MultiScale.dp.binom: Fit Two-Class Model with Gene-Level Probabilities for...

In lockEF/BayesianScreening: Perform Bayesian Screening with Gene Dependence

Description

Estimates posterior probability of a difference between two sample groups for each genomic variable (e.g., SNPs) using multinomial likelihood and the prospective Bayes factor, with gene-dependent prior probabilities of equality.

Usage

MultiScale.DP.binom(n,n0,n1,UniqGene,Gene,Concentration=c(1,1),alpha=1,K=10,NumDraws=1000)

Arguments

n	D X Q matrix, where D is the number of variables (e.g., SNPs) and Q is the number of categories. Each row gives total number of cases in each category for a given variable.
n0	D X Q matrix, where each row gives number of cases in each category for group 0.
n1	D X Q matrix, where each row gives number of cases in each category for group 1.
UniqGene	Vector giving the gene labels.
Gene	Vector giving the gene label for each variable (length must be equal to the number of rows in X).
Concentration	Dirichlet hyperparameter for the multinomial probablities (defaults to uniform)
alpha	Dirichlet process concentration parameter for the gene-level probabilities
K	Stick-breaking threshold for Dirichlet process
NumDraws	Number of MCMC draws for posterior inference.

Value

Returns an object with the following values, averaged over the MCMC iterations:

pG	Vector giving the gene-level prior for association for each gene
posts	Vector giving the posterior probability of association for each variable

Author(s)

Eric F. Lock

References

Lock, E. F. & Dunson, D. B. (2016). Bayesian genome- and epigenome-wide association studies with gene level dependence. Preprint.

Balding, D. J. (2006) A tutorial on statistical methods for population association studies. Nature Reviews Genetics, 7(10), 781–79.

lockEF/BayesianScreening documentation built on May 24, 2020, 11:50 p.m.