gmfp: Genomic Mediation analysis with Fixed Permutation scheme

In QidiPeng/eQTLMAPT: eQTL Mediaiton Analysis with accelerated Permutation Testing approaches

Genomic Mediation analysis with Fixed Permutation scheme

Description

The gmfp function performs genomic mediation analysis with fixed permutation. It tests for mediation effects for a set of user specified mediation trios(e.g., eQTL, cis- and trans-genes) in the genome with the assumption of the presence of cis-association.

It returns the mediation p-values(nominal and empirical), the coefficient of linear models(e.g, t_stat, std.error, beta, beta.total) and the proportions mediated(e.g., the percentage of reduction in trans-effects after accounting for cis-mediation).

Usage

gmfp(snp.dat, fea.dat, conf, trios.idx, cl = NULL, nperm = 10000)

Arguments

snp.dat	The eQTL genotype matrix. Each row is an eQTL, each column is a sample.
fea.dat	A feature profile matrix. Each row is for one feature, each column is a sample.
conf	A confounders matrix which is adjusted in mediation tests. Each row is a confounder, each column is a sample.
trios.idx	A matrix of selected trios indexes (row numbers) for mediation tests. Each row consists of the index (i.e., row number) of the eQTL in snp.dat, the index of cis-gene feature in fea.dat, and the index of trans-gene feature in fea.dat. The dimension is the number of trios by three.
cl	Parallel backend if it is set up. It is used for parallel computing. We set cl=NULL as default.
nperm	The number of permutations for testing mediation. If nperm=0, only the nominal P-value is calculated. We set nperm=10000 as default.

Details

The function performs genomic mediation analysis with fixed permutation. Fixed Permutation schemeWhen calculating the empirical P-value, the data is permutated by a fixed number of times, and the statistics after permutation are separately calculated. Assuming that the number of permutation is N, where the number of permutation statistics that is better than the original statistic is M, then the Empirical P-value = (M + 1) / (N + 1).

Value

The algorithm will return a list of empirical.p, nominal.p, beta, std.error, t_stat, beta.total, beta.change.

empirical.p	The mediation empirical P-values with nperm times permutation. A matrix with dimension of the number of trios.
nominal.p	The mediation nominal P-values. A matrix with dimension of the number of trios.
std.error	The return std.error value of feature1 for fit liner models. A matrix with dimension of the number of trios.
t_stat	The return t_stat value of feature1 for fit liner models. A matrix with dimension of the number of trios.
beta	The return beta value of feature2 for fit liner models in the case of feature1. A matrix with dimension of the number of trios.
beta.total	The return beta value of feature2 for fit liner models without considering feature1. A matrix with dimension of the number of trios.
beta.change	The proportions mediated. A matrix with dimension of the number of trios.

Examples

output <- gmfp(conf = dat$known.conf, fea.dat = dat$fea.dat, snp.dat = dat$snp.dat,
               trios.idx = dat$trios.idx[1:10,], nperm = 100)

## Not run: 
  ## generate a cluster with 2 nodes for parallel computing
  cl <- makeCluster(2)
  output <- gmfp(conf = dat$known.conf, fea.dat = dat$fea.dat, snp.dat = dat$snp.dat,
                 trios.idx = dat$trios.idx[1:10,], cl = cl, nperm = 100)
  stopCluster(cl)

## End(Not run)

QidiPeng/eQTLMAPT documentation built on Jan. 25, 2023, 11:03 p.m.