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Perform enrichment analysis for coexpression network modules

Usage

module_enrichment(
  net = NULL,
  background_genes,
  annotation,
  column = NULL,
  correction = "BH",
  p = 0.05,
  min_setsize = 10,
  max_setsize = 500,
  bp_param = BiocParallel::SerialParam()
)

Arguments

net

List object returned by exp2gcn.

background_genes

Character vector of genes to be used as background for the Fisher's Exact Test.

annotation

Annotation data frame with genes in the first column and functional annotation in the other columns. This data frame can be exported from Biomart or similar databases.

column

Column or columns of annotation to be used for enrichment. Both character or numeric values with column indices can be used. If users want to supply more than one column, input a character or numeric vector. Default: all columns from annotation.

correction

Multiple testing correction method. One of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr" or "none". Default is "BH".

p

P-value threshold. P-values below this threshold will be considered significant. Default is 0.05.

min_setsize

Numeric indicating the minimum gene set size to be considered. Gene sets correspond to levels of each variable in annotation). Default: 10.

max_setsize

Numeric indicating the maximum gene set size to be considered. Gene sets correspond to levels of each variable in annotation). Default: 500.

bp_param

BiocParallel back-end to be used. Default: BiocParallel::SerialParam()

Value

A data frame of overrepresentation results with the following variables:

term

character, functional term ID/name.

genes

numeric, intersection length between input genes and genes in a particular functional term.

all

numeric, number of all genes in a particular functional term.

pval

numeric, P-value for the hypergeometric test.

padj

numeric, P-value adjusted for multiple comparisons using the method specified in parameter adj.

category

character, name of the grouping variable (i.e., column name of annotation).

module

character, module name.

Author

Fabricio Almeida-Silva

Examples

# \donttest{
data(filt.se)
data(zma.interpro)
background <- rownames(filt.se)
gcn <- exp2gcn(filt.se, SFTpower = 18, cor_method = "pearson")
#> ..connectivity..
#> ..matrix multiplication (system BLAS)..
#> ..normalization..
#> ..done.
mod_enrich <- module_enrichment(gcn, background, zma.interpro, p=1)
#> Enrichment analysis for module black...
#> Enrichment analysis for module blue...
#> Enrichment analysis for module brown...
#> Enrichment analysis for module green...
#> Enrichment analysis for module red...
#> Enrichment analysis for module yellow...
# }