runEA: Application of enrichment methods to multiple datasets
In GSEABenchmarkeR: Reproducible GSEA Benchmarking

Description Usage Arguments Value Author(s) See Also Examples

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

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

`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.

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.

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

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

BiocParallelParam and register for configuration of parallel computation.

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

    # 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])
    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)