# diffVariantError: differentiate a variant from sequencing error In hiPOD: hierarchical Pooled Optimal Design

## Description

This function finds a practical threshold to differentiate a variant call from sequencing error.

## Usage

 `1` ```diffVariantError(Xmean, N.p, error, N.test = 1) ```

## Arguments

 `Xmean` The average coverage on the pool `N.p` The pool size: number of individuals per pool `error` Sequencing error rate `N.test` Number of tests, usually the same as number of pools P

## Details

It is a helper function to calculate the probability of detection.

## Value

diffVariantError() returns a vector c(v, p0, p1), where v is the threshold for a variant call, p0 is the false discovery rate and p1 is the lower bound of true discovery rate.

Wei E. Liang

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function(Xmean, N.p, error, N.test=1) { theta <- (1-error)/(2*N.p) + error*(1-1/(2*N.p)) for(v in 1:Xmean) { p1 <- pbinom(v-1, Xmean, theta, lower.tail=FALSE) p0 <- pbinom(v-1, Xmean, error, lower.tail=FALSE) if(p1/p0 > 5 & p0<0.05/N.test) break; } c(v, p0, p1) } ```

hiPOD documentation built on May 29, 2017, 9:10 a.m.