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.
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, ].
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. 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)
#> [1] 1.262647e-06
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)
#> [1] 0.02944818
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)
#> [1] 0.1329197