EnrichmentBrowser (version 1.0.3)

sbea: Set-based enrichment analysis (SBEA)

Description

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

Usage

sbea(method=c("ora", "safe", "gsea", "samgs"), eset, gs,  alpha=0.05, perm=1000, out.file=NULL, browse=FALSE)
    sbea.methods()

Arguments

method

Set-based enrichment analysis method. Currently, the following set-based enrichment analysis methods are supported: ‘ora’, ‘safe’, ‘gsea’, and ‘samgs’. See Details. For basic ora also set 'perm=0'. Default is ‘ora’. This can also be the name of a tailored function implementing set-based enrichment. See Details.

eset

Expression set. Either an object of class 'ExpressionSet' or an absolute file path to an RData file containing the gene expression set. See 'read.eset' and 'probe.2.gene.eset' for required annotations in the pData and fData slot.

gs

Gene sets. Either a list of gene sets (vectors of KEGG gene IDs) or a text file in GMT format storing all gene sets under investigation.

alpha

Statistical significance level. Defaults to 0.05.

perm

Number of permutations of the expression matrix to estimate the null distribution. Defaults to 1000. For basic ora set 'perm=0'.

out.file

Optional output file the gene set ranking will be written to.

browse

Logical. Should results be displayed in the browser for interactive exploration? Defaults to FALSE.

Value

sbea.methods: 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 'ea.browse' and from which a flat gene set ranking can be extracted by calling 'gs.ranking'. If 'out.file' is given, the ranking is written to the specified file.

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

It is also possible to use additional set-based enrichment methods. This requires to implement a function that takes 'eset', '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.

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: read.eset, probe.2.gene.eset get.kegg.genesets to retrieve gene sets from KEGG.

Output: gs.ranking to retrieve the ranked list of gene sets. ea.browse for exploration of resulting gene sets.

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

Examples

    # currently supported methods
    sbea.methods()

    # (1) reading the expression data from file
    exprs.file <- system.file("extdata/ALL_exprs.tab", package="EnrichmentBrowser")
    pdat.file <- system.file("extdata/ALL_pData.tab", package="EnrichmentBrowser")
    fdat.file <- system.file("extdata/ALL_fData.tab", package="EnrichmentBrowser")
    probe.eset <- read.eset(exprs.file, pdat.file, fdat.file)

    # (2) summarizing probe expression on gene level
    gene.eset <- probe.2.gene.eset(probe.eset) 

    # (3) getting all human KEGG gene sets
    # hsa.gs <- get.kegg.genesets("hsa")
    gs.file <- system.file("extdata/hsa_kegg_gs.gmt", package="EnrichmentBrowser")
    hsa.gs <- parse.genesets.from.GMT(gs.file)

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

    # (5) result visualization and exploration
    gs.ranking(ea.res)
    
    ea.browse(ea.res)
    

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

    sbea.res2 <- sbea(method="dummy.sbea", eset=gene.eset, gs=hsa.gs)
    gs.ranking(sbea.res2)