public void TestMixtureModelOnChr1()
{
string file = Path.Combine(TestPaths.LocalTestDataDirectory, "Chr1.csv");
SparseArray <int> AD = new SparseArray <int>();
SparseArray <int> DP = new SparseArray <int>();
using (StreamReader sr = new StreamReader(new FileStream(file, FileMode.Open)))
{
var counter = 0;
while (counter < 20000)
{
string line = sr.ReadLine();
string[] arr = line.Split(',');
int dp = int.Parse(arr[arr.Length - 1]);
DP.Add(dp);
if (arr.Length == 2)
{
AD.Add(dp - int.Parse(arr[0]));
}
else
{
AD.Add(int.Parse(arr[arr.Length - 2]));
}
counter++;
}
}
MixtureModel model = MixtureModel.FitMixtureModel(AD, DP);
Assert.Equal(0.000656, model.Means[0], 3);
Assert.Equal(0.366, model.Means[1], 3);
Assert.Equal(0.998, model.Means[2], 3);
}
public void TestMixtureModelOnThreeCoins()
{
string file = Path.Combine(TestPaths.LocalTestDataDirectory, "ThreeCoins.csv");
List <int> k = new List <int>();
List <int> n = new List <int>();
using (StreamReader sr = new StreamReader(new FileStream(file, FileMode.Open)))
{
while (true)
{
string line = sr.ReadLine();
if (line == null)
{
break;
}
string[] arr = line.Split(',');
k.Add(int.Parse(arr[0]));
n.Add(int.Parse(arr[1]));
}
}
MixtureModel model;
model = MixtureModel.FitMixtureModel(k, n,
new double[] { 0.5686903, 0.3308862, 0.4617437 });
Assert.True(Math.Abs(model.Means[0] - 0.2335885) < 0.001);
Assert.True(Math.Abs(model.Means[1] - 0.4100772) < 0.001);
Assert.True(Math.Abs(model.Means[2] - 0.5074295) < 0.001);
}
public void MalformedDataTest()
{
SparseArray <int> alleleDepth = new SparseArray <int>();
SparseArray <int> totalDepth = new SparseArray <int>();
for (int i = 0; i < 10; i++)
{
alleleDepth.Add(0);
totalDepth.Add(10);
}
Assert.Throws <MixtureModelException>(() => MixtureModel.FitMixtureModel(alleleDepth, totalDepth));
}
private static List <MixtureModel> GetNewModels(string vcfIn, string outDir, List <RecalibratedVariantsCollection> variants)
{
var models = new List <MixtureModel>();
// Perform fitting for SNVs
Logger.WriteToLog("Finding thresholds for SNVs.");
MixtureModel snvModel = MixtureModel.FitMixtureModel(variants[0].Ad, variants[0].Dp);
models.Add(snvModel);
// Perform fitting for indels
Logger.WriteToLog("Finding thresholds for indels.");
MixtureModel indelModel = MixtureModel.FitMixtureModel(variants[1].Ad, variants[1].Dp);
models.Add(indelModel);
MixtureModel.WriteModelFile(outDir, Path.GetFileName(vcfIn).Replace(".vcf", ".model"), models);
return(models);
}
public void OutOfOrderStartingMeansTest()
{
string file = Path.Combine(TestPaths.LocalTestDataDirectory, "Chr1.csv");
SparseArray <int> AD = new SparseArray <int>();
SparseArray <int> DP = new SparseArray <int>();
using (StreamReader sr = new StreamReader(new FileStream(file, FileMode.Open)))
{
int counter = 0;
while (true)
{
string line = sr.ReadLine();
if (line == null || counter > 100000)
{
break;
}
string[] arr = line.Split(',');
int dp = int.Parse(arr[arr.Length - 1]);
DP.Add(dp);
if (arr.Length == 2)
{
AD.Add(dp - int.Parse(arr[0]));
}
else
{
AD.Add(int.Parse(arr[arr.Length - 2]));
}
counter++;
}
}
var model1 = MixtureModel.FitMixtureModel(AD, DP, new double[] { 0.01, 0.45, 0.99 });
var model2 = MixtureModel.FitMixtureModel(AD, DP, new double[] { 0.45, 0.01, 0.99 });
for (int i = 0; i < model1.Means.Length; i++)
{
Assert.Equal(model1.Means[i], model2.Means[i], 4);
Assert.Equal(model1.MixtureWeights[i], model2.MixtureWeights[i], 4);
}
}