generate_feature: Feature Generation for Contamination Detection Model

In sssc: Same Species Sample Contamination Detection

Description

Generates features from each pair of input VCF objects for training contamination detection model.

Usage

generate_feature(file, hom_p = 0.999, het_p = 0.5, hom_rho = 0.005,
  het_rho = 0.1, mixture, homcut = 0.99, highcut = 0.7, hetcut = 0.3)

Arguments

file	VCF input object
hom_p	The initial value for p in Homozygous Beta-Binomial model, default is 0.999
het_p	The initial value for p in Heterozygous Beta-Binomial model, default is 0.5
hom_rho	The initial value for rho in Homozygous Beta-Binomial model, default is 0.005
het_rho	The initial value for rho in Heterozygous Beta-Binomial model, default is 0.1
mixture	A vector of whether the sample is contaminated: 0 for pure; 1 for contaminated
homcut	Cutoff allele frequency value between hom and high, default is 0.99
highcut	Cutoff allele frequency value between high and het, default is 0.7
hetcut	Cutoff allele frequency value between het and low, default is 0.3

Value

A data frame with all features for training model of contamination detection

sssc documentation built on May 2, 2019, 4:03 p.m.