Candidate Gene Prioritization for Non-Communicable Diseases Based on Functional Information.
prioGene: Prioritize candidate genes for complex non-communicable diseases
:writing_hand: Authors
Erqiang Hu
College of Bioinformatics Science and Technology, Harbin Medical University
:arrow_double_down: Installation
Get the development version from github:
if(!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("huerqiang/prioGene")
Or the released version from CRAN:
install.packages("prioGene")
Common operations on prioGene
library(prioGene)
1. Construction of disease related networks
The function deal_net could get a disease-related network by retaining disease-causing genes and their One-step interaction neighbors in a human biological network. The parameter net means a human biological network, a matrix of two columns. The parameter dise_gene means a one-column-matrix of gene symbols obtained from the OMIM database or other disease-related databases. They need to be provided by the users. We provide examples separately in the package: prioGene::net and prioGene::dise_gene.
net_disease <- deal_net(net,dise_gene)
2. Calculation of network weights
These five functions form a pipeline to weight the nodes and edges of the network based on functional information. GO function annotation information comes from org.Hs.eg.db.
genes_mat <- get_gene_mat(net_disease)
terms_mat <- get_term_mat(net_disease)
net_disease_term <- get_net_disease_term(genes_mat,net_disease)
node_weight <- get_node_weight(genes_mat)
edge_weight <- get_edge_weight(net_disease_term,terms_mat)
3. Prioritization of candidate genes
The prioritization of candidate genes was performed based on disease risk scores of each gene obtained from an iteration process considering disease risks transferred between genes.
R_0<- get_R_0(dise_gene,node_weight,f=1)
result <- get_R(node_weight, net_disease_term, bet = 0.5, R_0 = R_0, threshold = 10^(-9))