oracle_cor {updog}

Calculates the correlation between the true genotype and an oracle estimator.

Description

Calculates the correlation between the oracle MAP estimator (where we have perfect knowledge about the data generation process) and the true genotype. This is a useful approximation when you have a lot of individuals.

Usage

oracle_cor(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

The Pearson correlation between the true genotype and the oracle estimator.

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. doi:10.1534/genetics.118.301468.

Examples

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