P_graph: Build Probability-Based Network

In pageRank: Temporal and Multiplex PageRank for Gene Regulatory Network Analysis

Description

Build probability-based regulator-target interaction network.

Usage

P_graph(
  expmat,
  net,
  sep = 5,
  method = c("difference", "mi"),
  null = NULL,
  threshold = 0.001
)

Arguments

expmat	(matrix) Gene expression matrix.
net	(data.frame) Network, with "reg" and "target" in column name.
sep	(numeric) Number of bins for calculating marginal/joint probability.
method	(character) Method for calculating probability-based distance, either PXY-PXPY ("difference") or mutual information ("mi").
null	(ecdf) Null distribution of probability-based distance. Either from random interactions by P_null function, or all interactions in net.
threshold	(numeric) P-value threshold for filtering interactions in net.

Value

(igraph) Network graph with "pvalue" and "direction", and "pagerank" as edge/vertex attributes.

Author(s)

DING, HONGXU ([e-mail])

Examples

library(bcellViper)
data(bcellViper)
dset <- exprs(dset)
net <- do.call(rbind, lapply(1:10, function(i, regulon){
  data.frame(reg=rep(names(regulon)[i], 10),
             target=names(regulon[[i]][[1]])[1:10],
             direction=rep(1, 10),
             stringsAsFactors = FALSE)}, regulon=regulon))
P_graph(dset, net, method="difference", null=NULL, threshold=0.05)

pageRank documentation built on Nov. 8, 2020, 6:52 p.m.