SIGNET

Code for "Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks"

Requirements:

1. R package "Matrix" (https://cran.r-project.org/web/packages/Matrix/) This package is used to support sparse matrix, which is used when loading gene networks (highly sparse).

2. R package "Rcpp" (https://cran.r-project.org/web/packages/Rcpp/index.html) This package is used to support efficient implementation of Gibbs sampling

3. R package "BayesLogit" (https://cran.r-project.org/web/packages/BayesLogit/index.html) This package provides an efficient sampler for Polya-Gamma random variable

Usage :

1. Compute gene-level p-values with PASCAL (http://www2.unil.ch/cbg/index.php?title=Pascal). The precomputed PASCAL files for 14 phenotypes can be found in the "example" folder.

2. Download tissue-specific gene networks (or other alternative context-specific gene networks), e.g. regulatorycircuits (http://regulatorycircuits.org/) and put these networks into one folder. Notice that only unzipped network files are allowed to put into this folder.

3. Use the function "SIGNET" from SIGNET.R to perform inference.

   command : result <- SIGNET(p_value_file, network_dir, iters = 20000, remove_HLA=TRUE, edge_threshold=0)
   parameters :
   p_value_file : gene-level p-values file generated in step (1)
   network_dir : the folder path for gene networks obtained in step (2)
   iters : the number of iterations for performing MCMC sampling
   remove_HLA : whether or not to remove genes located at HLA region
   edge_threshold : the threshold for remove noisy edges in gene networks

   output : a list of result with components as
   result$network.name : the names for gene networks, obtained as file names for these networks
   result$data : a dataframe , each row is a gene and three columns denote gene names ('Gene'), gene-level p-values ('pval'), gene-level local FDR ('localFDR')
   result$para.list : a dataframe containing parameters of each MCMC sampling step
   result$network.included.prob : the posterior includsion probabilities for these gene networks

If you have any questions, please contact me (Mengmeng Wu, wmm15@mails.tsinghua.edu.cn).