call_genotypes: Genotype calling

In hhhh5/ewastools: EWAS Tools

Genotype calling

Description

Detect SNP probes which do not fit into on of the three categories (AA,AB,BB). A mixture model (3 Beta distributions, 1 uniform distribution for outliers) is fitted to all SNP probes. After learning the model parameters via EM algorithm, the probability of being an outlier is computed for each SNP.

Usage

call_genotypes(snpmatrix, learn = FALSE, maxiter = 50)

mxm_(genotypes)

snp_outliers(genotypes)

eBeta(x, w)

Arguments

snpmatrix	Matrix of beta-values for SNP probes. Provide SNPs probes as rows and samples as columns.
maxiter	Maximal number of iterations of the Expectation-Maximization algorithm learning the mixture model
genotypes	Output of call_genotypes

Value

For call_genotypes, a list containing

par	Parameters of the mixture model
loglik	Log-likelihood in each iteration of the EM algorithm
outliers	A-posteriori probability of SNP being an outlier
gamma	A-posteriori probabilities for each of the three genotypes

For snp_outliers, a metric assessing the outlierness of the SNP beta-values. High values may indicate either contaminated or failed samples.

For mxm_, a histogram showing the distribution of beta-values for SNP probes with the density function of the mixture model overlaid.

Author(s)

Jonathan A. Heiss

hhhh5/ewastools documentation built on Feb. 7, 2024, 6:21 p.m.