pageRank: Rank meta-genes with the PageRank algorithm

In qwang-big/irene: R interface to interactive epigenetic analysis

Description

Rank meta-genes according to the scores from the promoter and targeting enhancers

Usage

pageRank(pcs, x, damping = 0.85, dWeight = 1e-99, fun = NULL,
    maxTry = 10, gene.id = TRUE, rewire = FALSE, statLog = NULL)

Arguments

pcs	data frame containing the ID of genomic regions, and a series of PCs for ranking.
pe	data frame representing the likelihood of enhancer-promoter interactions. The first column is the enhancer Id, and the second column is the promoter Id, and the third is the likelihood of enhancer-promoter interaction.
damping	the damping factor in the PageRank algorithm.
gene.id	logical, only output gene names if true, otherwise both gene names and their PageRank scores will be output.

Details

This function ranks meta-genes based on the 'personalized' PageRank algorithm in the 'igraph' implementation. The node weights are linearly transformed from the computed PCs, and edge weights are from the likelihood of enhancer-promoter interactions. The enhancer-promoter network are built from a bipartite directed graph, wherein the directions are from the enhancer nodes to their target promoter nodes.

Value

A sorted vector representing the PageRank order of meta-gene names, which highly ranked genes will appear on top.

Author(s)

Qi Wang

Examples

data(CLL)
data(sample)
j <- c(1,2,6,8)
res <- dPCA(meta, bed, data, datasets=j, transform=j, normlen=j, processedData=TRUE, verbose=TRUE)
pg <- pageRank(res$gr, h1)

qwang-big/irene documentation built on May 23, 2019, 1:47 p.m.