This package presents an improved analytical tool for prioritizing genes associated with diseases using gene network information. The DiSNEP package implemented the Disease-Specific Network Enhancement Prioritization (DiSNEP) framework. The DiSNEP framework first enhances a comprehensive gene network specifically for a disease through a diffusion process on a gene-gene similarity matrix derived from a disease omics data. The enhanced disease-specific gene network thus better reflects true gene interactions for the disease and may improve prioritizing disease-associated genes subsequently.[1]

A brief tutorial:

library(DiSNEP) data("s0") data("adjacency") data("signals")

Enhance a general network s0 into a disease specific network by diffusion on a similarity matrix generated from a disease omics data.

se=diffus_matrix(s0,adjacency,alpha=0.75,iter=10, difference=1e-6)

Denoise the enhanced network and make it binary and symmetric.


Prioritize the disease association signals by diffusion process on a gene network

res=diffus_vec(signals,se_post,type="pvalue", beta=0.75, iter=10, difference=1e-6, top=100)


  1. Ruan P, Wang S. DiSNEP: a Disease-Specific gene Network Enhancement to improve Prioritizing candidate disease genes. Briefings in Bioinformatics, submitted.
  2. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research 2018;47:D607-D613.

pfruan/DiSNEP documentation built on June 17, 2020, 6:05 a.m.