oracle_mis: Calculate oracle misclassification error rate.

In dcgerard/mupdog: Flexible Genotyping for Polyploids

Calculate oracle misclassification error rate.

Description

Given perfect knowledge of the data generating parameters, oracle_mis calculates the misclassification error rate, where the error rate is taken over both the data generation process and the allele-distribution. This is an ideal level of the misclassification error rate and any real method will have a larger rate than this. This is a useful approximation when you have a lot of individuals.

Usage

oracle_mis(n, ploidy, seq, bias, od, dist)

Arguments

n	The read-depth.
ploidy	The ploidy of the individual.
seq	The sequencing error rate.
bias	The allele-bias.
od	The overdispersion parameter.
dist	The distribution of the alleles.

Details

To come up with dist, you need some additional assumptions. For example, if the population is in Hardy-Weinberg equilibrium and the allele frequency is alpha then you could calculate dist using the R code: dbinom(x = 0:ploidy, size = ploidy, prob = alpha). Alternatively, if you know the genotypes of the individual's two parents are, say, ref_count1 and ref_count2, then you could use the get_q_array function from the updog package: get_q_array(ploidy)[ref_count1 + 1, ref_count2 + 1, ].

Value

A double. The oracle misclassification error rate.

Author(s)

David Gerard

References

• Gerard, D., Ferrão, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. Genetics, 210(3), 789-807. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1534/genetics.118.301468")}.

Examples

## Hardy-Weinberg population with allele-frequency of 0.75.
## Moderate bias and moderate overdispersion.
## See how oracle misclassification error rates change as we
## increase the ploidy.
ploidy <- 2
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
oracle_mis(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

ploidy <- 4
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
oracle_mis(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

ploidy <- 6
dist <- stats::dbinom(0:ploidy, ploidy, 0.75)
oracle_mis(n = 100, ploidy = ploidy, seq = 0.001,
           bias = 0.7, od = 0.01, dist = dist)

dcgerard/mupdog documentation built on Dec. 12, 2023, 4 a.m.