/// <summary>
/// Applies the Bayesian Discriminant function at protein level
/// </summary>
/// <param name="myParams"></param>
public void BayesianCleaningAtProteinLevel()
{
int minNoExamplesPerClass = 5;
PatternTools.GaussianDiscriminant.Discriminant gd = new PatternTools.GaussianDiscriminant.Discriminant();
List <int> dims = new List <int> {
0, 1, 2, 3, 4, 5
};
int negativeClassExampleCounter = 0;
foreach (MyProtein p in MyProteinList)
{
//Find out what class does this belong
double label = 1;
if (p.Locus.StartsWith(myParams.LabeledDecoyTag))
{
label = -1;
negativeClassExampleCounter++;
}
gd.MySparseMatrix.addRow(new sparseMatrixRow((int)label, dims, p.InputVector));
}
//We need to make sure everything is working properly in this new normalization
//This greately degrades the classifier!!! never use!!!!
//gd.MySparseMatrix.NormalizeAllColumnsToRangeFrom0To1New();
//------
Console.WriteLine("Target examples for protein model = " + gd.MySparseMatrix.theMatrixInRows.FindAll(a => a.Lable == 1).Count);
Console.WriteLine("Decoy examples for protein model = " + gd.MySparseMatrix.theMatrixInRows.FindAll(a => a.Lable == -1).Count);
gd.Model(false, new List <int>(), minNoExamplesPerClass, true, false);
if (gd.ClassLableClassPackageDic.Keys.Count != 2)
{
throw new System.ArgumentException("Not enough examples to generate protein classification model. No available negative datapoints: " + negativeClassExampleCounter);
}
Parallel.ForEach(MyProteinList, r =>
//foreach (Scan s in p.MyScans)
{
//The result is ordered by class number
var results = gd.Classify(r.InputVector.ToArray());
double BayesianDiference = results[0].Score - results[1].Score;
r.BayesianScore = BayesianDiference;
}
);
double BayesianMin = MyProteinList.Min(a => a.BayesianScore);
double BayesianMax = MyProteinList.Max(a => a.BayesianScore);
double BayesianDif = BayesianMax - BayesianMin;
MyProteinList.Sort((a, b) => b.BayesianScore.CompareTo(a.BayesianScore));
int numberOfReverseProteins = MyProteinList.FindAll(a => a.Locus.StartsWith(myParams.LabeledDecoyTag)).Count;
int numberOfForwardProteins = MyProteinList.FindAll(a => !a.Locus.StartsWith(myParams.LabeledDecoyTag)).Count;
//Now lets do the filtering
int cutOffValue = MyProteinList.Count;
for (cutOffValue = MyProteinList.Count - 1; cutOffValue > 0; cutOffValue--)
{
if (MyProteinList[cutOffValue].Locus.StartsWith(myParams.LabeledDecoyTag))
{
numberOfReverseProteins--;
}
else
{
numberOfForwardProteins--;
}
//Calculate FDR;
double fdr = (double)numberOfReverseProteins / ((double)numberOfForwardProteins + (double)numberOfReverseProteins);
if (fdr <= myParams.ProteinFDR)
{
break;
}
}
MyProteinList.RemoveRange(cutOffValue, MyProteinList.Count - cutOffValue);
//Must cal this method to correct for the removed proteins
RebuildScansFromModifiedProteinList();
}
