sbea: Set-based enrichment analysis (SBEA)
In EnrichmentBrowser: Seamless navigation through combined results of set-based and network-based enrichment analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

sbeaMethods()

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

`method`	Set-based enrichment analysis method. Currently, the following set-based enrichment analysis methods are supported: ‘ora’, ‘safe’, ‘gsea’, ‘padog’, ‘roast’, ‘camera’, ‘gsa’, ‘gsva’, ‘globaltest’, ‘samgs’, ‘ebm’, and ‘mgsa’. For basic ora also set 'perm=0'. Default is ‘ora’. This can also be a user-defined function implementing a set-based enrichment method. See Details.
`se`	Expression dataset. An object of class `SummarizedExperiment`. Mandatory minimal annotations: colData column storing binary group assignment (named "GROUP") rowData column storing (log2) fold changes of differential expression between sample groups (named "FC") rowData column storing adjusted (corrected for multiple testing) p-values of differential expression between sample groups (named "ADJ.PVAL") Additional optional annotations: colData column defining paired samples or sample blocks (named "BLOCK") metadata slot named "annotation" giving the organism under investigation in KEGG three letter code (e.g. "hsa" for Homo sapiens) metadata slot named "dataType" indicating the expression data type ("ma" for microarray, "rseq" for RNA-seq)
`gs`	Gene sets. Either a list of gene sets (character vectors of 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 sample group assignments. Defaults to 1000. For basic ora set 'perm=0'. Using method="gsea" and 'perm=0' invokes the permutation approximation from the npGSEA package.
`padj.method`	Method for adjusting nominal gene set p-values to multiple testing. For available methods see the man page of the stats function `p.adjust`. Defaults to'none', i.e. leaves the nominal gene set p-values unadjusted.
`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.
`assay`	Character. The name of the assay for enrichment analysis if `se` is a `SummarizedExperiment` with multiple assays. Defaults to `"auto"`, which automatically determines the appropriate assay based on data type provided and enrichment method selected. See details.
`...`	Additional arguments passed to individual sbea methods. This includes currently for ORA and MGSA: beta: Log2 fold change significance level. Defaults to 1 (2-fold). sig.stat: decides which statistic is used for determining significant DE genes. Options are: 'p' (Default): genes with adjusted p-value below alpha. 'fc': genes with abs(log2(fold change)) above beta '&': p & fc (logical AND) '\|': p \| fc (logical OR) 'xxp': top xx % of genes sorted by adjusted p-value 'xxfc' top xx % of genes sorted by absolute log2 fold change.

'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' and 'gs' 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.

Using a SummarizedExperiment with *multiple assays*:

For the typical use case within the EnrichmentBrowser workflow this will be a SummarizedExperiment with two assays: (i) an assay storing the *raw* expression values, and (ii) an assay storing the *norm*alized expression values as obtained with the normalize function.

In this case, assay = "auto" will *auto*matically determine the assay based on the data type provided and the enrichment method selected. For usage outside of the typical workflow, the assay argument can be used to provide the name of the assay for the enrichment analysis.

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.

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

Geistlinger at al. (2020) Towards a gold standard for benchmarking gene set enrichment analysis. Briefings in Bioinformatics.

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

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

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.

    # 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)
    {
        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)