README.md

In pfruan/DiSNEP: DiSNEP: a Disease-Specific Enhancement for gene Network Enhancement to improve Prioritizing candidate disease genes

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.

pfruan/DiSNEP documentation built on Oct. 12, 2023, 3:29 p.m.