// var poissonDist = new MathNet.Numerics.Distributions.Poisson(errorRate * coverage);
public static double AssignRawPoissonQScore(int callCount, int coverage, int estimatedBaseCallQuality)
// ReSharper restore InconsistentNaming
{
double errorRate = MathOperations.QtoP(estimatedBaseCallQuality);
double callCountMinusOne = callCount - 1;
double callCountDouble = callCount;
var lambda = errorRate * coverage;
var poissonDist = new MathNet.Numerics.Distributions.Poisson(lambda);
var pValue = 1 - poissonDist.CumulativeDistribution(callCountMinusOne);
if (pValue > 0)
{
return(MathOperations.PtoQ(pValue));
}
else
{
//Approximation to get around precision issues.
double A = poissonDist.ProbabilityLn((int)callCountMinusOne);
double correction = (callCountDouble - lambda) / callCountDouble;
var qScore = -10.0 * (A - Math.Log(2.0 * correction)) / Math.Log(10.0);
return(qScore);
}
}
private void SetNextOrderAmount()
{
switch (OptionA)
{
case EnumTypes.BuyerAOptions.Static:
NextOrderAmount = this.Amount;
break;
case EnumTypes.BuyerAOptions.Random:
Random rnd = new Random();
int rand = rnd.Next(Convert.ToInt32(this.MinAmount), Convert.ToInt32(this.MaxAmount) + 1);
NextOrderAmount = rand;
break;
case EnumTypes.BuyerAOptions.Poisson:
MathNet.Numerics.Distributions.Poisson poisson = new MathNet.Numerics.Distributions.Poisson(this.Lambda);
NextOrderAmount = poisson.Sample();
break;
case EnumTypes.BuyerAOptions.Gauss:
MathNet.Numerics.Distributions.Normal normal = new MathNet.Numerics.Distributions.Normal(this.MeanOptionA, this.DeviationOptionA);
NextOrderAmount = normal.Sample();
break;
}
}
// Poisson.Cdf(observedCallCount - 1.0, coverage* errorRate));
public double[] Triangle_AssignQValue(double depth, double noise, double callCounts)
{
var poissonDist = new MathNet.Numerics.Distributions.Poisson(noise * depth);
double rawCDF = poissonDist.CumulativeDistribution(callCounts - 1.0);
double P = 1 - rawCDF;
//Approximation to get around precision issues.
double A = poissonDist.ProbabilityLn((int)callCounts - 1);
double correction = (callCounts - noise * depth) / callCounts;
double Qnew = -10.0 * (A - Math.Log(2.0 * correction)) / Math.Log(10.0);
return(new double[] { P, Qnew });
}
/// <summary>
/// Assign a q-score for a genotype call.
/// </summary>
public static int Compute(CalledAllele allele, int minQScore, int maxQScore)
{
if (allele.TotalCoverage == 0)
{
return(minQScore);
}
Genotype calledGT = allele.Genotype;
//parameters
float noiseHomRef = 0.05f;
float noiseHomAlt = 0.075f;
float expectedHetFreq = 0.40f; //a real 50% typically shows up at <50%, more like 40% or 45%
float depth = (float)allele.TotalCoverage;
//distributions
var poissonHomRefNoise = new MathNet.Numerics.Distributions.Poisson(noiseHomRef * depth);
var poissonHomAltNoise = new MathNet.Numerics.Distributions.Poisson(noiseHomAlt * depth);
var binomialHomAltExpected = new MathNet.Numerics.Distributions.Binomial(expectedHetFreq, allele.TotalCoverage);
var nonAlleleCalls = Math.Max(allele.TotalCoverage - allele.AlleleSupport, 0); //sanitize for funny insertion cases
double LnPofH0GT = 0; //H0 is the null hypothesis. The working assumption that the GT given to the allele is correct
double LnPofH1GT = 0; //H1 is the alternate hypothesis. The possibility that H0 is wrong, and the second-best GT was actually the right one
//the GT Q model measures how much *more* likely H0 is than H1, given the observations.
switch (calledGT)
{
case Genotype.HemizygousRef:
LnPofH0GT = poissonHomRefNoise.ProbabilityLn(nonAlleleCalls);
LnPofH1GT = binomialHomAltExpected.ProbabilityLn(nonAlleleCalls);
break;
case Genotype.HemizygousAlt:
LnPofH0GT = poissonHomAltNoise.ProbabilityLn(nonAlleleCalls);
LnPofH1GT = binomialHomAltExpected.ProbabilityLn(allele.AlleleSupport);
break;
default:
return(minQScore);
}
var qScore = (int)Math.Floor(10.0 * Math.Log10(Math.E) * (LnPofH0GT - LnPofH1GT));
return(Math.Max(Math.Min(qScore, maxQScore), minQScore));
}
public double TakeSamples()
{
var dateTimeElapsed = 0.0;
var dateTime = DateTime.Now;
if (this.DistributionName == "Binomial")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}-Trials:{TrialsNumber}";
var binomaial = new MathNet.Numerics.Distributions.Binomial(0.5, this.TrialsNumber);
var generatedsamples = binomaial.Samples().Take(SamplesNumber).ToArray();
}
else if (this.DistributionName == "Geometric")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var geometric = new MathNet.Numerics.Distributions.Geometric(0.5);
var generatedsamples = geometric.Samples().Take(SamplesNumber).ToArray();
}
else if (this.DistributionName == "Poisson")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var poisson = new MathNet.Numerics.Distributions.Poisson(0.5);
var generatedsamples = poisson.Samples().Take(SamplesNumber).ToArray();
}
else if (this.DistributionName == "Normal")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var normal = new MathNet.Numerics.Distributions.Normal(0.5, 2);
var generatedsamples = normal.Samples().Take(SamplesNumber).ToArray();
}
dateTimeElapsed = (DateTime.Now - dateTime).TotalMilliseconds;
return(dateTimeElapsed);
}
/// <summary>
/// Assign a q-score for a genotoype call.
/// </summary>
public static int Compute(CalledAllele allele, int minQScore, int maxQScore)
{
if (allele.TotalCoverage == 0)
{
return(minQScore);
}
Genotype calledGT = allele.Genotype;
//parameters
float noiseHomRef = 0.05f;
float noiseHomAlt = 0.075f;
float noiseHetAlt = 0.10f;
float expectedHetFreq = 0.40f; //a ref 50% typically shows up at <50%, more like 40% or 45%
float depth = (float)allele.TotalCoverage;
float support = (float)allele.AlleleSupport;
//distributions
var poissonHomRefNoise = new MathNet.Numerics.Distributions.Poisson(noiseHomRef * depth);
var poissonHomAltNoise = new MathNet.Numerics.Distributions.Poisson(noiseHomAlt * depth);
var binomialHomAltExpected = new MathNet.Numerics.Distributions.Binomial(expectedHetFreq, allele.TotalCoverage);
var binomialHomRefNoise = new MathNet.Numerics.Distributions.Binomial(noiseHetAlt, allele.TotalCoverage);
var binomialHomAltNoise = new MathNet.Numerics.Distributions.Binomial((1 - noiseHetAlt), allele.TotalCoverage);
var nonAlleleCalls = Math.Max(allele.TotalCoverage - allele.AlleleSupport, 0); //sanitize for funny insertion cases
double LnPofH0GT = 0; //H0 is the null hypothesis. The working assumption that the GT given to the allele is correct
double LnPofH1GT = 0; //H1 is the alternate hypothesis. The possibility that H0 is wrong, and the second-best GT was actually the right one
//the GT Q model measures how much *more* likely H0 is than H1, given the observations.
switch (calledGT)
{
case Genotype.HomozygousRef:
LnPofH0GT = poissonHomRefNoise.ProbabilityLn(nonAlleleCalls);
LnPofH1GT = binomialHomAltExpected.ProbabilityLn(nonAlleleCalls);
break;
case Genotype.HomozygousAlt:
LnPofH0GT = poissonHomAltNoise.ProbabilityLn(nonAlleleCalls);
LnPofH1GT = binomialHomAltExpected.ProbabilityLn(allele.AlleleSupport);
break;
case Genotype.HeterozygousAlt1Alt2:
case Genotype.HeterozygousAltRef:
if (allele.Frequency >= 0.50)
{
//test alternate GT as being homAlt
LnPofH0GT = binomialHomAltExpected.ProbabilityLn((int)(depth * allele.Frequency));
LnPofH1GT = binomialHomAltNoise.ProbabilityLn((int)(depth * allele.Frequency));
}
else
{ //test alternate GT as being homRef
LnPofH0GT = binomialHomAltExpected.ProbabilityLn((int)(depth * allele.Frequency));
LnPofH1GT = binomialHomRefNoise.ProbabilityLn((int)(depth * allele.Frequency));
}
break;
default:
return(minQScore);
}
//note, Ln(X)=Log10 (X) / Log10 (e).
// ->
//Log10(A)-Log10(B) = Log10 (e) (ln (A) - ln (B)) = Log10(A/B)
/* for debugging..
* var LogPofCalledGT = Math.Log10(Math.E) * (LnPofCalledGT);
* var LogPofAltGT = Math.Log10(Math.E) * (LnPofAltGT);
* Console.WriteLine(LogPofCalledGT);
* Console.WriteLine(LogPofAltGT);
*/
var qScore = (int)Math.Floor(10.0 * Math.Log10(Math.E) * (LnPofH0GT - LnPofH1GT));
if ((LnPofH1GT <= int.MinValue) && (LnPofH0GT > LnPofH1GT)) //H1 infinitely more likely
{
return(maxQScore);
}
if ((LnPofH0GT <= int.MinValue) && (LnPofH0GT < LnPofH1GT)) //H0 infinitely more likely
{
return(minQScore);
}
return(Math.Max(Math.Min(qScore, maxQScore), minQScore));
}
public double TakeSamples()
{
var dateTimeElapsed = 0.0;
var dateTime = DateTime.Now;
//IEnumerable<int> generatedSamplesEnumerable = Enumerable.Empty<int>();
//IEnumerable<double> generatedSamplesDoubleEnumerable = Enumerable.Empty<double>();
if (this.DistributionName == "Binomial")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}-Trials:{TrialsNumber}";
var binomaial = new MathNet.Numerics.Distributions.Binomial(0.5, this.TrialsNumber);
var generatedsamples = binomaial.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesEnumerable = binomaial.Samples().Take(SamplesNumber);
//foreach (var item in generatedSamplesEnumerable)
//{
// var test = item;
//}
}
else if (this.DistributionName == "Geometric")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var geometric = new MathNet.Numerics.Distributions.Geometric(0.5);
var generatedsamples = geometric.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesEnumerable = geometric.Samples().Take(SamplesNumber);
//foreach (var item in generatedSamplesEnumerable)
//{
// var test = item;
//}
}
else if (this.DistributionName == "Poisson")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var poisson = new MathNet.Numerics.Distributions.Poisson(0.5);
var generatedsamples = poisson.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesEnumerable = poisson.Samples().Take(SamplesNumber);
//foreach (var item in generatedSamplesEnumerable)
//{
// var test = item;
//}
}
else if (this.DistributionName == "Normal")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var normal = new MathNet.Numerics.Distributions.Normal(0.5, 2);
var generatedsamples = normal.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesDoubleEnumerable = normal.Samples().Take(SamplesNumber);
//foreach(var item in generatedSamplesDoubleEnumerable)
//{
// var test = item;
//}
}
dateTimeElapsed = (DateTime.Now - dateTime).TotalMilliseconds;
return(dateTimeElapsed);
}
