fuzzy_term_clustering: Heuristic Fuzzy Multiple-linkage Partitioning of Enriched...

In pathfindR: Enrichment Analysis Utilizing Active Subnetworks

Heuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms

Description

Heuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms

Usage

fuzzy_term_clustering(
  kappa_mat,
  enrichment_res,
  kappa_threshold = 0.35,
  use_description = FALSE
)

Arguments

kappa_mat	matrix of kappa statistics (output of create_kappa_matrix)
enrichment_res	data frame of pathfindR enrichment results. Must-have columns are 'Term_Description' (if use_description = TRUE) or 'ID' (if use_description = FALSE), 'Down_regulated', and 'Up_regulated'. If use_active_snw_genes = TRUE, 'non_Signif_Snw_Genes' must also be provided.
kappa_threshold	threshold for kappa statistics, defining strong relation (default = 0.35)
use_description	Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default = FALSE)

Details

The fuzzy clustering algorithm was implemented based on: Huang DW, Sherman BT, Tan Q, et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007;8(9):R183.

Value

a boolean matrix of cluster assignments. Each row corresponds to an enriched term, each column corresponds to a cluster.

Examples

## Not run: 
fuzzy_term_clustering(kappa_mat, enrichment_res)
fuzzy_term_clustering(kappa_mat, enrichment_res, kappa_threshold = 0.45)

## End(Not run)

pathfindR documentation built on Oct. 9, 2023, 1:07 a.m.