[Timeout(36000000)] // These can take a long time in code coverage mode public void TestRunQuantification() { var cache = new QrFactorizationCache(); var csvReader = new DsvFileReader(GetTextReader("quant.csv"), ','); var dataRowsByProtein = ToDataRows(ReadCsvFile(csvReader)); var expectedResultsByProtein = ReadCsvFile(new DsvFileReader(GetTextReader("runquantdata.csv"), ',')).ToLookup(row => row["Protein"]); foreach (var entry in dataRowsByProtein) { var expectedResultsByRun = expectedResultsByProtein[entry.Key].ToLookup(row => row["RUN"]); FoldChangeDataSet dataSet = FoldChangeCalculator.MakeDataSet(entry.Value); var designMatrix = DesignMatrix.GetRunQuantificationDesignMatrix(dataSet); var runNames = FoldChangeCalculator.GetUniqueList(entry.Value.Select(row => row.Run)); var results = designMatrix.PerformLinearFit(cache); for (int i = 0; i < dataSet.RunCount; i++) { string message = string.Format("Protein:{0} Run:{1}", entry.Key, runNames[i]); var expectedRow = expectedResultsByRun[runNames[i]].FirstOrDefault(); Assert.IsNotNull(expectedRow); Assert.AreEqual(double.Parse(expectedRow["LogIntensities"], CultureInfo.InvariantCulture), results[i].EstimatedValue, .000001, message); Assert.AreEqual(int.Parse(expectedRow["NumFeature"], CultureInfo.InvariantCulture), dataSet.FeatureCount, message); Assert.AreEqual(int.Parse(expectedRow["NumPeaks"], CultureInfo.InvariantCulture), dataSet.GetFeatureCountForRun(i), message); } } }
private void TestGroupComparison(TextReader textReader, bool includeInteraction, IDictionary <string, LinearFitResult> expectedResults) { var csvReader = new DsvFileReader(textReader, ','); var dataRowsByProtein = ToDataRows(ReadCsvFile(csvReader)); Assert.AreNotEqual(0, dataRowsByProtein.Count); var cache = new QrFactorizationCache(); foreach (var entry in dataRowsByProtein) { FoldChangeDataSet dataSet = FoldChangeCalculator.MakeDataSet(entry.Value); var designMatrix = DesignMatrix.GetDesignMatrix(dataSet, includeInteraction); var foldChange = designMatrix.PerformLinearFit(cache).First(); LinearFitResult expectedResult = null; if (null != expectedResults) { Assert.IsTrue(expectedResults.TryGetValue(entry.Key, out expectedResult)); } if (null != expectedResult) { Assert.AreEqual(expectedResult.EstimatedValue, foldChange.EstimatedValue, 1E-6); Assert.AreEqual(expectedResult.DegreesOfFreedom, foldChange.DegreesOfFreedom); Assert.AreEqual(expectedResult.StandardError, foldChange.StandardError, 1E-6); Assert.AreEqual(expectedResult.TValue, foldChange.TValue, 1E-6); Assert.AreEqual(expectedResult.PValue, foldChange.PValue, 1E-6); } } }
private GroupComparisonResult CalculateFoldChangeUsingRegression( GroupComparisonSelector selector, List <RunAbundance> runAbundances) { var detailRows = new List <DataRowDetails>(); GetDataRows(selector, detailRows); if (detailRows.Count == 0) { return(null); } runAbundances = runAbundances ?? new List <RunAbundance>(); var foldChangeDataRows = detailRows .Where(row => !double.IsNaN(row.GetLog2Abundance()) && !double.IsInfinity(row.GetLog2Abundance())) .Select(row => new FoldChangeCalculator.DataRow { Abundance = row.GetLog2Abundance(), Control = row.Control, Feature = row.IdentityPath, Run = row.ReplicateIndex, Subject = row.BioReplicate, }).ToArray(); FoldChangeDataSet runQuantificationDataSet = FoldChangeCalculator.MakeDataSet(foldChangeDataRows); var runNumberToReplicateIndex = FoldChangeCalculator.GetUniqueList(foldChangeDataRows.Select(row => row.Run)); var runQuantificationDesignMatrix = DesignMatrix.GetRunQuantificationDesignMatrix(runQuantificationDataSet); var quantifiedRuns = runQuantificationDesignMatrix.PerformLinearFit(_qrFactorizationCache); var subjects = new List <int>(); for (int run = 0; run < quantifiedRuns.Count; run++) { int iRow = runQuantificationDataSet.Runs.IndexOf(run); subjects.Add(runQuantificationDataSet.Subjects[iRow]); var replicateIndex = runNumberToReplicateIndex[run]; var replicateDetails = _replicateIndexes.First(kvp => kvp.Key == replicateIndex).Value; runAbundances.Add(new RunAbundance { ReplicateIndex = replicateIndex, Control = replicateDetails.IsControl, BioReplicate = replicateDetails.BioReplicate, Log2Abundance = quantifiedRuns[run].EstimatedValue }); } var abundances = quantifiedRuns.Select(result => result.EstimatedValue).ToArray(); var quantifiedDataSet = new FoldChangeDataSet( abundances, Enumerable.Repeat(0, quantifiedRuns.Count).ToArray(), Enumerable.Range(0, quantifiedRuns.Count).ToArray(), subjects, runQuantificationDataSet.SubjectControls); if (quantifiedDataSet.SubjectControls.Distinct().Count() < 2) { return(null); } var foldChangeResult = DesignMatrix.GetDesignMatrix(quantifiedDataSet, false).PerformLinearFit(_qrFactorizationCache).First(); return(new GroupComparisonResult(selector, quantifiedRuns.Count, foldChangeResult, runAbundances)); }
[Timeout(36000000)] // These can take a long time in code coverage mode public void TestGroupComparisonWithRunQuantification() { var csvReader = new DsvFileReader(GetTextReader("quant.csv"), ','); var dataRowsByProtein = ToDataRows(ReadCsvFile(csvReader)); var expectedResultsByProtein = ReadCsvFile(new DsvFileReader(GetTextReader("result_newtesting_v2.csv"), ',')) .ToDictionary(row => row["Protein"]); var cache = new QrFactorizationCache(); foreach (var entry in dataRowsByProtein) { FoldChangeDataSet dataSet = FoldChangeCalculator.MakeDataSet(entry.Value); var quantifiedRuns = DesignMatrix.GetRunQuantificationDesignMatrix(dataSet).PerformLinearFit(cache); var subjects = new List <int>(); for (int run = 0; run < quantifiedRuns.Count; run++) { int iRow = dataSet.Runs.IndexOf(run); subjects.Add(dataSet.Subjects[iRow]); } var abundances = quantifiedRuns.Select(result => result.EstimatedValue).ToArray(); var quantifiedDataSet = new FoldChangeDataSet( abundances, Enumerable.Repeat(0, quantifiedRuns.Count).ToArray(), Enumerable.Range(0, quantifiedRuns.Count).ToArray(), subjects, dataSet.SubjectControls); var foldChangeResult = DesignMatrix.GetDesignMatrix(quantifiedDataSet, false).PerformLinearFit(cache).First(); var expectedResult = expectedResultsByProtein[entry.Key]; string message = entry.Key; Assert.AreEqual(double.Parse(expectedResult["logFC"], CultureInfo.InvariantCulture), foldChangeResult.EstimatedValue, 1E-6, message); Assert.AreEqual(double.Parse(expectedResult["SE"], CultureInfo.InvariantCulture), foldChangeResult.StandardError, 1E-6, message); Assert.AreEqual(int.Parse(expectedResult["DF"], CultureInfo.InvariantCulture), foldChangeResult.DegreesOfFreedom, message); if (Math.Abs(foldChangeResult.EstimatedValue) > 1E-8) { Assert.AreEqual(double.Parse(expectedResult["pvalue"], CultureInfo.InvariantCulture), foldChangeResult.PValue, 1E-6, message); Assert.AreEqual(double.Parse(expectedResult["Tvalue"], CultureInfo.InvariantCulture), foldChangeResult.TValue, 1E-6, message); } } }