EliminateCpxRedundance: Hierarchical Clustering of Modules

View source: R/EliminateCpxRedundance.R

EliminateCpxRedundanceR Documentation

Hierarchical Clustering of Modules

Description

This function reduces redundancy in the reference complexes by first computing the overlap of two complexes via Jaccard index, followed by merging overlapping complexes with user-defined threshold (here is 0.2).

Usage

EliminateCpxRedundance(
  rawCpx,
  custom_bg = NULL,
  sim_method = "euclidean",
  linkage = "average",
  h = 0.2
)

Arguments

rawCpx

A list containing protein complexes

custom_bg

Vector of proteins names to use as a background. If given, refcpx will be first mapped to the background proteisn, followed by removing redundancy in the refcpx.

sim_method

c(euclidean", "maximum", "manhattan", "canberra", "binary", or "minkowski"); Default is euclidean

linkage

c("average", "ward", "single", "complete", "mcquitty", "median", "centroid"); Default is average.

h

numeric scalar or vector with heights where the tree should be cut; Defaults to 0.2

Details

EliminateCpxRedundance

Value

List of unique complexes.

Author(s)

Matineh Rahmatbakhsh

Examples

# predicted interactions
pred_ppi <- read.table(
system.file("extdata/ppi_input_ClusterONE.txt", package = "MACP"),
header = FALSE)
# get all the proteins in the predicted network
custom_bg <- union(pred_ppi$V1, pred_ppi$V2)
# reference complexes
data("refcpx")
# reduce redundancy in reference complexes
filt_cpx <- EliminateCpxRedundance(refcpx,
custom_bg,
sim_method = "euclidean",
linkage="average",
h = 0.2)

MACP documentation built on March 7, 2023, 7:42 p.m.