Genotype calling
call_genotypes.Rd
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)
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.