reactomepa.enrich: Reactome pathway analysis

In christensensm/COMPOSE: Calculation and Visualization of Phenotypic CRISPR Screens

Description

reactome.enrich utilizes the package 'ReactomePA' to test for enrichment of reactome pathways in your gene list

Usage

reactomepa.enrich(
  CSS,
  genome.db = org.Hs.eg.db,
  pvalue = 0.05,
  combine = FALSE,
  combine.by = NULL,
  metric = "cutoff",
  cutoff = 1,
  save = FALSE,
  save.table = FALSE,
  showCategory = 15,
  font.size = 8,
  cex_label_category = 1,
  cex_label_gene = 1
)

Arguments

CSS	output CRISPR screen score matrix from create.CSS()
genome.db	genome database from which to convert gene symbols to entrez ids
pvalue	specified cutoff for pathway significance (default = 0.05)
combine	logical: do you want to combine the columns in the data? (default=FALSE)
combine.by	vector separating the columns of your data into groups for comparing multiple groups
metric	defines whether to look for enrichment based on a gene list of differentially expressed genes defined by a 'cutoff' (default) or by 'gsea' (one sample or fully combined samples only)
cutoff	numeric cutoff to define differential expression (default = 1)
save	logical: saves plots to pdf
save.table	logical: saves tables of enriched pathways
showCategory	number of categories to display
font.size	font size of labels in dotplot and barplot

Value

list of (1) output from ReactomePA enrichment tests and (2) readable output

See Also

ReactomePA, clusterProfiler

Examples

reactome.enrich <- reactomepa.enrich(CSS.data[,1, drop=FALSE])
reactome.enrich <- reactomepa.enrich(CSS.data[,c(1:5)])
reactome.enrich <- reactomepa.enrich(CSS.data[,c(1:5)], combine = T)
reactome.enrich <- reactomepa.enrich(CSS.data[,1, drop = FALSE], metric = 'gsea')
...

christensensm/COMPOSE documentation built on Dec. 22, 2020, 3:43 a.m.