columnSignificance: Determine which terms are found to be significant using each...

In ActivePathways: Integrative Pathway Enrichment Analysis of Multivariate Omics Data

Determine which terms are found to be significant using each column individually.

Description

Determine which terms are found to be significant using each column individually.

Usage

columnSignificance(
  scores,
  gmt,
  background,
  cutoff,
  significant,
  correction_method,
  pvals
)

Arguments

scores	A numerical matrix of p-values where each row is a gene and each column represents an omics dataset (evidence). Rownames correspond to the genes and colnames to the datasets. All values must be 0<=p<=1. We recommend converting missing values to ones.
gmt	A GMT object to be used for enrichment analysis. If a filename, a GMT object will be read from the file.
background	A character vector of gene names to be used as a statistical background. By default, the background is all genes that appear in gmt.
cutoff	A maximum merged p-value for a gene to be used for analysis. Any genes with merged, unadjusted p > significant will be discarded before testing.
significant	Significance cutoff for selecting enriched pathways. Pathways with adjusted_p_val <= significant will be selected as results.
correction_method	Statistical method to correct p-values. See p.adjust for details.
pvals	p-value for the pathways calculated by ActivePathways

Value

a data.table with columns 'term_id' and a column for each column in scores, indicating whether each term (pathway) was found to be significant or not when considering only that column. For each term, either report the list of related genes if that term was significant, or NA if not.

ActivePathways documentation built on Nov. 2, 2023, 5:12 p.m.