public void ExtractLcMsFeaturesForTrainingSet()
{
var methodName = MethodBase.GetCurrentMethod().Name;
TestUtils.ShowStarting(methodName);
const string idFileFolder = @"D:\MassSpecFiles\training\FilteredIdResult";
if (!Directory.Exists(idFileFolder))
{
Assert.Ignore(@"Skipping test {0} since folder not found: {1}", methodName, idFileFolder);
}
var tolerance = new Tolerance(10);
var tolerance2 = new Tolerance(20);
var id = 1;
for (var d = 0; d < TrainSetFileLists.Length; d++)
{
var dataset = TrainSetFileLists[d];
var dataname = Path.GetFileNameWithoutExtension(dataset);
var filtedIdResultFile = string.Format(@"{0}\{1}.trainset.tsv", idFileFolder, Path.GetFileNameWithoutExtension(dataset));
var featureResult = string.Format(@"{0}\{1}.ms1ft", idFileFolder, Path.GetFileNameWithoutExtension(dataset));
if (!File.Exists(dataset))
{
Console.WriteLine(@"Warning: Skipping since file not found: {0}", dataset);
continue;
}
if (!File.Exists(filtedIdResultFile))
{
Console.WriteLine(@"Warning: Skipping since file not found: {0}", filtedIdResultFile);
continue;
}
var run = PbfLcMsRun.GetLcMsRun(dataset);
var targetStatWriter = new StreamWriter(string.Format(@"D:\MassSpecFiles\training\statistics\{0}.tsv", Path.GetFileNameWithoutExtension(dataset)));
var decoyStatWriter = new StreamWriter(string.Format(@"D:\MassSpecFiles\training\statistics\{0}_decoy.tsv", Path.GetFileNameWithoutExtension(dataset)));
var writer = new StreamWriter(featureResult);
writer.Write("Ms2MinScan\tMs2MaxScan\tMs2MinCharge\tMs2MaxCharge\tMs2Mass\t");
writer.Write("Mass\tMinScan\tMaxScan\tMinCharge\tMaxCharge\tMinTime\tMaxTime\tElution\tGood\n");
var tsvParser = new TsvFileParser(filtedIdResultFile);
var featureFinder = new LcMsPeakMatrix(run);
for (var i = 0; i < tsvParser.NumData; i++)
{
var minScan = int.Parse(tsvParser.GetData("MinScan")[i]);
var maxScan = int.Parse(tsvParser.GetData("MaxScan")[i]);
var minCharge = int.Parse(tsvParser.GetData("MinCharge")[i]);
var maxCharge = int.Parse(tsvParser.GetData("MaxCharge")[i]);
var mass = double.Parse(tsvParser.GetData("Mass")[i]);
writer.Write(minScan);
writer.Write("\t");
writer.Write(maxScan);
writer.Write("\t");
writer.Write(minCharge);
writer.Write("\t");
writer.Write(maxCharge);
writer.Write("\t");
writer.Write(mass);
writer.Write("\t");
var binNum = featureFinder.Comparer.GetBinNumber(mass);
var binMass = featureFinder.Comparer.GetMzAverage(binNum);
var binNumList = (mass < binMass) ? new int[] { binNum, binNum - 1, binNum + 1 } : new int[] { binNum, binNum + 1, binNum - 1 };
LcMsPeakCluster refinedFeature = null;
foreach (var bi in binNumList)
{
var tempList = new List <LcMsPeakCluster>();
var features = featureFinder.FindFeatures(bi);
var massTh = (mass < 2000) ? tolerance2.GetToleranceAsTh(mass) : tolerance.GetToleranceAsTh(mass);
foreach (var feature in features)
{
if (Math.Abs(mass - feature.Mass) < massTh)
{
tempList.Add(feature);
}
}
//var nHits = 0;
var highestAbu = 0d;
//var scans = Enumerable.Range(minScan, maxScan - minScan + 1);
foreach (var feature in tempList)
{
//var scans2 = Enumerable.Range(feature.MinScanNum, feature.MaxScanNum - feature.MinScanNum + 1);
//var hitScans = scans.Intersect(scans2).Count();
if (feature.MinScanNum < 0.5 * (minScan + maxScan) &&
0.5 * (minScan + maxScan) < feature.MaxScanNum)
{
if (feature.Abundance > highestAbu)
{
refinedFeature = feature;
highestAbu = feature.Abundance;
}
}
/*if (hitScans > 0)
* {
* refinedFeature = feature;
* nHits = hitScans;
* }*/
}
if (refinedFeature != null)
{
break;
}
}
if (refinedFeature != null)
{
writer.Write(refinedFeature.Mass);
writer.Write("\t");
writer.Write(refinedFeature.MinScanNum);
writer.Write("\t");
writer.Write(refinedFeature.MaxScanNum);
writer.Write("\t");
writer.Write(refinedFeature.MinCharge);
writer.Write("\t");
writer.Write(refinedFeature.MaxCharge);
writer.Write("\t");
writer.Write(refinedFeature.MinElutionTime);
writer.Write("\t");
writer.Write(refinedFeature.MaxElutionTime);
writer.Write("\t");
writer.Write(refinedFeature.MaxElutionTime - refinedFeature.MinElutionTime);
writer.Write("\t");
var good = (refinedFeature.MinScanNum <= minScan && refinedFeature.MaxScanNum >= maxScan);
writer.Write(good ? 1 : 0);
writer.Write("\n");
//writer.Write(0); writer.Write("\t");
//writer.Write(0); writer.Write("\n");
OutputEnvelopPeakStat(id, refinedFeature, targetStatWriter);
var chargeRange = featureFinder.GetDetectableMinMaxCharge(refinedFeature.RepresentativeMass, run.MinMs1Mz, run.MaxMs1Mz);
refinedFeature.UpdateWithDecoyScore(featureFinder.Ms1Spectra, chargeRange.Item1, chargeRange.Item2);
OutputEnvelopPeakStat(id, refinedFeature, decoyStatWriter);
id++;
}
else
{
writer.Write(0);
writer.Write("\t");
writer.Write(0);
writer.Write("\t");
writer.Write(0);
writer.Write("\t");
writer.Write(0);
writer.Write("\t");
writer.Write(0);
writer.Write("\t");
writer.Write(0);
writer.Write("\t");
writer.Write(0);
writer.Write("\t");
writer.Write(0);
writer.Write("\t");
writer.Write(0);
writer.Write("\n");
}
//var feature = featureFinder.FindLcMsPeakCluster(mass, (int) scan, (int) charge);
}
writer.Close();
targetStatWriter.Close();
decoyStatWriter.Close();
Console.WriteLine(dataname);
}
}
public void TestLcMsFeatureFinder()
{
var methodName = MethodBase.GetCurrentMethod().Name;
Utils.ShowStarting(methodName);
var pbfFilePath = Utils.GetPbfTestFilePath(false);
var pbfFile = Utils.GetTestFile(methodName, pbfFilePath);
// var outTsvFilePath = MassSpecDataReaderFactory.ChangeExtension(rawFile, "ms1ft");
//var scoreDataPath = @"D:\MassSpecFiles\training";
var scorer = new LcMsFeatureLikelihood();
var stopwatch = Stopwatch.StartNew();
Console.WriteLine(@"Start loading MS1 data from {0}", pbfFile.FullName);
var run = PbfLcMsRun.GetLcMsRun(pbfFile.FullName);
var featureFinder = new LcMsPeakMatrix(run, scorer);
Console.WriteLine(@"Complete loading MS1 data. Elapsed Time = {0:0.000} sec",
(stopwatch.ElapsedMilliseconds) / 1000.0d);
var container = new LcMsFeatureContainer(featureFinder.Ms1Spectra, scorer, new LcMsFeatureMergeComparer(new Tolerance(10)));
var minSearchMassBin = featureFinder.Comparer.GetBinNumber(11180.33677);
var maxSearchMassBin = featureFinder.Comparer.GetBinNumber(11180.33677);
double totalMassBin = maxSearchMassBin - minSearchMassBin + 1;
Console.WriteLine(@"Start MS1 feature extraction.");
stopwatch.Restart();
for (var binNum = minSearchMassBin; binNum <= maxSearchMassBin; binNum++)
{
var clusters = featureFinder.FindFeatures(binNum);
container.Add(clusters);
if (binNum > minSearchMassBin && (binNum - minSearchMassBin) % 1000 == 0)
{
var elapsed = (stopwatch.ElapsedMilliseconds) / 1000.0d;
var processedBins = binNum - minSearchMassBin;
var processedPercentage = ((double)processedBins / totalMassBin) * 100;
Console.WriteLine(
@"Processing {0:0.0}% of mass bins ({1:0.0} Da); elapsed time = {2:0.000} sec; # of features = {3}",
processedPercentage, featureFinder.Comparer.GetMzEnd(binNum), elapsed,
container.NumberOfFeatures);
}
}
Console.WriteLine(@"Complete MS1 feature extraction.");
Console.WriteLine(@" - Elapsed time = {0:0.000} sec", (stopwatch.ElapsedMilliseconds) / 1000.0d);
Console.WriteLine(@" - Number of extracted features = {0}", container.NumberOfFeatures);
// write result files
Console.WriteLine(@"Start selecting mutually independent features from feature network graph");
stopwatch.Stop();
// Start to quantify accurate abundance
stopwatch.Restart();
//var quantAnalyzer = new TargetMs1FeatureMatrix(run);
//var oriResult = new List<Ms1FeatureCluster>();
//var quantResult = new List<Ms1Feature>();
var featureId = 0;
var ms1ScanNums = run.GetMs1ScanVector();
//tsvWriter.WriteLine(GetHeaderString() + "\tQMinScanNum\tQMaxScanNum\tQMinCharge\tQMaxCharge\tQAbundance");
var filteredFeatures = container.GetFilteredFeatures(featureFinder);
foreach (var feature in filteredFeatures)
{
Console.Write(featureId);
Console.Write("\t");
Console.Write(feature.Mass);
Console.Write("\t");
Console.Write(feature.MinScanNum);
Console.Write("\t");
Console.Write(feature.MaxScanNum);
Console.Write("\t");
Console.Write(feature.MinCharge);
Console.Write("\t");
Console.Write(feature.MaxCharge);
Console.Write("\t");
Console.Write(feature.RepresentativeScanNum);
Console.Write("\t");
Console.Write(feature.RepresentativeMz);
Console.Write("\t");
Console.Write(feature.RepresentativeCharge);
Console.Write("\t");
//Console.Write(feature.BestSummedEnvelopeDistance); Console.Write("\t");
//Console.Write(feature.BestEnvelopeDistance); Console.Write("\t");
Console.Write(feature.BestDistanceScoreAcrossCharge[0]);
Console.Write("\t");
Console.Write(feature.BestDistanceScoreAcrossCharge[1]);
Console.Write("\t");
Console.Write(feature.BestCorrelationScoreAcrossCharge[0]);
Console.Write("\t");
Console.Write(feature.BestCorrelationScoreAcrossCharge[1]);
Console.Write("\t");
Console.Write(feature.BestIntensityScoreAcrossCharge[0]);
Console.Write("\t");
Console.Write(feature.BestIntensityScoreAcrossCharge[1]);
Console.Write("\t");
Console.Write(feature.AbundanceDistributionAcrossCharge[0]);
Console.Write("\t");
Console.Write(feature.AbundanceDistributionAcrossCharge[1]);
Console.Write("\t");
Console.Write(feature.XicCorrelationBetweenBestCharges[0]);
Console.Write("\t");
Console.Write(feature.XicCorrelationBetweenBestCharges[1]);
Console.Write("\t");
Console.Write(feature.Score);
Console.Write("\n");
featureId++;
}
}
public void TestLcMsFeatureFinder()
{
var methodName = MethodBase.GetCurrentMethod().Name;
TestUtils.ShowStarting(methodName);
const string rawFile = @"D:\MassSpecFiles\training\raw\QC_Shew_Intact_26Sep14_Bane_C2Column3.pbf";
//const string rawFile = @"D:\MassSpecFiles\CompRef\CPTAC_Intact_CR_Pool_2_25Jun15_Bane_15-02-02RZ.pbf";
//const string rawFile = @"D:\MassSpecFiles\IMER\Dey_IMERblast_01_08May14_Alder_14-01-33.pbf";
//const string rawFile = @"\\proto-11\MSXML_Cache\PBF_Gen_1_193\2015_3\MZ20150729FG_WT1.pbf";
if (!File.Exists(rawFile))
{
Assert.Ignore(@"Skipping test {0} since file not found: {1}", methodName, rawFile);
}
// var outTsvFilePath = MassSpecDataReaderFactory.ChangeExtension(rawFile, "ms1ft");
//var scoreDataPath = @"D:\MassSpecFiles\training";
var scorer = new LcMsFeatureLikelihood();
var stopwatch = Stopwatch.StartNew();
Console.WriteLine(@"Start loading MS1 data from {0}", rawFile);
var run = PbfLcMsRun.GetLcMsRun(rawFile);
var featureFinder = new LcMsPeakMatrix(run, scorer);
Console.WriteLine(@"Complete loading MS1 data. Elapsed Time = {0:0.000} sec",
(stopwatch.ElapsedMilliseconds) / 1000.0d);
var container = new LcMsFeatureContainer(featureFinder.Ms1Spectra, scorer, new LcMsFeatureMergeComparer(new Tolerance(10)));
var minSearchMassBin = featureFinder.Comparer.GetBinNumber(11180.33677);
var maxSearchMassBin = featureFinder.Comparer.GetBinNumber(11180.33677);
double totalMassBin = maxSearchMassBin - minSearchMassBin + 1;
Console.WriteLine(@"Start MS1 feature extraction.");
stopwatch.Restart();
for (var binNum = minSearchMassBin; binNum <= maxSearchMassBin; binNum++)
{
var clusters = featureFinder.FindFeatures(binNum);
container.Add(clusters);
if (binNum > minSearchMassBin && (binNum - minSearchMassBin) % 1000 == 0)
{
var elapsed = (stopwatch.ElapsedMilliseconds) / 1000.0d;
var processedBins = binNum - minSearchMassBin;
var processedPercentage = ((double)processedBins / totalMassBin) * 100;
Console.WriteLine(
@"Processing {0:0.0}% of mass bins ({1:0.0} Da); elapsed time = {2:0.000} sec; # of features = {3}",
processedPercentage, featureFinder.Comparer.GetMzEnd(binNum), elapsed,
container.NumberOfFeatures);
}
}
Console.WriteLine(@"Complete MS1 feature extraction.");
Console.WriteLine(@" - Elapsed time = {0:0.000} sec", (stopwatch.ElapsedMilliseconds) / 1000.0d);
Console.WriteLine(@" - Number of extracted features = {0}", container.NumberOfFeatures);
// write result files
Console.WriteLine(@"Start selecting mutually independent features from feature network graph");
stopwatch.Stop();
// Start to quantify accurate abundance
stopwatch.Restart();
//var quantAnalyzer = new TargetMs1FeatureMatrix(run);
//var oriResult = new List<Ms1FeatureCluster>();
//var quantResult = new List<Ms1Feature>();
var featureId = 0;
var ms1ScanNums = run.GetMs1ScanVector();
//tsvWriter.WriteLine(GetHeaderString() + "\tQMinScanNum\tQMaxScanNum\tQMinCharge\tQMaxCharge\tQAbundance");
var filteredFeatures = container.GetFilteredFeatures(featureFinder);
foreach (var feature in filteredFeatures)
{
Console.Write(featureId);
Console.Write("\t");
Console.Write(feature.Mass);
Console.Write("\t");
Console.Write(feature.MinScanNum);
Console.Write("\t");
Console.Write(feature.MaxScanNum);
Console.Write("\t");
Console.Write(feature.MinCharge);
Console.Write("\t");
Console.Write(feature.MaxCharge);
Console.Write("\t");
Console.Write(feature.RepresentativeScanNum);
Console.Write("\t");
Console.Write(feature.RepresentativeMz);
Console.Write("\t");
Console.Write(feature.RepresentativeCharge);
Console.Write("\t");
//Console.Write(feature.BestSummedEnvelopeDistance); Console.Write("\t");
//Console.Write(feature.BestEnvelopeDistance); Console.Write("\t");
Console.Write(feature.BestDistanceScoreAcrossCharge[0]);
Console.Write("\t");
Console.Write(feature.BestDistanceScoreAcrossCharge[1]);
Console.Write("\t");
Console.Write(feature.BestCorrelationScoreAcrossCharge[0]);
Console.Write("\t");
Console.Write(feature.BestCorrelationScoreAcrossCharge[1]);
Console.Write("\t");
Console.Write(feature.BestIntensityScoreAcrossCharge[0]);
Console.Write("\t");
Console.Write(feature.BestIntensityScoreAcrossCharge[1]);
Console.Write("\t");
Console.Write(feature.AbundanceDistributionAcrossCharge[0]);
Console.Write("\t");
Console.Write(feature.AbundanceDistributionAcrossCharge[1]);
Console.Write("\t");
Console.Write(feature.XicCorrelationBetweenBestCharges[0]);
Console.Write("\t");
Console.Write(feature.XicCorrelationBetweenBestCharges[1]);
Console.Write("\t");
Console.Write(feature.Score);
Console.Write("\n");
featureId++;
}
}
/// <summary>
/// Find features in the data file
/// </summary>
/// <param name="rawFile">Data file (either a pbf file or a file type from which a pbf file can be auto-created)</param>
/// <returns>0 if success; negative number on error</returns>
private int ProcessFile(string rawFile)
{
var outDirectory = GetOutputDirectory(rawFile);
if (string.IsNullOrEmpty(outDirectory))
{
return(-1);
}
var baseName = Path.GetFileName(MassSpecDataReaderFactory.RemoveExtension(rawFile));
var ms1FeaturesFilePath = Path.Combine(outDirectory, baseName + "." + FileExtension);
var outCsvFilePath = Path.Combine(outDirectory, baseName + "_" + FileExtension + ".csv");
var pngFilePath = Path.Combine(outDirectory, baseName + "_" + FileExtension + ".png");
if (File.Exists(ms1FeaturesFilePath))
{
Console.WriteLine(@"ProMex output already exists: {0}", ms1FeaturesFilePath);
return(-2);
}
if (!File.Exists(rawFile))
{
ShowErrorMessage(@"Cannot find input file: " + rawFile);
return(-3);
}
var stopwatch = Stopwatch.StartNew();
Console.WriteLine(@"Start loading MS1 data from {0}", rawFile);
var run = PbfLcMsRun.GetLcMsRun(rawFile);
var featureFinder = new LcMsPeakMatrix(run, _likelihoodScorer, 1, 60, Parameters.MaxThreads);
Console.WriteLine(@"Complete loading MS1 data. Elapsed Time = {0:0.000} sec", (stopwatch.ElapsedMilliseconds) / 1000.0d);
if (run.GetMs1ScanVector().Length == 0)
{
ShowErrorMessage(@"Data file has no MS1 spectra: " + Path.GetFileName(rawFile));
return(-4);
}
var comparer = featureFinder.Comparer;
var container = new LcMsFeatureContainer(featureFinder.Ms1Spectra, _likelihoodScorer, new LcMsFeatureMergeComparer(new Tolerance(10)));
var minSearchMassBin = comparer.GetBinNumber(Parameters.MinSearchMass);
var maxSearchMassBin = comparer.GetBinNumber(Parameters.MaxSearchMass);
double totalMassBin = maxSearchMassBin - minSearchMassBin + 1;
Console.WriteLine(@"Start MS1 feature extraction.");
stopwatch.Restart();
for (var binNum = minSearchMassBin; binNum <= maxSearchMassBin; binNum++)
{
var clusters = featureFinder.FindFeatures(binNum);
container.Add(clusters);
if (binNum > minSearchMassBin && (binNum - minSearchMassBin) % 1000 == 0)
{
var elapsed = (stopwatch.ElapsedMilliseconds) / 1000.0d;
var processedBins = binNum - minSearchMassBin;
var processedPercentage = ((double)processedBins / totalMassBin) * 100;
Console.WriteLine(@"Processing {0:0.0}% of mass bins ({1:0.0} Da); elapsed time = {2:0.000} sec; # of features = {3}",
processedPercentage, featureFinder.Comparer.GetMzEnd(binNum), elapsed,
container.NumberOfFeatures);
}
}
Console.WriteLine(@"Complete MS1 feature extraction.");
Console.WriteLine(@" - Elapsed time = {0:0.000} sec", (stopwatch.ElapsedMilliseconds) / 1000.0d);
Console.WriteLine(@" - Number of extracted features = {0}", container.NumberOfFeatures);
Console.WriteLine(@"Start selecting mutually independent features from feature network graph");
stopwatch.Restart();
// write result files
var tsvWriter = new StreamWriter(ms1FeaturesFilePath);
tsvWriter.WriteLine(GetHeaderString(Parameters.ScoreReport));
StreamWriter csvWriter = null;
if (Parameters.CsvOutput)
{
csvWriter = new StreamWriter(outCsvFilePath);
csvWriter.WriteLine("scan_num,charge,abundance,mz,fit,monoisotopic_mw,FeatureID");
}
var filteredFeatures = container.GetFilteredFeatures(featureFinder);
var featureId = 0;
foreach (var feature in filteredFeatures)
{
featureId++;
tsvWriter.WriteLine("{0}\t{1}", featureId, GetString(feature, Parameters.ScoreReport));
var mostAbuIdx = feature.TheoreticalEnvelope.IndexOrderByRanking[0];
if (csvWriter != null)
{
foreach (var envelope in feature.EnumerateEnvelopes())
{
//var mostAbuIsotopeInternalIndex = cluster.IsotopeList.SortedIndexByIntensity[0];
var mostAbuPeak = envelope.Peaks[mostAbuIdx];
if (mostAbuPeak == null || !mostAbuPeak.Active)
{
continue;
}
var fitscore = 1.0 - feature.BestCorrelationScore;
csvWriter.WriteLine(string.Format("{0},{1},{2},{3},{4},{5},{6}", envelope.ScanNum, envelope.Charge, envelope.Abundance, mostAbuPeak.Mz, fitscore, envelope.MonoMass, featureId));
}
}
}
tsvWriter.Close();
Console.WriteLine(@"Complete feature filtration");
Console.WriteLine(@" - Elapsed time = {0:0.000} sec", (stopwatch.ElapsedMilliseconds) / 1000.0d);
Console.WriteLine(@" - Number of filtered features = {0}", featureId);
Console.WriteLine(@" - ProMex output: {0}", ms1FeaturesFilePath);
if (csvWriter != null)
{
csvWriter.Close();
Console.WriteLine(@" - ProMex output in ICR2LS format: {0}", outCsvFilePath);
}
if (Parameters.FeatureMapImage)
{
CreateFeatureMapImage(run, ms1FeaturesFilePath, pngFilePath);
}
return(0);
}
/// <summary>
/// Find features in the data file
/// </summary>
/// <param name="rawFile">Data file (either a pbf file or a file type from which a pbf file can be auto-created)</param>
/// <returns>0 if success; negative number on error</returns>
private int ProcessFile(string rawFile)
{
var outDirectory = GetOutputDirectory(rawFile);
if (string.IsNullOrEmpty(outDirectory))
{
return(-1);
}
var baseName = Path.GetFileName(MassSpecDataReaderFactory.RemoveExtension(rawFile));
var ms1FeaturesFilePath = Path.Combine(outDirectory, baseName + "." + FileExtension);
var outCsvFilePath = Path.Combine(outDirectory, baseName + "_" + FileExtension + ".csv");
var pngFilePath = Path.Combine(outDirectory, baseName + "_" + FileExtension + ".png");
if (File.Exists(ms1FeaturesFilePath))
{
ShowErrorMessage("ProMex output already exists: " + ms1FeaturesFilePath);
return(-2);
}
if (!File.Exists(rawFile))
{
ShowErrorMessage("Cannot find input file: " + rawFile);
return(-3);
}
var stopwatch = Stopwatch.StartNew();
Console.WriteLine("Start loading MS1 data from {0}", rawFile);
var run = PbfLcMsRun.GetLcMsRun(rawFile);
var featureFinder = new LcMsPeakMatrix(run, _likelihoodScorer, 1, 60, Parameters.MaxThreads);
Console.WriteLine("Complete loading MS1 data. Elapsed Time = {0:0.000} sec", (stopwatch.ElapsedMilliseconds) / 1000.0d);
if (run.GetMs1ScanVector().Length == 0)
{
ShowErrorMessage(@"Data file has no MS1 spectra: " + Path.GetFileName(rawFile));
return(-4);
}
if (featureFinder.Ms1PeakCount == 0)
{
ShowErrorMessage(@"Data file has no MS1 peaks: " + Path.GetFileName(rawFile));
return(-5);
}
var comparer = featureFinder.Comparer;
var container = new LcMsFeatureContainer(featureFinder.Ms1Spectra, _likelihoodScorer, new LcMsFeatureMergeComparer(new Tolerance(10)));
var minSearchMassBin = comparer.GetBinNumber(Parameters.MinSearchMass);
var maxSearchMassBin = comparer.GetBinNumber(Parameters.MaxSearchMass);
double totalMassBin = maxSearchMassBin - minSearchMassBin + 1;
Console.WriteLine("Start MS1 feature extraction.");
stopwatch.Restart();
for (var binNum = minSearchMassBin; binNum <= maxSearchMassBin; binNum++)
{
var clusters = featureFinder.FindFeatures(binNum);
container.Add(clusters);
if (binNum > minSearchMassBin && (binNum - minSearchMassBin) % 1000 == 0)
{
var elapsed = (stopwatch.ElapsedMilliseconds) / 1000.0d;
var processedBins = binNum - minSearchMassBin;
var processedPercentage = processedBins / totalMassBin * 100;
Console.WriteLine("Processing {0:0.0}% of mass bins ({1:0.0} Da); elapsed time = {2:0.000} sec; # of features = {3}",
processedPercentage, featureFinder.Comparer.GetMzEnd(binNum), elapsed,
container.NumberOfFeatures);
}
}
Console.WriteLine("Complete MS1 feature extraction.");
Console.WriteLine(" - Elapsed time = {0:0.000} sec", (stopwatch.ElapsedMilliseconds) / 1000.0d);
Console.WriteLine(" - Number of extracted features = {0}", container.NumberOfFeatures);
Console.WriteLine("Start selecting mutually independent features from feature network graph");
stopwatch.Restart();
var featureId = FilterAndOutputFeatures(container, featureFinder, outCsvFilePath, ms1FeaturesFilePath);
Console.WriteLine("Complete feature filtration");
Console.WriteLine(" - Elapsed time = {0:0.000} sec", (stopwatch.ElapsedMilliseconds) / 1000.0d);
Console.WriteLine(" - Number of filtered features = {0}", featureId);
Console.WriteLine(" - ProMex output: {0}", ms1FeaturesFilePath);
if (Parameters.CsvOutput)
{
Console.WriteLine(" - ProMex output in ICR2LS format: {0}", outCsvFilePath);
}
if (Parameters.FeatureMapImage)
{
CreateFeatureMapImage(run, ms1FeaturesFilePath, pngFilePath);
}
return(0);
}