public void ValidateProbability(
[Values(new[] { 0.3, 0.7 }, new[] { 0.1, 0.3, 0.6 }, new[] { 0.15, 0.35, 0.3, 0.2 })] double[] p,
[Values(new[] { 1, 9 }, new[] { 1, 3, 6 }, new[] { 1, 1, 1, 7 })] int[] x,
[Values(0.121060821, 0.105815808, 0.000145152)] double res)
{
var b = new Multinomial(p, x.Sum());
AssertHelpers.AlmostEqual(b.Probability(x), res, 12);
}
public void ValidateProbabilityLn(int[] x)
{
var b = new Multinomial(_largeP, x.Sum());
AssertHelpers.AlmostEqualRelative(b.ProbabilityLn(x), Math.Log(b.Probability(x)), 12);
}
public void ValidateProbability(double[] p, int[] x, double res)
{
var b = new Multinomial(p, x.Sum());
AssertHelpers.AlmostEqualRelative(b.Probability(x), res, 12);
}
public void ValidateProbabilityLn(int[] x)
{
var b = new Multinomial(_largeP, x.Sum());
AssertHelpers.AlmostEqualRelative(b.ProbabilityLn(x), Math.Log(b.Probability(x)), 12);
}
public void ValidateProbability(double[] p, int[] x, double res)
{
var b = new Multinomial(p, x.Sum());
AssertHelpers.AlmostEqualRelative(b.Probability(x), res, 12);
}
public void ValidateProbabilityLn([Values(new[] { 1, 2, 3, 4, 5, 6, 7, 8, 9 }, new[] { 1, 1, 1, 2, 2, 2, 3, 3, 3 }, new[] { 5, 6, 7, 8, 7, 6, 5, 4, 3 })] int[] x)
{
var b = new Multinomial(_largeP, x.Sum());
AssertHelpers.AlmostEqual(b.ProbabilityLn(x), Math.Log(b.Probability(x)), 12);
}
/// <summary>
/// Calculates the Q scores and phred-scaled genotype posteriors for a 1/2 locus.
/// This method uses a multinomial distribution to calculate the Q score and posteriors for a 1/2 locus. The probabilities
/// of each class is estimated from the input models.
/// </summary>
/// <param name="ad">
/// A three member int array that specifies the allele depths of the reference, AD1 and AD2, in that order.
/// </param>
/// <param name="dp">
/// Total read depth at the locus.
/// </param>
/// <param name="means">
/// An IList containing two members, each a double array specifying the means of the models of AD1 and AD2, in that order.
/// Each array is three elements long.
/// </param>
/// <returns>
/// A RecalibratedVariant object containing the genotype posteriors and Q scores.
/// </returns>
/// <remarks>
/// The prior probability is specified as: homozygous reference-99%, uniform prior for the other possible genotypes.
/// If total read depth is greater than 500, Q score and posteriors cannot be estimated so the maximal values are returned.
/// </remarks>
public static MixtureModelResult GetMultinomialQScores(int[] ad, int dp, IList <double[]> means)
{
if (dp > 500) // Can't be calculated
{
return new MixtureModelResult()
{
GenotypeCategory = SimplifiedDiploidGenotype.HeterozygousAltRef,
QScore = (int)_maxQScore,
GenotypePosteriors = new float[] { _maxQScore, _maxQScore, _maxQScore, _maxQScore, 0, _maxQScore }
}
}
;
double[] tempPosts = new double[6];
int postCount = 0;
double postNorm = 0;
for (int m2 = 0; m2 < means[1].Length; m2++)
{
for (int m1 = 0; m1 < means[0].Length; m1++)
{
// Exclude when either allele is hom-alt and the other alelle is not hom-ref
if ((m1 == 2 && m2 != 0) ||
(m2 == 2 && m1 != 0))
{
continue;
}
var p = CalculateProbabilities(m1, m2, means);
Multinomial mm = new Multinomial(p, dp);
double prior = 0.01 / 5;
if (m1 == 0 && m2 == 0)
{
prior = 0.99;
}
tempPosts[postCount] = mm.Probability(ad) * prior;
postNorm = postNorm + tempPosts[postCount];
postCount++;
}
}
float[] gp = new float[tempPosts.Length];
for (int i = 0; i < tempPosts.Length; i++)
{
gp[i] = Math.Min(_maxQScore, MathOperations.PToQ_CapAt300(tempPosts[i] / postNorm));
}
int qScore = Math.Min((int)_maxQScore, (int)Math.Round(MathOperations.PToQ_CapAt300(1 - tempPosts[4] / postNorm)));
return(new MixtureModelResult()
{
GenotypeCategory = SimplifiedDiploidGenotype.HeterozygousAltRef,
QScore = qScore,
GenotypePosteriors = gp
});
// Helper method
double[] CalculateProbabilities(int genotype1, int genotype2, IList <double[]> probs)
{
double[] result = new double[probs[0].Length];
result[1] = probs[0][genotype1];
result[2] = probs[1][genotype2];
result[0] = 1 - result[1] - result[2];
// Deal with when p is less than or equal to 0
if (result[0] <= 0)
{
if (genotype1 == 2)
{
result[0] = 1 - result[1];
}
else if (genotype2 == 2)
{
result[0] = 1 - result[2];
}
else if (genotype1 == 1 && genotype2 == 1)
{
result[0] = 1 - probs[0][2];
}
}
return(result);
}
}
public void ValidateProbabilityLn([Values(new[] { 1, 2, 3, 4, 5, 6, 7, 8, 9 }, new[] { 1, 1, 1, 2, 2, 2, 3, 3, 3 }, new[] { 5, 6, 7, 8, 7, 6, 5, 4, 3 })] int[] x)
{
var b = new Multinomial(_largeP, x.Sum());
AssertHelpers.AlmostEqual(b.ProbabilityLn(x), Math.Log(b.Probability(x)), 12);
}
public void ValidateProbability(
[Values(new[] { 0.3, 0.7 }, new[] { 0.1, 0.3, 0.6 }, new[] { 0.15, 0.35, 0.3, 0.2 })] double[] p,
[Values(new[] { 1, 9 }, new[] { 1, 3, 6 }, new[] { 1, 1, 1, 7 })] int[] x,
[Values(0.121060821, 0.105815808, 0.000145152)] double res)
{
var b = new Multinomial(p, x.Sum());
AssertHelpers.AlmostEqual(b.Probability(x), res, 12);
}