delta.recal

a vetor of the same length as number of positions: the non-reference counts of the jth sample at m positions.

a vetor of the same length as number of positions: the coverage of jth sample at m positions.

a vetor of the same length as number of positions: posterior probability of RR of the jth sample.

a vetor of the same length as number of positions: posterior probability of RV of the jth sample.

a vetor of the same length as number of positions: posterior probability of VV of the jth sample.

a vetor of the same length as number of positions: the estimator of mu(position effects).

a single value representing the estimator of eta.

delta.recal calculates the optimal estimator of certain sample effect when given other parameters.

Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.

delta.recal: One step maximization of delta when given other parameters

Description

Usage

Arguments

Value

References