estep: E step In ebGenotyping: Genotyping and SNP Detection using Next Generation Sequencing Data

Description

This function calculates the E step of ECM algorithm for the model described in 'An Empirical Bayes Method for Genotyping and SNP detection Using Multi-sample Next-generation Sequencing Data'.

Usage

 `1` ```estep(mu, delta, pm1, p0, dat, cvg) ```

Arguments

 `mu` a vetor of the same length as number of positions: the position effect. `delta` a vetor of the same length as number of samples: the sample effect. `pm1` a single value,which is larger than 0 and less than 1: the probability of RR. `p0` a single value,which is larger than 0 and less than 1: the probability of RV. `dat` a n*m matrix: the ith row, jth column of the matrix represents the non-reference counts of ith sample at jth position. `cvg` a n*m matrix: the ith row, jth column of the matrix represents the depth of ith sample at jth position.

Details

The value of mu and delta must satisfy that each element of outer(delta,mu,"+") must less than zero. This is the requirement of the model described in paper "Genotyping for Rare Variant Detection Using Next-generation Sequencing Data."

Value

 `zRR` a n*m matrix: the posterior probabilities of genotype RR for n samples at m positions `zRV` a n*m matrix: the posterior probabilities of genotype RV for n samples at m positions `zVV` a n*m matrix: the posterior probabilities of genotype VV for n samples at m positions

Note

The most important function in this package is "ecm". "estep" is a function called by "ecm" to realize one E step in the whole process of iteration in "ecm".

Author(s)

Na You <youn@mail.sysu.edu.cn> and Gongyi Huang<53hgy@163.com>

References

Na You and Gongyi Huang.(2016) An Empirical Bayes Method for Genotyping and SNP detection Using Multi-sample Next-generation Sequencing Data.

ebGenotyping documentation built on May 2, 2019, 9:28 a.m.