updog (version 2.1.5)

oracle_mis: 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)

Value

A double. The oracle misclassification error rate.

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.

Author

David Gerard

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, ].

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. 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)