Calculates the correlation between the true genotype and an oracle estimator.
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.
oracle_cor(n, ploidy, seq, bias, od, dist)
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. |
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, ].
The Pearson correlation between the true genotype and the oracle estimator.
David Gerard
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.
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.