get_hub_kleinberg: Determine hub genes based on Kleinberg's score

In GWENA: Pipeline for augmented co-expression analysis

Description

Compute Kleinberg's score (defined as the principal eigenvector of A*t(A), where A is the similarity matrix of the graph) of each gene by module if provided or for whole network if not, and return the top_n highest ones.

Usage

get_hub_kleinberg(network, modules = NULL, top_n = 5, k_th = NULL)

Arguments

network	matrix or data.frame, square table representing connectivity between each genes as returned by build_net. Can be whole network or a single module.
modules	list, modules defined as list of gene vectors. If null, network is supposed to be the whole network or an already split module
top_n	integer, number genes to be considered as hub genes
k_th	decimal, Kleinberg's score threshold above or equal to which genes are considered as hubs

Details

If you provide a top_n value, you can't provide a k_th value and vice versa. If none of them is provided, top_n = 5. For more information on Kleinberg's score, look at hub_score from igraph.

Value

A list of vectors, or single vector of gene names

Examples

mat <- matrix(runif(40*40), 40)
colnames(mat) <- paste0("gene_", seq_len(ncol(mat)))
rownames(mat) <- paste0("gene_", seq_len(nrow(mat)))
get_hub_degree(mat)
get_hub_kleinberg(mat, top_n = NULL, k_th = 0.9)

GWENA documentation built on Feb. 17, 2021, 2:01 a.m.