix instead of a matrix
    es.dgCMat <- gsva(gsvaParam(yMat, geneSets), verbose=FALSE)
    esAsMatrix <- as(es.dgCMat, "matrix")

    checkTrue(identical(es.mat, esAsMatrix))
}