MRWR: A global propagation algorithm, random walk with restart...

In ssMutPA: Single-Sample Mutation-Based Pathway Analysis

A global propagation algorithm, random walk with restart (RWR), to predict probable influence of nodes in the network by seed nodes.

Description

The function 'MRWR' is used to predict probable influence of nodes in the network by seed nodes.

Usage

MRWR(
  net_AdjMatrNorm,
  Seeds,
  net_data,
  mut_gene,
  r = 0.7,
  BC_Num = length(V(net_data)$name),
  cut_point = 0
)

Arguments

net_AdjMatrNorm	Row normalized network adjacency matrix.
Seeds	A vector containing the gene symbols of the seed nodes.
net_data	A list of the PPI network information,including nodes and edges .
mut_gene	A vector containing the gene symbols of the mutated genes in a sample.
r	A numeric value between 0 and 1. r is a certain probability of continuing the random walk or restarting from the restart set. Default to 0.7.
BC_Num	Number of background genes required to calculate seed node weight.
cut_point	The threshold of indicator function .

Value

An matrix of global weight, where the row names are genes in the network and the column names are samples.

Examples

#load the data
net_path <- system.file("extdata","ppi_network.Rdata",package = "ssMutPA")
load(net_path)
net_AdjMatr<-as.matrix(igraph::get.adjacency(ppi_network))
net_AdjMatrNorm <- t(t(net_AdjMatr)/(Matrix::colSums(net_AdjMatr, na.rm = FALSE, dims = 1)))
data(mut_status)
mut_gene<-intersect(names(mut_status[,1])[which(mut_status[,1]!=0)],igraph::V(ppi_network)$name)
seed<-intersect(names(mut_status[,1])[which(mut_status[,1]!=0)],igraph::V(ppi_network)$name)
#perform the function `MRWR`.
RWR_res<-MRWR(net_AdjMatrNorm,Seeds=seed,net_data=ppi_network,mut_gene,BC_Num = 12436)

ssMutPA documentation built on Oct. 16, 2024, 1:06 a.m.