Flexible Genotyping for Polyploids
The Beta-Binomial Distribution
Filter SNPs based on the output of multidog().
Flexible genotyping for polyploids from next-generation sequencing dat...
Flexible genotyping for polyploids from next-generation sequencing dat...
Return arrayicized elements from the output of multidog.
Return the probabilities of an offspring's genotype given its parental...
Tests if an argument is a flexdog object.
Tests if an argument is a multidog object.
Log-sum-exponential trick using just two doubles.
Log-sum-exponential trick.
Fit flexdog to multiple SNPs.
Calculate the correlation of the oracle estimator with the true genoty...
Calculates the correlation between the true genotype and an oracle est...
The joint probability of the genotype and the genotype estimate of an ...
Get the oracle misclassification error rate directly from the joint di...
Get the oracle misclassification error rates (conditional on true geno...
Returns the oracle misclassification rates for each genotype.
Calculate oracle misclassification error rate.
Construct an oracle plot from the output of oracle_joint.
Make a genotype plot.
Draw a genotype plot from the output of flexdog.
Plot the output of multidog.
Simulate GBS data from the flexdog likelihood.
Simulate individual genotypes from one of the supported flexdog mode...
updog: Flexible Genotyping for Polyploids
EM algorithm to fit weighted ash objective.
Implements empirical Bayes approaches to genotype polyploids from next generation sequencing data while accounting for allele bias, overdispersion, and sequencing error. The main functions are flexdog() and multidog(), which allow the specification of many different genotype distributions. Also provided are functions to simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as functions to calculate oracle genotyping error rates, oracle_mis(), and correlation with the true genotypes, oracle_cor(). These latter two functions are useful for read depth calculations. Run browseVignettes(package = "updog") in R for example usage. See Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details on the implemented methods.