Application of enrichment methods to multiple datasets

This function applies selected methods for enrichment analysis to selected datasets of a compendium.

Usage

runEA(
  exp.list,
  methods,
  gs,
  perm = 1000,
  parallel = NULL,
  save2file = FALSE,
  out.dir = NULL,
  ...
)

Arguments

exp.list

Experiment list. A list of datasets, each being of class SummarizedExperiment. In case of just one dataset a single SummarizedExperiment is also allowed. See the documentation of sbea for required minimal annotations.

methods

Methods for enrichment analysis. This can be either

• a character vector with method names chosen from sbeaMethods and nbeaMethods,

• a user-defined function implementing a method for enrichment analysis, or

• a named list, containing pre-defined and/or user-defined enrichment methods. See examples.

gs

Gene sets, i.e. a list of character vectors of gene IDs.

perm

Number of permutations of the sample group assignments. Defaults to 1000. Can also be an integer vector matching the length of methods to assign different numbers of permutations for different methods.

parallel

Parallel computation mode. An instance of class BiocParallelParam. See the vignette of the BiocParallel package for switching between serial, multi-core, and grid execution. Defaults to NULL, which then uses the first element of BiocParallel::registered() for execution. If not changed by the user, this accordingly defaults to multi-core execution on the local host.

save2file

Logical. Should results be saved to file for subsequent benchmarking? Defaults to FALSE.

out.dir

Character. Determines the output directory where results are saved to. Defaults to NULL, which then writes to tools::R_user_dir("GSEABenchmarkeR") in case save2file is set to TRUE.

...

Additional arguments passed to the selected enrichment methods.

Value

A list with an entry for each method applied. Each method entry is a list with an entry for each dataset analyzed. Each dataset entry is a list of length 2, with the first element being the runtime and the second element being the gene set ranking, as obtained from applying the respective method to the respective dataset.

See also

sbea and nbea for carrying out set- and network-based enrichment analysis.

BiocParallelParam and register for configuration of parallel computation.

Author

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

Examples

    # loading three datasets from the GEO2KEGG compendium
    geo2kegg <- loadEData("geo2kegg", nr.datasets=3)
#> Loading GEO2KEGG data compendium ...

    # only considering the first 1000 probes for demonstration
    geo2kegg <- lapply(geo2kegg, function(d) d[1:1000,]) 

    # preprocessing and DE analysis for two of the datasets
    geo2kegg <- maPreproc(geo2kegg[2:3])
#> Summarizing probe level expression ...
    geo2kegg <- runDE(geo2kegg)

    # getting a subset of human KEGG gene sets
    gs.file <- system.file("extdata/hsa_kegg_gs.gmt", package="EnrichmentBrowser")
    kegg.gs <- EnrichmentBrowser::getGenesets(gs.file)

    # applying two methods to two datasets 
    res <- runEA(geo2kegg, methods=c("ora", "camera"), gs=kegg.gs, perm=0)

    # applying a user-defined 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)
    }  
    res <- runEA(geo2kegg, methods=dummySBEA, gs=kegg.gs)

    # applying a mix of pre-defined and user-defined methods
    methods <- list(camera = "camera", dummySBEA = dummySBEA)
    res <- runEA(geo2kegg, methods, gs=kegg.gs, perm=0)