em_hmm: EM algorithm in combination with a non-parametric algorithm...

In ReAD: Powerful Replicability Analysis of Genome-Wide Association Studies

EM algorithm in combination with a non-parametric algorithm for estimation of the rLIS statistic.

Description

Estimate the rLIS values accounting for the linkage disequilibrium across two genome-wide association studies via the four-state hidden Markov model. Apply a step-up procedure to control the FDR of replicability null.

Usage

em_hmm(pa_in, pb_in, pi0a_in, pi0b_in)

Arguments

pa_in	A numeric vector of p-values from study 1.
pb_in	A numeric vector of p-values from study 2.
pi0a_in	An initial estimate of the null probability in study 1.
pi0b_in	An initial estimate of the null probability in study 2.

Value

rLIS	The estimated rLIS for replicability null.
fdr	The adjusted values based on rLIS for FDR control.
loglik	The log-likelihood value with converged estimates of the unknowns.
pi	An estimate of the stationary probabilities of four states (0,0), (0,1), (1,0), (1,1).
A	An estimate of the 4-by-4 transition matrix.
f1	A non-parametric estimate for the non-null probability density function in study 1.
f2	A non-parametric estimate for the non-null probability density function in study 2.

ReAD documentation built on July 9, 2023, 6:38 p.m.