fgseaMultilevel: Runs preranked gene set enrichment analysis.

In fgsea: Fast Gene Set Enrichment Analysis

Description

This feature is based on the adaptive multilevel splitting Monte Carlo approach. This allows us to exceed the results of simple sampling and calculate arbitrarily small P-values.

Usage

fgseaMultilevel(
  pathways,
  stats,
  sampleSize = 101,
  minSize = 1,
  maxSize = Inf,
  eps = 1e-10,
  scoreType = c("std", "pos", "neg"),
  nproc = 0,
  gseaParam = 1,
  BPPARAM = NULL,
  nPermSimple = 1000,
  absEps = NULL
)

Arguments

pathways	List of gene sets to check.
stats	Named vector of gene-level stats. Names should be the same as in 'pathways'
sampleSize	The size of a random set of genes which in turn has size = pathwaySize
minSize	Minimal size of a gene set to test. All pathways below the threshold are excluded.
maxSize	Maximal size of a gene set to test. All pathways above the threshold are excluded.
eps	This parameter sets the boundary for calculating the p value.
scoreType	This parameter defines the GSEA score type. Possible options are ("std", "pos", "neg")
nproc	If not equal to zero sets BPPARAM to use nproc workers (default = 0).
gseaParam	GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores.
BPPARAM	Parallelization parameter used in bplapply. Can be used to specify cluster to run. If not initialized explicitly or by setting 'nproc' default value 'bpparam()' is used.
nPermSimple	Number of permutations in the simple fgsea implementation for preliminary estimation of P-values.
absEps	deprecated, use 'eps' parameter instead

Value

A table with GSEA results. Each row corresponds to a tested pathway. The columns are the following

• pathway – name of the pathway as in 'names(pathway)';

• pval – an enrichment p-value;

• padj – a BH-adjusted p-value;

• log2err – the expected error for the standard deviation of the P-value logarithm.

• ES – enrichment score, same as in Broad GSEA implementation;

• NES – enrichment score normalized to mean enrichment of random samples of the same size;

• size – size of the pathway after removing genes not present in 'names(stats)'.

• leadingEdge – vector with indexes of leading edge genes that drive the enrichment, see http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading.

Examples

data(examplePathways)
data(exampleRanks)
fgseaMultilevelRes <- fgseaMultilevel(examplePathways, exampleRanks, maxSize=500)

Example output

Warning message:
In fgseaMultilevel(examplePathways, exampleRanks, maxSize = 500) :
  For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.

fgsea documentation built on Nov. 8, 2020, 5:22 p.m.