run_enrichment: run_enrichment

In cinaR: A Computational Pipeline for Bulk 'ATAC-Seq' Profiles

run_enrichment

Description

This function is run, if the enrichment pipeline wants to be called afterwards. Setting reference genome to the same genome which cinaR was run should be given to this function!

Usage

run_enrichment(
  results,
  geneset = NULL,
  experiment.type = "ATAC-Seq",
  reference.genome = NULL,
  enrichment.method = NULL,
  enrichment.FDR.cutoff = 1,
  background.genes.size = 20000,
  verbose = TRUE
)

Arguments

results	list, DA peaks list for different contrasts
geneset	Pathways to be used in enrichment analyses. If not set vp2008 (Chaussabel, 2008) immune modules will be used. This can be set to any geneset using 'read.gmt' function from 'qusage' package. Different modules are available: https://www.gsea-msigdb.org/gsea/downloads.jsp.
experiment.type	The type of experiment either set to "ATAC-Seq" or "RNA-Seq"
reference.genome	genome of interested species. It should be 'hg38', 'hg19' or 'mm10'.
enrichment.method	There are two methodologies for enrichment analyses, Hyper-geometric p-value (HPEA) or Geneset Enrichment Analyses (GSEA).
enrichment.FDR.cutoff	FDR cut-off for enriched terms, p-values are corrected by Benjamini-Hochberg procedure
background.genes.size	number of background genes for hyper-geometric p-value calculations. Default is 20,000.
verbose	prints messages through running the pipeline

Value

list, enrichment analyses results along with corresponding differential analyses outcomes

Examples

library(cinaR)
data(atac_seq_consensus_bm) # calls 'bed'

# a vector for comparing the examples
contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = TRUE),
                    function(x){x[1]})[4:25]

results <- cinaR(bed, contrasts, reference.genome = "mm10", run.enrichment = FALSE)

results_with_enrichment <- run_enrichment(results, reference.genome = "mm10")

cinaR documentation built on May 18, 2022, 5:12 p.m.