Set-based enrichment analysis (SBEA)

Description

This is the main function for the enrichment analysis of gene sets. It implements and wraps existing implementations of several frequently used methods and allows a flexible inspection of resulting gene set rankings.

Usage

sbeaMethods()

sbea(method = EnrichmentBrowser::sbeaMethods(), se, gs, alpha = 0.05,
  perm = 1000, padj.method = "none", out.file = NULL,
  browse = FALSE, ...)

Arguments

Details

'ora': overrepresentation analysis, simple and frequently used test based on the hypergeometric distribution (see Goeman and Buhlmann, 2007, for a critical review).

'safe': significance analysis of function and expression, generalization of ORA, includes other test statistics, e.g. Wilcoxon's rank sum, and allows to estimate the significance of gene sets by sample permutation; implemented in the safe package (Barry et al., 2005).

'gsea': gene set enrichment analysis, frequently used and widely accepted, uses a Kolmogorov-Smirnov statistic to test whether the ranks of the p-values of genes in a gene set resemble a uniform distribution (Subramanian et al., 2005).

'padog': pathway analysis with down-weighting of overlapping genes, incorporates gene weights to favor genes appearing in few pathways versus genes that appear in many pathways; implemented in the PADOG package.

'roast': rotation gene set test, uses rotation instead of permutation for assessment of gene set significance; implemented in the limma and edgeR packages for microarray and RNA-seq data, respectively.

'camera': correlation adjusted mean rank gene set test, accounts for inter-gene correlations as implemented in the limma and edgeR packages for microarray and RNA-seq data, respectively.

'gsa': gene set analysis, differs from GSEA by using the maxmean statistic, i.e. the mean of the positive or negative part of gene scores in the gene set; implemented in the GSA package.

'gsva': gene set variation analysis, transforms the data from a gene by sample matrix to a gene set by sample matrix, thereby allowing the evaluation of gene set enrichment for each sample; implemented in the GSVA package.

'globaltest': global testing of groups of genes, general test of groups of genes for association with a response variable; implemented in the globaltest package.

'samgs': significance analysis of microarrays on gene sets, extends the SAM method for single genes to gene set analysis (Dinu et al., 2007).

'ebm': empirical Brown's method, combines $p$-values of genes in a gene set using Brown's method to combine $p$-values from dependent tests; implemented in the EmpiricalBrownsMethod package.

'mgsa': model-based gene set analysis, Bayesian modeling approach taking set overlap into account by working on all sets simultaneously, thereby reducing the number of redundant sets; implemented in the mgsa package.

It is also possible to use additional set-based enrichment methods. This requires to implement a function that takes 'se', 'gs', 'alpha', and 'perm' as arguments and returns a numeric vector 'ps' storing the resulting p-value for each gene set in 'gs'. This vector must be named accordingly (i.e. names(ps) == names(gs)). See examples.

Value

sbeaMethods: a character vector of currently supported methods;

sbea: if(is.null(out.file)): an enrichment analysis result object that can be detailedly explored by calling eaBrowse and from which a flat gene set ranking can be extracted by calling gsRanking. If 'out.file' is given, the ranking is written to the specified file.

Author(s)

Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>

References

Goeman and Buhlmann (2007) Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics, 23, 980-7.

Barry et al. (2005) Significance Analysis of Function and Expression. Bioinformatics, 21:1943-9.

Subramanian et al. (2005) Gene Set Enrichment Analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA, 102:15545-50.

Dinu et al. (2007) Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics, 8:242

See Also

Input: readSE, probe2gene getGenesets to retrieve gene sets from databases such as GO and KEGG.

Output: gsRanking to retrieve the ranked list of gene sets. eaBrowse for exploration of resulting gene sets.

Other: nbea to perform network-based enrichment analysis. combResults to combine results from different methods.

Examples

    # currently supported methods
    sbeaMethods()

    # (1) expression data: 
    # simulated expression values of 100 genes
    # in two sample groups of 6 samples each
    se <- makeExampleData(what="SE")
    se <- deAna(se)

    # (2) gene sets:
    # draw 10 gene sets with 15-25 genes
    gs <- makeExampleData(what="gs", gnames=names(se))

    # (3) make 2 artificially enriched sets:
    sig.genes <- names(se)[rowData(se)$ADJ.PVAL < 0.1]
    gs[[1]] <- sample(sig.genes, length(gs[[1]])) 
    gs[[2]] <- sample(sig.genes, length(gs[[2]]))   

    # (4) performing the enrichment analysis
    ea.res <- sbea(method="ora", se=se, gs=gs, perm=0)

    # (5) result visualization and exploration
    gsRanking(ea.res)

    # using your own tailored function as enrichment method
    dummySBEA <- function(se, gs, alpha, perm)
    {
        sig.ps <- sample(seq(0, 0.05, length=1000), 5)
        nsig.ps <- sample(seq(0.1, 1, length=1000), length(gs)-5)
        ps <- sample(c(sig.ps, nsig.ps), length(gs))
        names(ps) <- names(gs)
        return(ps)
    }

    ea.res2 <- sbea(method=dummySBEA, se=se, gs=gs)
    gsRanking(ea.res2)