public void TestGetNValueUsingTTest()
{
double expectedNValue = -9.276;
double expectedPValue = 0.137;
double expectedLogFoldChange = -0.087;
List <double> proteinFirstConditionIntensityValues = new List <double>();
List <double> proteinSecondConditionIntensityValues = new List <double>();
proteinFirstConditionIntensityValues.Add(25.535);
proteinFirstConditionIntensityValues.Add(25.482);
proteinFirstConditionIntensityValues.Add(25.308);
proteinFirstConditionIntensityValues.Add(25.373);
proteinSecondConditionIntensityValues.Add(25.370);
proteinSecondConditionIntensityValues.Add(25.368);
proteinSecondConditionIntensityValues.Add(25.359);
proteinSecondConditionIntensityValues.Add(25.251);
double sOValue = 0.3;
StatisticalTests statisticalTests = new StatisticalTests();
List <double> proteinStatistics = statisticalTests.GetNValueUsingTTest(proteinFirstConditionIntensityValues,
proteinSecondConditionIntensityValues, sOValue, false);
Assert.AreEqual(expectedNValue, proteinStatistics[0], 0.001);
Assert.AreEqual(expectedPValue, proteinStatistics[1], 0.001);
Assert.AreEqual(expectedLogFoldChange, proteinStatistics[2], 0.001);
}
public void TestCalculateNvaluethreshold()
{
double expectedNValueThreshold = 74;
List <double> observedNValues = new List <double>();
for (int i = 0; i < 100; i++)
{
observedNValues.Add(i + 100);
}
List <double> permutedNValues = new List <double>();
for (int i = 0; i < 100; i++)
{
permutedNValues.Add(i);
}
double FDR = 0.25;
StatisticalTests statisticalTests = new StatisticalTests();
double outputNValueThrehold = statisticalTests.calculateNvaluethreshold(observedNValues,
permutedNValues, FDR);
Assert.AreEqual(expectedNValueThreshold, outputNValueThrehold, 0.001);
}
public void TestCalculateProteinIntensityValuesStandardDeviation()
{
List <double> testIntensityValues = new List <double>();
testIntensityValues.Add(24.75);
testIntensityValues.Add(25.15);
testIntensityValues.Add(28.35);
testIntensityValues.Add(21.95);
double intensityValuesMean = 25.05;
double expectedStandardDev = 2.269;
StatisticalTests statisticalTests = new StatisticalTests();
double outputStandardDev = statisticalTests.CalculateProteinIntensityValuesStandardDeviation
(testIntensityValues, intensityValuesMean);
Assert.AreEqual(expectedStandardDev, outputStandardDev, 0.001);
}
public void TestStatisticalTestsGenerateAllCombinationsOfTwoConditions()
{
StatisticalTests statisticalTests = new StatisticalTests();
List <double> testIndices = new List <double>();
testIndices.Add(1);
testIndices.Add(2);
testIndices.Add(3);
testIndices.Add(4);
List <List <int> > expectedIndicesPairs = new List <List <int> >();
expectedIndicesPairs.Add(new List <int>()
{
0, 1
});
expectedIndicesPairs.Add(new List <int>()
{
0, 2
});
expectedIndicesPairs.Add(new List <int>()
{
0, 3
});
expectedIndicesPairs.Add(new List <int>()
{
1, 2
});
expectedIndicesPairs.Add(new List <int>()
{
1, 3
});
expectedIndicesPairs.Add(new List <int>()
{
2, 3
});
List <List <int> > resultIndicesPairs = statisticalTests.GenerateAllCombinationsOfTwoIndices(testIndices);
CollectionAssert.AreEqual(expectedIndicesPairs, resultIndicesPairs);
}
public void TestGetNValueUsingPermutationTests()
{
List <double> expectedPermutedNValues = new List <double>();
expectedPermutedNValues.Add(-13.021);
expectedPermutedNValues.Add(-12.269);
expectedPermutedNValues.Add(-8.298);
expectedPermutedNValues.Add(-12.119);
expectedPermutedNValues.Add(-8.270);
expectedPermutedNValues.Add(-8.161);
expectedPermutedNValues.Add(-17.649);
expectedPermutedNValues.Add(-19.749);
expectedPermutedNValues.Add(-24.623);
expectedPermutedNValues.Add(-20.303);
expectedPermutedNValues.Add(-23.756);
expectedPermutedNValues.Add(-20.594);
List <double> proteinFirstConditionIntensityValues = new List <double>();
List <double> proteinSecondConditionIntensityValues = new List <double>();
proteinFirstConditionIntensityValues.Add(25.535);
proteinFirstConditionIntensityValues.Add(25.482);
proteinFirstConditionIntensityValues.Add(25.308);
proteinFirstConditionIntensityValues.Add(25.373);
proteinSecondConditionIntensityValues.Add(25.370);
proteinSecondConditionIntensityValues.Add(25.368);
proteinSecondConditionIntensityValues.Add(25.359);
proteinSecondConditionIntensityValues.Add(25.251);
double sOValue = 0.3;
StatisticalTests statisticalTests = new StatisticalTests();
List <double> outputPermutedNValues = statisticalTests.GetNValueUsingPermutationtests(proteinFirstConditionIntensityValues,
proteinSecondConditionIntensityValues, sOValue);
Assert.That(expectedPermutedNValues, Is.EqualTo(outputPermutedNValues).Within(0.001));
}
public static void Main(string[] args)
{
RunProteinSignificanceClassifier proteinBasedSignificance = new RunProteinSignificanceClassifier();
int numberSamples = 0;
// Parse the ExperimentalDesign File to get info of samples and conditions they belong to
Dictionary <string, List <string> > samplefileConditionRelation = proteinBasedSignificance.ExpermientalDesignParser("C:/Users/Anay/Desktop/UW Madison/Smith Lab/Spectra Data/ExperimentalDesign.tsv"
, ref numberSamples);
// get all conditions and pair them up for Significance classification
List <string> allConditions = new List <string>(samplefileConditionRelation.Keys);
List <List <string> > allTwoConditionCombinations = proteinBasedSignificance.GenerateAllCombinationsOfTwoConditions(allConditions);
foreach (List <string> conditionPair in allTwoConditionCombinations)
{
string firstCondition = conditionPair[0];
string secondCondition = conditionPair[1];
double sOValue = 0.1;
double meanFraction = 0.1;
int maxSignificantCount = 0;
while (meanFraction < 1)
{
while (sOValue < 1)
{
for (int k = 0; k < numberSamples; k++)
{
proteinBasedSignificance = new RunProteinSignificanceClassifier();
//Declaring variables which will be generated after parsing QuantifiedPeptides file
List <ProteinRowInfo> allProteinInfo = new List <ProteinRowInfo>();
List <string> samplesFileNames = new List <string>();
int maxInvalidIntensityValues = k;
proteinBasedSignificance.ProteinDataParser(allProteinInfo, maxInvalidIntensityValues,
samplesFileNames, "C:/Users/Anay/Desktop/UW Madison/Smith Lab/Spectra Data/FlashLFQ_2020-04-26-17-39-35/QuantifiedProteins.tsv");
// imputes missing intensity values for each protein
ImputationProcess imputationProcess = new ImputationProcess();
imputationProcess.RunImputationProcess(allProteinInfo, samplesFileNames, meanFraction);
// Declaring variables which will be generated after T-Tests and Permutation Tests
List <double> observedNValues = new List <double>(); // will store observed N values
List <double> permutedNValues = new List <double>(); // will store permuted N values
StatisticalTests statisticalTests = new StatisticalTests();
// contains proteins and their observed N value, P Value and Log Fold Change
Dictionary <string, List <double> > allProteinObservedStatistics = new Dictionary <string, List <double> >();
// Creating threads for Parallelizing code
ThreadPool.GetMaxThreads(out int workerThreadsCount, out int ioThreadsCount);
int[] threads = Enumerable.Range(0, workerThreadsCount).ToArray();
Parallel.ForEach(threads, (i) =>
{
// Compute observed and permuted N Values for each protein using T Tests and Permutation Testing
for (; i < allProteinInfo.Count; i += workerThreadsCount)
{
ProteinRowInfo proteinRowInfo = allProteinInfo[i];
Dictionary <string, double> samplesintensityData = proteinRowInfo.SamplesIntensityData;
List <string> firstConditionAssociatedSamples = samplefileConditionRelation.GetValueOrDefault(firstCondition);
List <string> secondConditionAssociatedSamples = samplefileConditionRelation.GetValueOrDefault(secondCondition);
List <double> proteinFirstConditionIntensityValues = new List <double>();
List <double> proteinSecondConditionIntensityValues = new List <double>();
// get the protein's intensity values corresponding to the chosen pair of conditions
foreach (string sampleFileName in samplesFileNames)
{
if (firstConditionAssociatedSamples.Contains(sampleFileName))
{
proteinFirstConditionIntensityValues.Add(samplesintensityData[sampleFileName]);
}
if (secondConditionAssociatedSamples.Contains(sampleFileName))
{
proteinSecondConditionIntensityValues.Add(samplesintensityData[sampleFileName]);
}
}
// Compute observed N Values with the chosen pair of conditions using T-Tests and
// store in observedNValues array
List <double> proteinStatistics = statisticalTests.GetNValueUsingTTest(proteinFirstConditionIntensityValues, proteinSecondConditionIntensityValues,
sOValue, false);
// Compute permuted N Values with the chosen pair of conditions using T-Tests and
// store in permutedNValues array
List <double> proteinPermutedNavlues = statisticalTests.GetNValueUsingPermutationtests(proteinFirstConditionIntensityValues,
proteinSecondConditionIntensityValues, sOValue);
// add computed original and permuted statistics for the protein
lock (allProteinObservedStatistics)
{
// add protein and its observed N value, P Value and Log Fold Change in that order
allProteinObservedStatistics.Add(proteinRowInfo.ProteinID, new List <double>()
{
proteinStatistics[0],
proteinStatistics[1], proteinStatistics[2]
});
observedNValues.Add(proteinStatistics[0]);
foreach (double permutedNValue in proteinPermutedNavlues)
{
permutedNValues.Add(permutedNValue);
}
}
}
});
// makes the permuted N values list and the observed N Values list of the same size
proteinBasedSignificance.ResizePermutedArray(permutedNValues, permutedNValues.Count() - observedNValues.Count());
// get the threshold at which we will filter out the significant proteins
double nValueThreshold = statisticalTests.calculateNvaluethreshold(observedNValues, permutedNValues, 0.05);
// determine number of signifcant proteins detected
int newSignificantCount = observedNValues.Count(x => x >= nValueThreshold);
if (newSignificantCount > maxSignificantCount)
{
maxSignificantCount = newSignificantCount;
proteinBasedSignificance.PrintSignificantProtein(allProteinInfo, nValueThreshold, allProteinObservedStatistics,
"C:/Users/Anay/Desktop/UW Madison/Smith Lab/Project 1/ConsoleApp1/ProteinBasedSignificanceModified.csv");
Console.WriteLine("Sig Count - " + maxSignificantCount + "meanFraction - " + meanFraction + "sOValue - "
+ sOValue + "k - " + k);
}
}
sOValue = sOValue + 0.1;
}
sOValue = 0.1;
meanFraction = meanFraction + 0.3;
}
}
}
public static void Main(string[] args)
{
Program proteinBasedSignificance = new Program();
// Parse the ExperimentalDesign File to get info of samples and conditions they belong to
Dictionary <string, List <string> > samplefileConditionRelation = proteinBasedSignificance.ExpermientalDesignParser("C:/Users/Anay/Desktop/UW Madison/Smith Lab/Spectra Data/ExperimentalDesign.tsv");
// get all conditions and pair them up for Significance classification
List <string> allConditions = new List <string>(samplefileConditionRelation.Keys);
List <List <string> > allTwoConditionCombinations = proteinBasedSignificance.GenerateAllCombinationsOfTwoConditions(allConditions);
foreach (List <string> conditionPair in allTwoConditionCombinations)
{
string firstCondition = conditionPair[0];
string secondCondition = conditionPair[1];
double sOValue = 0.1;
double meanFraction = 0.1;
int maxSignificantCount = 0;
while (meanFraction < 1)
{
while (sOValue < 1)
{
for (int k = 1; k < 9; k++)
{
proteinBasedSignificance = new Program();
//Declaring variables which will be generated after parsing QuantifiedPeptides file
List <ProteinRowInfo> allProteinInfo = new List <ProteinRowInfo>();
List <string> samplesFileNames = new List <string>();
int maxInvalidIntensityValues = k;
proteinBasedSignificance.ProteinDataParser(allProteinInfo, maxInvalidIntensityValues,
samplesFileNames, "C:/Users/Anay/Desktop/UW Madison/Smith Lab/Spectra Data/FlashLFQ_2020-04-26-17-39-35/QuantifiedProteins.tsv");
// imputes missing intensity values for each protein
ImputationProcess imputationProcess = new ImputationProcess();
imputationProcess.RunImputationProcess(allProteinInfo, samplesFileNames, meanFraction);
// Declaring variables which will be generated after T-Tests and Permutation Tests
List <double> actualNValues = new List <double>(); // will store actual(real) N values
List <double> actualPValues = new List <double>(); // will store actual(real) P values
List <double> actualLogFoldChange = new List <double>(); // will store actual(real) Log Fold Change values
List <double> permutedNValues = new List <double>(); // will store permuted(fake) N values
StatisticalTests statisticalTests = new StatisticalTests();
// Compute actual and permuted N Values for each protein using T Tests and Permutation Testing
for (int i = 0; i < allProteinInfo.Count; i++)
{
ProteinRowInfo proteinRowInfo = allProteinInfo[i];
Dictionary <string, double> samplesintensityData = proteinRowInfo.SamplesIntensityData;
List <string> firstConditionAssociatedSamples = samplefileConditionRelation.GetValueOrDefault(firstCondition);
List <string> secondConditionAssociatedSamples = samplefileConditionRelation.GetValueOrDefault(secondCondition);
List <double> proteinFirstConditionIntensityValues = new List <double>();
List <double> proteinSecondConditionIntensityValues = new List <double>();
// get the protein's intensity values corresponding to the chosen pair of conditions
foreach (string sampleFileName in samplesFileNames)
{
if (firstConditionAssociatedSamples.Contains(sampleFileName))
{
proteinFirstConditionIntensityValues.Add(samplesintensityData[sampleFileName]);
}
if (secondConditionAssociatedSamples.Contains(sampleFileName))
{
proteinSecondConditionIntensityValues.Add(samplesintensityData[sampleFileName]);
}
}
// Compute actual(real) N Values with the chosen pair of conditions using T-Tests and
// store in actualNValues array
statisticalTests.GetNValueUsingTTest(proteinFirstConditionIntensityValues, proteinSecondConditionIntensityValues,
actualNValues, actualPValues, actualLogFoldChange, sOValue);
// Compute permuted(fake) N Values with the chosen pair of conditions using T-Tests and
// store in permutedNValues array
statisticalTests.GetNValueUsingPermutationtests(proteinFirstConditionIntensityValues, proteinSecondConditionIntensityValues,
permutedNValues, sOValue);
}
// makes the permuted N values list and the actual N Values list of the same size
proteinBasedSignificance.ResizePermutedArray(permutedNValues, permutedNValues.Count() - actualNValues.Count());
// Copy of the actual N values which will be used when determind the N Value threshold for target FDR
List <double> actualNValuesCopy = new List <double>();
for (int i = 0; i < actualNValues.Count; i++)
{
actualNValuesCopy.Add(actualNValues[i]);
}
// get the threshold at which we will filter out the significant proteins
double nValueThreshold = statisticalTests.calculateNvaluethreshold(actualNValuesCopy, permutedNValues, 0.05);
// determine number of signifcant proteins detected
int newSignificantCount = actualNValues.Count(x => x >= nValueThreshold);
if (newSignificantCount > maxSignificantCount)
{
maxSignificantCount = newSignificantCount;
proteinBasedSignificance.PrintSignificantProtein(allProteinInfo, actualNValues, nValueThreshold, actualPValues,
actualLogFoldChange, "C:/Users/Anay/Desktop/UW Madison/Smith Lab/Project 1/ConsoleApp1/ProteinBaseedSignificance.csv");
}
}
sOValue = sOValue + 0.1;
}
sOValue = 0.1;
meanFraction = meanFraction + 0.3;
}
}
}
