f.eta.2

a single value: the current estimate of eta

a vetor of the same length as number of positions: the current estimate of mu.

a vetor of the same length as number of samples: the current estimate of delta.

delta

a n*m matrix: the ith row, jth column of the matrix represents the non-reference counts of ith sample at jth position.

a n*m matrix: the ith row, jth column of the matrix represents the depth of ith sample at jth position.

a n*m matrix: the ith row, jth column of the matrix represents the posterior probability of RR of the ith sample at jth position.

a n*m matrix: the ith row, jth column of the matrix represents the posterior probability of RV of the ith sample at jth position.

a n*m matrix: the ith row, jth column of the matrix represents the posterior probability of VV of the jth sample at jth position.

f.eta.2 calculates the first-order derivative of Q function with respect to eta.

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.

f.eta.2: The second-order derivative with respect to eta

Description

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References