/**
* P(somatic | D) = P(somatic) * P(D | somatic) = P(somatic) * P(D | normals are ref) * P(D |
* tumors are non-ref)
*
* <p>P(! somatic | D) = P(! somatic) * P(D | ! somatic) = P(! somatic) * * ( P(D | normals are
* non-ref) * P(D | tumors are non-ref) [germline] + P(D | normals are ref) * P(D | tumors are
* ref)) [no-variant at all]
*
* @param vc
* @return
*/
private double calcLog10pSomatic(final VariantContext vc) {
// walk over tumors
double log10pNonRefInTumors = log10pNonRefInSamples(vc, tumorSample);
double log10pRefInTumors = log10pRefInSamples(vc, tumorSample);
// walk over normals
double log10pNonRefInNormals = log10pNonRefInSamples(vc, normalSample);
double log10pRefInNormals = log10pRefInSamples(vc, normalSample);
// priors
double log10pSomaticPrior = QualityUtils.qualToErrorProbLog10(somaticPriorQ);
double log10pNotSomaticPrior = Math.log10(1 - QualityUtils.qualToErrorProb(somaticPriorQ));
double log10pNotSomaticGermline = log10pNonRefInNormals + log10pNonRefInTumors;
double log10pNotSomaticNoVariant = log10pRefInNormals + log10pRefInTumors;
double log10pNotSomatic =
log10pNotSomaticPrior
+ MathUtils.log10sumLog10(
new double[] {log10pNotSomaticGermline, log10pNotSomaticNoVariant});
double log10pSomatic = log10pSomaticPrior + log10pNonRefInTumors + log10pRefInNormals;
double lod = log10pSomatic - log10pNotSomatic;
return Double.isInfinite(lod) ? -10000 : lod;
}
static {
baseMatchArray = new double[MAX_CACHED_QUAL + 1];
baseMismatchArray = new double[MAX_CACHED_QUAL + 1];
for (int k = 1; k <= MAX_CACHED_QUAL; k++) {
double baseProb = QualityUtils.qualToProb((byte) k);
baseMatchArray[k] = probToQual(baseProb);
baseMismatchArray[k] = (double) (k);
}
}
/**
* Initializes the matrix that holds all the constants related to the editing distance between the
* read and the haplotype.
*
* @param haplotypeBases the bases of the haplotype
* @param readBases the bases of the read
* @param readQuals the base quality scores of the read
* @param startIndex where to start updating the distanceMatrix (in case this read is similar to
* the previous read)
*/
public void initializePriors(
final byte[] haplotypeBases,
final byte[] readBases,
final byte[] readQuals,
final int startIndex) {
// initialize the pBaseReadLog10 matrix for all combinations of read x haplotype bases
// Abusing the fact that java initializes arrays with 0.0, so no need to fill in rows and
// columns below 2.
for (int i = 0; i < readBases.length; i++) {
final byte x = readBases[i];
final byte qual = readQuals[i];
for (int j = startIndex; j < haplotypeBases.length; j++) {
final byte y = haplotypeBases[j];
prior[i + 1][j + 1] =
(x == y || x == (byte) 'N' || y == (byte) 'N'
? QualityUtils.qualToProb(qual)
: (QualityUtils.qualToErrorProb(qual)
/ (doNotUseTristateCorrection ? 1.0 : TRISTATE_CORRECTION)));
}
}
}
@Override
public Map<String, Object> annotate(
final RefMetaDataTracker tracker,
final AnnotatorCompatible walker,
final ReferenceContext ref,
final Map<String, AlignmentContext> stratifiedContexts,
final VariantContext vc,
final Map<String, PerReadAlleleLikelihoodMap> stratifiedPerReadAlleleLikelihoodMap) {
final GenotypesContext genotypes = vc.getGenotypes();
if (genotypes == null || genotypes.size() < MIN_SAMPLES) {
if (!warningLogged) {
logger.warn("Too few genotypes");
warningLogged = true;
}
return null;
}
int refCount = 0;
int hetCount = 0;
int homCount = 0;
for (final Genotype g : genotypes) {
if (g.isNoCall()) continue;
// TODO - fix me:
// Right now we just ignore genotypes that are not confident, but this throws off
// our HW ratios. More analysis is needed to determine the right thing to do when
// the genotyper cannot decide whether a given sample is het or hom var.
if (g.getLog10PError() > MIN_LOG10_PERROR) continue;
if (g.isHomRef()) refCount++;
else if (g.isHet()) hetCount++;
else homCount++;
}
if (refCount + hetCount + homCount == 0) return null;
double pvalue = HardyWeinbergCalculation.hwCalculate(refCount, hetCount, homCount);
// System.out.println(refCount + " " + hetCount + " " + homCount + " " + pvalue);
Map<String, Object> map = new HashMap<>();
map.put(getKeyNames().get(0), String.format("%.1f", QualityUtils.phredScaleErrorRate(pvalue)));
return map;
}
