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]

• The package can be installed through:
  if (!requireNamespace("devtools", quietly = TRUE))
  install.packages("devtools")
  library("devtools")
  install_github("pfruan/DiSNEP")

• The package DiSNEP depends on: R (>= 3.5.0), SMUT (>= 1.1), Rcpp (>= 0.12.3)

A brief tutorial:

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

• ‘s0’ is a n * n matrix representing a general network, where we randomly selected 1,000 genes from a general gene network STRING [2].
• ‘adjacency’ is a n * n gene-gene similarity matrix derived from a disease omics data.
• ‘signals’ is a n * 2 matrix with gene association signals, where column one has gene names and column two has gene association p-values obtained from paired t-tests comparing TCGA KIRP gene expression levels between tumor samples and adjacent normal samples on the same 1,000 genes in ‘s0’.

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)

• 's0’ is the original n * n general gene network..
• ‘adjacency’ is a n * n gene-gene similarity matrix derived from a disease omics data.
• ‘alpha’ is a regularization parameter representing weights for signal sources where α = 0 means no disease-specific enhancement. The default value is 0.75.
• ‘iter’ is number of iterations. The default value is 10.
• ‘difference’ is a parameter defining convergence when iterations stop. The default value is 1e-6.
• the returned value is an enhanced gene network but asymmetric and without being denoised.

Denoise the enhanced network and make it binary and symmetric.

se_post=post_process(se,percent=0.9)

• ‘se’ is the enhanced disease specific network but asymmetric and without being denoised. It is the returned value of diffus_matrix function.
• ‘percent’ is what percentage of edges to be considered as noise. The default value is 0.9.
• the returned value is a denoised, binary and symmetric gene network.

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)

• ‘signals’ is a n * 2 matrix with gene association signals, where column one has gene names and column two has association signals.
• 'se_post' is the denoised, binary and symmetric disease-specific network. It is the returned value of post_process function..
• ‘type’ indicates the type of input association signals, "pvalue" or "zscore".
• ‘beta’ is a regularization parameter representing weights for signal sources where beta = 0 means no prioritization. The default value is 0.75.
• ‘iter’ is number of iterations. The default value is 10.
• ‘difference’ is a parameter defining convergence when iterations stop. The default value is 1e-6.
• ‘top’ is a parameter indicating number of top ranked genes selected. The default value is 100.
• the returned value is selected prioritized genes.

Reference

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.