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use bio::stats::LogProb;
use model::sample::Observation;
pub type AlleleFreq = f64;
#[derive(Clone, Copy, Debug)]
pub struct LatentVariableModel {
purity: Option<LogProb>
}
impl LatentVariableModel {
pub fn new(purity: f64) -> Self {
assert!(purity > 0.0 && purity <= 1.0);
LatentVariableModel { purity: Some(LogProb(purity.ln())) }
}
pub fn with_single_sample() -> Self {
LatentVariableModel { purity: None }
}
fn impurity(&self) -> LogProb {
self.purity.unwrap().ln_one_minus_exp()
}
fn likelihood_observation(&self,
observation: &Observation,
allele_freq_case: LogProb,
allele_freq_control: Option<LogProb>) -> LogProb {
match (allele_freq_control, self.purity) {
(Some(allele_freq_control), Some(purity)) => {
let prob_control = self.impurity() +
(allele_freq_control + observation.prob_alt).ln_add_exp(
allele_freq_control.ln_one_minus_exp() + observation.prob_ref
);
let prob_case = purity +
(allele_freq_case + observation.prob_alt).ln_add_exp(
allele_freq_case.ln_one_minus_exp() + observation.prob_ref
);
let total = (observation.prob_mapping + prob_control.ln_add_exp(prob_case)).ln_add_exp(
observation.prob_mapping.ln_one_minus_exp() + observation.prob_mismapped
);
total
},
(None, purity) => {
if let Some(purity) = purity {
assert!(purity == LogProb::ln_one(), "no control allele frequency given but purity is not 1.0");
}
let prob_case = (allele_freq_case + observation.prob_alt).ln_add_exp(
allele_freq_case.ln_one_minus_exp() + observation.prob_ref
);
let total = (observation.prob_mapping + prob_case).ln_add_exp(
observation.prob_mapping.ln_one_minus_exp() + observation.prob_mismapped
);
total
}
(Some(_), None) => {
panic!("control allele frequency given but purity no purity defined")
}
}
}
#[cfg_attr(feature="flame_it", flame)]
pub fn likelihood_pileup(&self,
pileup: &[Observation],
allele_freq_case: f64,
allele_freq_control: Option<f64>) -> LogProb {
let allele_freq_case = LogProb(allele_freq_case.ln());
let allele_freq_control = allele_freq_control.map(|af| LogProb(af.ln()));
let likelihood = pileup.iter().fold(LogProb::ln_one(),
|prob, obs| prob + self.likelihood_observation(obs, allele_freq_case, allele_freq_control));
likelihood
}
}
#[cfg(test)]
mod tests {
use super::*;
use model::sample::{Observation, Evidence};
use itertools_num::linspace;
use bio::stats::LogProb;
#[test]
fn test_likelihood_observation() {
let model = LatentVariableModel::new(1.0);
let observation = Observation{
prob_mapping: LogProb::ln_one(),
prob_alt: LogProb::ln_one(),
prob_ref: LogProb::ln_zero(),
prob_mismapped: LogProb::ln_one(),
evidence: Evidence::dummy_alignment()
};
let lh = model.likelihood_observation(&observation, LogProb::ln_one(), Some(LogProb::ln_zero()));
assert_relative_eq!(*lh, *LogProb::ln_one());
let lh = model.likelihood_observation(&observation, LogProb::ln_zero(), Some(LogProb::ln_zero()));
assert_relative_eq!(*lh, *LogProb::ln_zero());
let lh = model.likelihood_observation(&observation, LogProb(0.5f64.ln()), Some(LogProb::ln_zero()));
assert_relative_eq!(*lh, 0.5f64.ln());
let lh = model.likelihood_observation(&observation, LogProb(0.5f64.ln()), Some(LogProb(0.5f64.ln())));
assert_relative_eq!(*lh, 0.5f64.ln());
let lh = model.likelihood_observation(&observation, LogProb(0.1f64.ln()), Some(LogProb::ln_zero()));
assert_relative_eq!(*lh, 0.1f64.ln());
let model = LatentVariableModel::new(0.5);
let lh = model.likelihood_observation(&observation, LogProb::ln_zero(), Some(LogProb::ln_one()));
assert_relative_eq!(*lh, 0.5f64.ln());
}
#[test]
fn test_likelihood_pileup() {
let model = LatentVariableModel::new(1.0);
let mut observations = Vec::new();
for _ in 0..5 {
observations.push(Observation{
prob_mapping: LogProb::ln_one(),
prob_alt: LogProb::ln_one(),
prob_ref: LogProb::ln_zero(),
prob_mismapped: LogProb::ln_one(),
evidence: Evidence::dummy_alignment()
});
}
for _ in 0..5 {
observations.push(Observation{
prob_mapping: LogProb::ln_one(),
prob_alt: LogProb::ln_zero(),
prob_ref: LogProb::ln_one(),
prob_mismapped: LogProb::ln_one(),
evidence: Evidence::dummy_alignment()
});
}
let lh = model.likelihood_pileup(&observations, 0.5, Some(0.0));
for af in linspace(0.0, 1.0, 10) {
if af != 0.5 {
let l = model.likelihood_pileup(&observations, af, Some(0.0));
assert!(lh > l);
}
}
}
}