Perform overrepresentation analysis for a set of genes

Perform overrepresentation analysis for a set of genes

Usage

ora(
  genes,
  annotation,
  column = NULL,
  background,
  correction = "BH",
  alpha = 0.05,
  min_setsize = 5,
  max_setsize = 500,
  bp_param = BiocParallel::SerialParam()
)

Arguments

genes

Character vector containing genes for overrepresentation analysis.

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.

background

Character vector of genes to be used as background for the overrepresentation analysis.

correction

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

alpha

Numeric indicating the adjusted P-value threshold for significance. Default: 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: 5.

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).

Examples

data(se_chlamy)
data(go_chlamy)
data(deg_list)

# Perform ORA for up-regulated genes in contrast F1_vs_P1
up_genes <- deg_list$F1_vs_P1
up_genes <- rownames(up_genes[up_genes$log2FoldChange > 0, ])

background <- rownames(se_chlamy)

ora(up_genes, go_chlamy, background = background)
#>               term genes all         pval       padj category
#> 133 dynein complex     8  13 1.924882e-05 0.00802676       GO