Inferring Gene-Disease Association by an Integrative Analysis of eQTL GWAS and Protein-Protein Interaction data
Link for our paper: https://www.ncbi.nlm.nih.gov/pubmed/30669151 #Jun WANG, Peking Univrsity, junwangmath@gmail.com
All the GWAS, eQTL and Protein-Protein Interaction dataset are stored at the "source.file.prepared.for.marginal.4coloc" folder. Unzip it and put it in the same folder of the other R scripts.
The main function is "Main.Function.Compute.Marginal.Posterior.Prob.R", which has 6 parameters.
Rscript Main.Function.Compute.Marginal.Posterior.Prob.R -5 0.8 -0.001 4000 1 1
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alpha.args=as.numeric(args[1]); alpha parameter in paper, alpha = -5 is suggested
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tao1.args=as.numeric(args[2]); beta1 - beta2 parameter in paper, beta1 - beta2 = 0.8 is suggested
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tao_1.args=as.numeric(args[3]); beta2 - beta3 parameter in paper, beta2 - beta3 = -0.001 is suggested
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step.mcmc=as.numeric(args[4]); number of Gibbs Sampling steps, 4000 steps is suggested
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task.id=as.numeric(args[5]); if task.id is set as 1, the program will not permutate the log-likelihood score computed from Sherlock, this will output the original marginal posterior probability. If task.id != 1, program will do permutation, to get the randomized marginal posterior probability. We suggest to do 2000 permutation, i.e. set task.id from 1 to 2000 in each run.
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disease.num=as.numeric(args[6]); which disease you want to run, see "disease.all" below
disease.all=c("AMD_Fritsche_2015_GTExV6_LD85","Barrett_08_GTExV6_LD85","Cholestreol_HDL_ONE_Eur_GTExV6_LD85","Cholestreol_TC_ONE_Eur_GTExV6_LD85","Cronhs.EU_Liu_2015_GTExV6_LD85","Franke_Cronhs_meta_GTExV6_LD85","HDL_Willer_2013_GTExV6_LD85","LDL_Willer_2013_GTExV6_LD85","TotalCholesterol_Willer_2013_GTExV6_LD85")
Our method is based on the Sherlock score (Bayesian Factor) calculated by "http://sherlock.ucsf.edu/index.html". If users would like to analysis their own data using our method, please calculate the Sherlock score first.