evalTypeIError: Evaluation of the type I error rate of enrichment methods

Description Usage Arguments Value Author(s) See Also Examples

View source: R/benchmark.R

Description

This function evaluates the type I error rate of selected methods for enrichment analysis when applied to one or more expression datasets.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
evalTypeIError(
  methods,
  exp.list,
  gs,
  alpha = 0.05,
  ea.perm = 1000,
  tI.perm = 1000,
  perm.block.size = -1,
  summarize = TRUE,
  save2file = FALSE,
  out.dir = NULL,
  verbose = TRUE,
  ...
)

Arguments

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.

exp.list

Experiment list. A list of datasets, each being of class SummarizedExperiment.

gs

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

alpha

Numeric. Statistical significance level. Defaults to 0.05.

ea.perm

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

tI.perm

Integer. Number of permutations of the sample group assignments during type I error rate evaluation. Defaults to 1000. Can also be an integer vector matching the length of methods to assign different numbers of permutations for different methods.

perm.block.size

Integer. When running in parallel, splits tI.perm into blocks of the indicated size. Defaults to -1, which indicates to not partition tI.perm.

summarize

Logical. If TRUE (default) applies summary to the vector of type I error rates across tI.perm permutations of the sample labels. Use FALSE to return the full vector of type I error rates.

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.

verbose

Logical. Should progress be reported? Defaults 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 either a summary (summarize=TRUE) or the full vector of type I error rates (summarize=FALSE) across tI.perm permutations of the sample labels.

Author(s)

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

See Also

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

BiocParallelParam and register for configuration of parallel computation.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
    # 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", package="EnrichmentBrowser")
    gs.file <- file.path(gs.file, "hsa_kegg_gs.gmt") 
    kegg.gs <- EnrichmentBrowser::getGenesets(gs.file)

    # evaluating type I error rate of two methods on two datasets
    # NOTE: using a small number of permutations for demonstration;
    #       for a meaningful evaluation tI.perm should be >= 1000   
    res <- evalTypeIError(geo2kegg, methods=c("ora", 
             "camera"), gs=kegg.gs, ea.perm=0, tI.perm=3)

    # applying a user-defined enrichment method ...
    # ... or a mix of pre-defined and user-defined methods
    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)
    }  

    methods <- list(camera = "camera", dummySBEA = dummySBEA)
    res <- evalTypeIError(methods, geo2kegg, gs=kegg.gs, tI.perm=3)   

waldronlab/GSEABenchmarkeR documentation built on July 20, 2021, 2:14 p.m.