private void buttonConvert_Click(object sender, EventArgs e)
{
if (!File.Exists(textBoxIndex.Text))
{
MessageBox.Show("Please specify an index file.");
return;
}
if (!File.Exists(textBoxSparseMatrix.Text))
{
MessageBox.Show("Please specify a sparse matrix file.");
return;
}
if (textBoxProjectDescription.Text.Length == 0)
{
MessageBox.Show("Please enter a simple project description.");
return;
}
if (saveFileDialog1.ShowDialog() != DialogResult.Cancel)
{
PatternLabProject plp = new PatternLabProject(textBoxSparseMatrix.Text, textBoxIndex.Text, textBoxProjectDescription.Text);
plp.Save(saveFileDialog1.FileName);
MessageBox.Show("Project converted.");
}
}
private void buttonLoadPLP_Click(object sender, EventArgs e)
{
openFileDialog1.Filter = "PatternLab Project (*.plp)|*.plp";
openFileDialog1.FileName = "";
if (openFileDialog1.ShowDialog() != DialogResult.Cancel)
{
plp = new PatternLabProject(openFileDialog1.FileName);
groupBoxStep2.Enabled = true;
labelNoElements.Text = plp.MyIndex.AllNames.Count.ToString();
}
}
private void buttonLoadPLP_Click(object sender, EventArgs e)
{
//Sparse Matrix Code
openFileDialog1.Filter = "PatternLab project file (*.plp)|*.plp";
if (openFileDialog1.ShowDialog() != DialogResult.Cancel)
{
plp = new PatternLabProject(openFileDialog1.FileName);
if (plp.MySparseMatrix.allDims().Count <= 2)
{
throw new Exception("The Anova test should only be used for experiments with at least 3 conditions");
}
if (checkBoxTotalSignalNormalization.Checked)
{
plp.MySparseMatrix.totalSignalNormalization();
}
buttonRecalculate.Enabled = true;
buttonSavePlot.Enabled = true;
Populate();
}
}
private void MenuItemExporToPLP_Click(object sender, RoutedEventArgs e)
{
SaveFileDialog sfd = new SaveFileDialog();
sfd.DefaultExt = ".txt";
sfd.Filter = "PatternLab Project (*.plp)|*.plp";
Nullable <bool> result = sfd.ShowDialog();
// Get the selected file name and display in a TextBox
if (result == true)
{
SparseMatrixIndexParserV2 smi = new SparseMatrixIndexParserV2();
int counter = 0;
List <FastaItem> orderedKeys = new List <FastaItem>();
foreach (KeyValuePair <FastaItem, List <PepQuant> > kvp in protPepDict)
{
if (kvp.Value.Count > IntegerUpDown.Value)
{
counter++;
SparseMatrixIndexParserV2.Index i = new SparseMatrixIndexParserV2.Index();
i.ID = counter;
i.Name = kvp.Key.SequenceIdentifier;
i.Description = kvp.Key.Description;
smi.Add(i);
orderedKeys.Add(kvp.Key);
}
}
SparseMatrix sm = new SparseMatrix();
sm.ClassDescriptionDictionary = new Dictionary <int, string>();
List <int> labels = Regex.Split(TextBoxClassLabel.Text, " ").Select(a => int.Parse(a)).ToList();
//Generate the dictionary
for (int i = 0; i < labels.Count; i++)
{
if (labels[i] < 0)
{
continue;
}
//Create the dictionary for the class
sm.ClassDescriptionDictionary.Add(i, (i).ToString());
List <int> dims = new List <int>();
List <double> values = new List <double>();
for (int j = 0; j < orderedKeys.Count; j++)
{
FastaItem fi = orderedKeys[j];
List <PepQuant> thePepQuants = protPepDict[fi];
double theIntensitySum = 0;
foreach (PepQuant pq in thePepQuants)
{
theIntensitySum += pq.MyQuants.Sum(a => a.MarkerIntensities[i]);
}
if (theIntensitySum > 0)
{
dims.Add(j + 1);
values.Add(theIntensitySum);
}
}
sparseMatrixRow smr = new sparseMatrixRow(i, dims, values);
sm.theMatrixInRows.Add(smr);
}
PatternLabProject plp = new PatternLabProject(sm, smi, "Isobaric Quant Project");
plp.Save(sfd.FileName);
MessageBox.Show("PLP file was saved");
Console.WriteLine("PLP file was saved.");
}
}
private void ButtonLoad_Click(object sender, RoutedEventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = "PatternLab project file (*.plp)|*.plp";
ofd.FileName = "";
if (ofd.ShowDialog() == true)
{
//- do the work
ButtonLoad.Content = "Working";
this.UpdateLayout();
TextBoxLegend.Clear();
TextBoxPatternLabProjectFile.Text = ofd.FileName;
PatternLabProject plp = new PatternLabProject(ofd.FileName);
plp.MySparseMatrix.UnsparseTheMatrix();
foreach (sparseMatrixRow unitSparseMatrixRow in plp.MySparseMatrix.theMatrixInRows)
{
unitSparseMatrixRow.Values = PatternTools.pTools.UnitVector(unitSparseMatrixRow.Values);
}
//Do the clustering
SparseMatrix smCLuster = new SparseMatrix();
if ((bool)RadioKernelPCA.IsChecked)
{
double[,] sm = plp.MySparseMatrix.ToDoubleArrayMatrix();
IKernel kernel = null;
//"Gaussian", "Linear", "Power", "Quadratic", "Sigmoid", "Spline"
if (ComboBoxKPCAKernels.SelectedValue.Equals("Gaussian"))
{
kernel = new Gaussian();
} else if (ComboBoxKPCAKernels.SelectedValue.Equals("Linear"))
{
kernel = new Linear();
} else if (ComboBoxKPCAKernels.SelectedValue.Equals("Power"))
{
kernel = new Power(2);
} else if (ComboBoxKPCAKernels.SelectedValue.Equals("Quadratic"))
{
kernel = new Quadratic();
} else if (ComboBoxKPCAKernels.SelectedValue.Equals("Sigmoid"))
{
kernel = new Sigmoid();
} else
{
kernel = new Spline();
}
// Creates the Kernel Principal Component Analysis of the given data
var kpca = new KernelPrincipalComponentAnalysis(sm, kernel);
// Compute the Kernel Principal Component Analysis
kpca.Compute();
// Creates a projection of the information
double[,] components = kpca.Transform(sm, 2);
for (int j = 0; j < components.GetLength(0); j++)
{
int l = plp.MySparseMatrix.theMatrixInRows[j].Lable;
sparseMatrixRow smr = new sparseMatrixRow(l, new List<int>() { 0, 1 }, new List<double>() { components[j, 0], components[j, 1] });
smr.FileName = plp.MySparseMatrix.theMatrixInRows[j].FileName;
smCLuster.addRow(smr);
}
smCLuster.ClassDescriptionDictionary = plp.MySparseMatrix.ClassDescriptionDictionary;
} else
{
MDS2 mds2 = new MDS2(plp.MySparseMatrix);
smCLuster = mds2.Converge(250, (double)DoubleUpDownSpringOutlier.Value);
}
Plot(smCLuster);
ButtonLoad.Content = "Browse";
}
}
public void Plot(List <TermScoreCalculator.TermScoreAnalysis> terms, PatternLabProject plp, int fontSize)
{
PlotModel MyModel = new PlotModel();
MyModel.Title = "Identification distribution along selected GO Terms";
var categoryAxis1 = new CategoryAxis();
categoryAxis1.Position = AxisPosition.Left;
categoryAxis1.FontSize = fontSize;
MyModel.Axes.Add(categoryAxis1);
var linearAxis1 = new LinearAxis();
linearAxis1.Position = AxisPosition.Bottom;
linearAxis1.FontSize = fontSize;
MyModel.Axes.Add(linearAxis1);
if (plp == null)
{
var barSeries1 = new BarSeries();
MyModel.Series.Add(barSeries1);
foreach (var tsa in terms)
{
categoryAxis1.Labels.Add(tsa.TermName);
barSeries1.Items.Add(new BarItem(tsa.ProteinIDs.Keys.Count, -1));
}
}
else
{
SparseMatrix sm = plp.MySparseMatrix.ShatterMatrixSum();
List <int> labels = sm.ExtractLabels();
Dictionary <int, BarSeries> barDict = new Dictionary <int, BarSeries>();
foreach (KeyValuePair <int, string> kvp in sm.ClassDescriptionDictionary)
{
BarSeries bs = new BarSeries();
bs.Title = kvp.Value;
barDict.Add(kvp.Key, bs);
MyModel.Series.Add(bs);
}
foreach (var tsa in terms)
{
int globalProtCounter = 0;
Dictionary <int, BarItem> tmpDict = new Dictionary <int, BarItem>();
foreach (int l in labels)
{
sparseMatrixRow theRow = sm.theMatrixInRows.Find(a => a.Lable == l);
int protCounter = 0;
foreach (string p in tsa.ProteinIDs.Keys)
{
//Search for proteins in this class
string cleanID = PatternTools.pTools.CleanPeptide(p, true);
int index = plp.MyIndex.TheIndexes.FindIndex(a => a.Name.Equals(cleanID));
if (index > -1)
{
double value = theRow.Values[index];
if (value > 0)
{
protCounter++;
globalProtCounter++;
}
else
{
Debug.Assert(true, "Protein not found");
}
}
}
//We need to use this as a work around as there are some terms that get 0 prots.
tmpDict.Add(l, new BarItem(protCounter, -1));
}
if (globalProtCounter > 0)
{
categoryAxis1.Labels.Add(tsa.TermName);
foreach (KeyValuePair <int, BarItem> kvp in tmpDict)
{
barDict[kvp.Key].Items.Add(kvp.Value);
}
}
}
}
MyPlot.Model = MyModel;
}
private async void Button_Click(object sender, RoutedEventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = "PatternLab Project (*.plp)|*.plp";
ofd.FileName = "";
if (ofd.ShowDialog() != true)
{
return;
}
Dictionary <int, string> allClassDescriptions = new Dictionary <int, string>();
try
{
WaitWindowWPF.Visibility = Visibility.Visible;
await Task.Run(() => {
plp = new PatternLabProject(ofd.FileName);
allClassDescriptions = plp.MySparseMatrix.ClassDescriptionDictionary;
plp.MySparseMatrix.theMatrixInRows.Sort((a, b) => a.Lable.CompareTo(b.Lable));
//Verify if the sparse matrix classes are appropriate
List <int> theLabels = plp.MySparseMatrix.ExtractLabels();
List <int> newLabels = new List <int>();
for (int i = 0; i < theLabels.Count; i++)
{
newLabels.Add(i + 1);
}
if (theLabels.Count != 2)
{
ClassSelection cs = new ClassSelection(theLabels, newLabels, plp.MySparseMatrix, true, 3);
cs.ShowDialog();
}
});
WaitWindowWPF.Visibility = Visibility.Collapsed;
ComboBoxFonts.Text = "Times New Roman";
ButtonPlot.IsEnabled = true;
ButtonSavePlot.IsEnabled = true;
}
catch
{
MessageBox.Show("Problems loading sparse matrix / index files");
return;
}
//Verify if the classes follow the pattern 1,2,3, else we should correct and warn the uses
List <int> lables = plp.MySparseMatrix.ExtractLabels();
Dictionary <int, int> labelConversion = new Dictionary <int, int>();
Dictionary <int, string> newClassDescriptions = new Dictionary <int, string>();
for (int i = 0; i < lables.Count; i++)
{
labelConversion.Add(lables[i], i + 1);
if (lables[i] != i + 1)
{
MessageBox.Show("Matrix lable " + lables[i] + " will be represented as group " + (i + 1));
}
}
foreach (sparseMatrixRow r in plp.MySparseMatrix.theMatrixInRows)
{
r.Lable = labelConversion[r.Lable];
}
//----------------------------------
//Update the descriptions, in case they have the word group
if (TextBoxC1Name.Text.Equals("Group") && lables.Count >= 1 && plp.MySparseMatrix.ClassDescriptionDictionary.ContainsKey(lables[0]) || allClassDescriptions.ContainsValue(TextBoxC1Name.Text))
{
TextBoxC1Name.Text = plp.MySparseMatrix.ClassDescriptionDictionary[lables[0]];
}
if (TextBoxC2Name.Text.Equals("Group") && lables.Count >= 2 && plp.MySparseMatrix.ClassDescriptionDictionary.ContainsKey(lables[1]) || allClassDescriptions.ContainsValue(TextBoxC2Name.Text))
{
TextBoxC2Name.Text = plp.MySparseMatrix.ClassDescriptionDictionary[lables[1]];
}
if (TextBoxC3Name.Text.Equals("Group") && lables.Count >= 3 && plp.MySparseMatrix.ClassDescriptionDictionary.ContainsKey(lables[2]) || allClassDescriptions.ContainsValue(TextBoxC3Name.Text))
{
TextBoxC3Name.Text = plp.MySparseMatrix.ClassDescriptionDictionary[lables[2]];
}
if (lables.Count != 2)
{
RadioButtonFilteringProbability.IsEnabled = false;
}
}
private void buttonParse_Click(object sender, EventArgs e)
{
openFileDialog1.Filter = "PatternLab Project (*.plp)|*.plp";
openFileDialog1.FileName = "";
if (openFileDialog1.ShowDialog() == DialogResult.Cancel)
{
return;
}
plp = new PatternLabProject(openFileDialog1.FileName);
//To make sure we dont duplicate info in the table
buttonParse.Text = "Wait!";
this.Update();
dataGridView1.Rows.Clear();
folds.Clear();
//try
//{
//Calculate the pscores and folds
ClassSelection cs;
//Verify if the sparse matrix classes are appropriate
List <int> theLabels = plp.MySparseMatrix.ExtractLabels();
buttonOptimizeFStringency.Enabled = true;
if (theLabels.Count > 2 || (theLabels.Count == 2 && ((!theLabels.Contains(1) || !theLabels.Contains(-1)))))
{
if (radioButtonAC.Checked)
{
cs = new ClassSelection(theLabels, new List <int> {
-1, 1
}, plp.MySparseMatrix, false, 2);
}
else if (radioButtonTTest.Checked)
{
cs = new ClassSelection(theLabels, new List <int> {
-1, 1
}, plp.MySparseMatrix, true, 2);
}
else
{
cs = new ClassSelection(theLabels, new List <int> {
-1, 1
}, plp.MySparseMatrix, true, 2);
}
cs.ShowDialog();
}
if (theLabels.Count == 1 && !theLabels.Contains(-1) && radioButtonTTest.Checked)
{
cs = new ClassSelection(theLabels, new List <int> {
-1
}, plp.MySparseMatrix, true, 1);
cs.ShowDialog();
}
//Refresh the labels
theLabels = plp.MySparseMatrix.ExtractLabels();
//Compress the matrix
if (radioButtonAC.Checked)
{
plp.MySparseMatrix.shatterMatrix();
}
else if (radioButtonTTest.Checked)
{
//Eliminate dims that do not satisfy the minimum readings
if (radioButtonAllClasses.Checked)
{
List <int> allDims = plp.MySparseMatrix.allDims();
List <int> dimsToEliminate = new List <int>();
foreach (int dim in allDims)
{
List <double> valuesT = plp.MySparseMatrix.ExtractDimValues(dim, 0, true);
if (valuesT.Count < (double)numericUpDownMinReplicates.Value)
{
dimsToEliminate.Add(dim);
}
}
foreach (int dim in dimsToEliminate)
{
plp.MySparseMatrix.eliminateDim(dim, 0, true);
}
}
else if (radioButtonPerClass.Checked)
{
List <int> allDims = plp.MySparseMatrix.allDims();
List <int> allClasses = plp.MySparseMatrix.ExtractLabels();
List <int> dimsToEliminate = new List <int>();
foreach (int dim in allDims)
{
foreach (int label in allClasses)
{
List <double> valuesT = plp.MySparseMatrix.ExtractDimValues(dim, label, false);
if (valuesT.Count < (double)numericUpDownMinReplicates.Value)
{
dimsToEliminate.Add(dim);
}
}
}
dimsToEliminate = dimsToEliminate.Distinct().ToList();
foreach (int dim in dimsToEliminate)
{
plp.MySparseMatrix.eliminateDim(dim, 0, true);
}
}
}
//matrix.saveMatrix(@"C:\Users\Paulo C Carvalho\Desktop\PatternLabExampleData\ExampleData\mstep2DimElimination.txt");
//Add pseudo spectral counts
if (!radioButtonUsingLabeledData.Checked)
{
plp.MySparseMatrix.addPseudoCounts(1);
}
//Determine what normalization the user specified
int normalization = 0;
if (radioButtonNRowSigma.Checked)
{
normalization = 1;
}
else if (radioButtonNTS.Checked)
{
normalization = 2;
}
else if (radioButtonUsingLabeledData.Checked)
{
normalization = 3;
}
if (theLabels.Count <= 2)
{
XFoldCalculations fs = new XFoldCalculations(plp.MySparseMatrix);
theData = fs.Xfold(normalization, radioButtonAC.Checked, (double)numericUpDownPValueLowerClip.Minimum);
//Calculate all the folds
folds.Clear();
foreach (var dataPoint in theData)
{
double correctedFoldChange;
correctedFoldChange = dataPoint.CorrectedFold();
//keep track of fold Changes
double foldChangeToAdd = correctedFoldChange;
if (foldChangeToAdd > 3)
{
foldChangeToAdd = 3;
}
else if (foldChangeToAdd < -3)
{
foldChangeToAdd = -3;
}
folds.Add(correctedFoldChange);
}
//Populate Result Table
//Fill the Data Table
foreach (XFoldStruct ac in theData)
{
//Just in case
if (ac.GPI == 0)
{
continue;
}
try
{
dataGridView1.Rows.Add(
ac.GPI,
plp.MyIndex.GetName(ac.GPI),
Math.Round(ac.FoldChange, 2),
Math.Round(ac.pValue, 4),
ac.QuantitationPositiveClass,
ac.QuantitationNegativeClass,
plp.MyIndex.GetDescription(ac.GPI)
);
}
catch (Exception e2)
{
MessageBox.Show(e2.Message);
}
}
}
//Were done
groupBoxSelectionParameters.Enabled = true;
groupBoxFunctions.Enabled = true;
//Make sure the canvas is clean
//acPlotWPF1.dispose();
//Clear the labels
labelBlueDots.Text = "0";
labelGreenDots.Text = "0";
labelRedDots.Text = "0";
labelTotalNumber.Text = "0";
//}
//catch (Exception z)
//{
// ErrorWindow ew = new ErrorWindow(z.GetBaseException().ToString());
// ew.ShowDialog();
//}
buttonParse.Text = "Parse";
}
private void buttonGo_Click(object sender, EventArgs e)
{
//Verify write permission to directory
if (!Directory.Exists(textBoxOutputDirectory.Text))
{
MessageBox.Show("Please specify a valid output directory");
return;
}
if (!Regex.IsMatch(textBoxIsobaricMasses.Text, "[0-9]+ [0-9]+"))
{
MessageBox.Show("Please fill out the masses of the isobaric tags.");
return;
}
if (!PatternTools.pTools.HasWriteAccessToFolder(textBoxOutputDirectory.Text))
{
MessageBox.Show("Please specify a valid output directory");
return;
}
//Obtain class labels
if (textBoxClassLabels.Text.Length == 0)
{
MessageBox.Show("Please input the class labels (eg., for iTRAQ 1,2,3,4");
return;
}
List <int> labels = Regex.Split(textBoxClassLabels.Text, " ").Select(a => int.Parse(a)).ToList();
//Obtain the isobaric masses
string[] im = Regex.Split(textBoxIsobaricMasses.Text, " ");
List <double> isobaricMasses = im.Select(a => double.Parse(a)).ToList();
if (labels.Count != isobaricMasses.Count)
{
MessageBox.Show("Please make sure that the class labels and isobaric masses match");
return;
}
buttonGo.Text = "Working...";
this.Update();
richTextBoxLog.Clear();
//--------------------------------------------
//Get signal from all
signalAllNormalizationDictionary = new Dictionary <string, double[]>();
//if (false)
FileInfo fi = new FileInfo(textBoxitraqSEPro.Text);
bool extractSignal = false;
ResultPackage rp = null;
if (checkBoxNormalizationChannelSignal.Checked)
{
//We should get the MS infor and merge it the the sepro package
if (fi.Extension.Equals(".sepr"))
{
rp = ResultPackage.Load(textBoxitraqSEPro.Text);
extractSignal = true;
}
List <FileInfo> rawFiles = fi.Directory.GetFiles("*.RAW").ToList();
foreach (FileInfo rawFile in rawFiles)
{
Console.WriteLine("Extracting data for " + rawFile.Name);
PatternTools.RawReader.RawReaderParams rParams = new PatternTools.RawReader.RawReaderParams();
rParams.ExtractMS1 = false;
rParams.ExtractMS2 = true;
rParams.ExtractMS3 = false;
PatternTools.RawReader.Reader reader = new PatternTools.RawReader.Reader(rParams);
List <MSLight> theMS2 = reader.GetSpectra(rawFile.FullName, new List <int>(), false);
theMS2.RemoveAll(a => a.Ions == null);
double [] totalSignal = new double[isobaricMasses.Count];
List <SQTScan> theScans = null;
//Update the sepro result package with the signal
if (extractSignal)
{
//Get all the scans from this file
string rawName = rawFile.Name.Substring(0, rawFile.Name.Length - 4);
theScans = rp.MyProteins.AllSQTScans.FindAll(a => a.FileName.Substring(0, a.FileName.Length - 4).Equals(rawName));
}
foreach (MSLight ms in theMS2)
{
double[] thisQuantitation = GetIsobaricSignal(ms.Ions, isobaricMasses);
if (extractSignal)
{
SQTScan scn = theScans.Find(a => a.ScanNumber == ms.ScanNumber);
if (scn != null)
{
scn.MSLight = ms;
scn.MSLight.Ions.RemoveAll(a => a.MZ > 400);
}
}
for (int i = 0; i < thisQuantitation.Length; i++)
{
totalSignal[i] += thisQuantitation[i];
}
}
string theName = rawFile.Name.Substring(0, rawFile.Name.Length - 3);
theName += "sqt";
signalAllNormalizationDictionary.Add(theName, totalSignal);
}
}
Console.WriteLine("Loading SEPro File");
if (!File.Exists(textBoxitraqSEPro.Text))
{
MessageBox.Show("Unable to find SEPro file");
return;
}
#region Load the spero or pepexplorer file
theScansToAnalyze = new List <SQTScan>();
List <FastaItem> theFastaItems = new List <FastaItem>();
if (fi.Extension.Equals(".sepr"))
{
Console.WriteLine("Loading SEPro file");
if (!extractSignal)
{
rp = ResultPackage.Load(textBoxitraqSEPro.Text);
}
rp.MyProteins.AllSQTScans.RemoveAll(a => a.MSLight == null);
theScansToAnalyze = rp.MyProteins.AllSQTScans;
Console.WriteLine("Done reading SEPro result");
theFastaItems = rp.MyProteins.MyProteinList.Select(a => new FastaItem(a.Locus, a.Sequence, a.Description)).ToList();
}
else if (fi.Extension.Equals(".mpex"))
{
Console.WriteLine("Loading PepExplorer file....");
PepExplorer2.Result2.ResultPckg2 result = PepExplorer2.Result2.ResultPckg2.DeserializeResultPackage(textBoxitraqSEPro.Text);
theFastaItems = result.MyFasta;
theScansToAnalyze = new List <SQTScan>();
foreach (PepExplorer2.Result2.AlignmentResult al in result.Alignments)
{
foreach (var dnr in al.DeNovoRegistries)
{
SQTScan sqt = new SQTScan();
sqt.ScanNumber = dnr.ScanNumber;
sqt.FileName = dnr.FileName;
sqt.PeptideSequence = dnr.PtmSequence;
theScansToAnalyze.Add(sqt);
}
}
//And now we need to retrieve the mass spectra. For this, the raw files should be inside the directory containing the mpex file
List <string> rawFiles = theScansToAnalyze.Select(a => a.FileName).Distinct().ToList();
for (int i = 0; i < rawFiles.Count; i++)
{
rawFiles[i] = rawFiles[i].Remove(rawFiles[i].Length - 3, 3);
rawFiles[i] = rawFiles[i] += "raw";
}
foreach (string fn in rawFiles)
{
Console.WriteLine("Retrieving spectra for file: " + fn);
ParserUltraLightRAW parser = new ParserUltraLightRAW();
string tmpFile = fn.Substring(0, fn.Length - 3);
List <SQTScan> scansForThisFile = theScansToAnalyze.FindAll(a => Regex.IsMatch(tmpFile, a.FileName.Substring(0, a.FileName.Length - 3), RegexOptions.IgnoreCase)).ToList();
List <int> scnNumbers = scansForThisFile.Select(a => a.ScanNumber).ToList();
FileInfo theInputFile = new FileInfo(textBoxitraqSEPro.Text);
List <MSUltraLight> theSpectra = parser.ParseFile(theInputFile.DirectoryName + "/" + fn, -1, 2, scnNumbers);
foreach (SQTScan sqt in scansForThisFile)
{
MSUltraLight spec = theSpectra.Find(a => a.ScanNumber == sqt.ScanNumber);
sqt.MSLight = new MSLight();
sqt.MSLight.MZ = spec.Ions.Select(a => (double)a.Item1).ToList();
sqt.MSLight.Intensity = spec.Ions.Select(a => (double)a.Item2).ToList();
}
Console.WriteLine("\tDone processing this file.");
}
}
else
{
throw new Exception("This file format is not supported.");
}
#endregion
//Obtaining multiplexed spectra
SEProQ.IsobaricQuant.YadaMultiplexCorrection.YMC ymc = null;
if (textBoxCorrectedYadaDirectory.Text.Length > 0)
{
Console.WriteLine("Reading Yada results");
ymc = new IsobaricQuant.YadaMultiplexCorrection.YMC(new DirectoryInfo(textBoxCorrectedYadaDirectory.Text));
Console.WriteLine("Done loading Yada results");
}
//Remove multiplexed spectra from sepro results
if (textBoxCorrectedYadaDirectory.Text.Length > 0)
{
int removedCounter = 0;
foreach (KeyValuePair <string, List <int> > kvp in ymc.fileNameScanNumberMultiplexDictionary)
{
Console.WriteLine("Removing multiplexed spectra for file :: " + kvp.Key);
richTextBoxLog.AppendText("Removing multiplexed spectra for file :: " + kvp.Key + "\n");
string cleanName = kvp.Key.Substring(0, kvp.Key.Length - 4);
cleanName += ".sqt";
foreach (int scnNo in kvp.Value)
{
int index = theScansToAnalyze.FindIndex(a => a.ScanNumber == scnNo && a.FileName.Equals(cleanName));
if (index >= 0)
{
Console.Write(theScansToAnalyze[index].ScanNumber + " ");
richTextBoxLog.AppendText(theScansToAnalyze[index].ScanNumber + " ");
removedCounter++;
theScansToAnalyze.RemoveAt(index);
}
}
Console.WriteLine("\n");
richTextBoxLog.AppendText("\n");
}
Console.WriteLine("Done removing multiplexed spectra :: " + removedCounter);
}
PatternTools.CSML.Matrix correctionMatrix = new PatternTools.CSML.Matrix();
if (checkBoxApplyPurityCorrection.Checked)
{
List <List <double> > correctionData = GetPurityCorrectionsFromForm();
correctionMatrix = IsobaricQuant.IsobaricImpurityCorrection.GenerateInverseCorrectionMatrix(correctionData);
}
//--------------------------------------------------------------------------------------------------------------------
//Prepare normalization Dictionary
signalIdentifiedNormalizationDictionary = new Dictionary <string, double[]>();
List <string> fileNames = theScansToAnalyze.Select(a => a.FileName).Distinct().ToList();
foreach (string fileName in fileNames)
{
signalIdentifiedNormalizationDictionary.Add(fileName, new double[isobaricMasses.Count]);
}
//-------------------------------------
//If necessary, correct for impurity and feed global signal dictionary
foreach (SQTScan scn in theScansToAnalyze)
{
double[] thisQuantitation = GetIsobaricSignal(scn.MSLight.Ions, isobaricMasses);
double maxSignal = thisQuantitation.Max();
//We can only correct for signal for those that have quantitation values in all places
if (checkBoxApplyPurityCorrection.Checked && (thisQuantitation.Count(a => a > maxSignal * (double)numericUpDownIonCountThreshold.Value) == isobaricMasses.Count))
{
thisQuantitation = IsobaricQuant.IsobaricImpurityCorrection.CorrectForSignal(correctionMatrix, thisQuantitation).ToArray();
}
if (checkBoxNormalizationChannelSignal.Checked)
{
for (int k = 0; k < thisQuantitation.Length; k++)
{
signalIdentifiedNormalizationDictionary[scn.FileName][k] += thisQuantitation[k];
}
}
scn.Quantitation = new List <List <double> >()
{
thisQuantitation.ToList()
};
}
//And now normalize -------------------
if (checkBoxNormalizationChannelSignal.Checked)
{
Console.WriteLine("Performing channel signal normalization for " + theScansToAnalyze.Count + " scans.");
foreach (SQTScan scn2 in theScansToAnalyze)
{
for (int m = 0; m < isobaricMasses.Count; m++)
{
scn2.Quantitation[0][m] /= signalIdentifiedNormalizationDictionary[scn2.FileName][m];
}
if (scn2.Quantitation[0].Contains(double.NaN))
{
Console.WriteLine("Problems on signal of scan " + scn2.FileNameWithScanNumberAndChargeState);
}
}
}
comboBoxSelectFileForGraphs.Items.Clear();
foreach (string file in signalIdentifiedNormalizationDictionary.Keys.ToList())
{
comboBoxSelectFileForGraphs.Items.Add(file);
}
tabControlMain.SelectedIndex = 1;
if (radioButtonAnalysisPeptideReport.Checked)
{
//Peptide Analysis
//Write Peptide Analysis
StreamWriter sw = new StreamWriter(textBoxOutputDirectory.Text + "/" + "PeptideQuantitationReport.txt");
//Eliminate problematic quants
int removed = theScansToAnalyze.RemoveAll(a => Object.ReferenceEquals(a.Quantitation, null));
Console.WriteLine("Problematic scans removed: " + removed);
var pepDic = from scn in theScansToAnalyze
group scn by scn.PeptideSequenceCleaned
into groupedSequences
select new { PeptideSequence = groupedSequences.Key, TheScans = groupedSequences.ToList() };
foreach (var pep in pepDic)
{
sw.WriteLine("Peptide:" + pep.PeptideSequence + "\tSpecCounts:" + pep.TheScans.Count);
foreach (SQTScan sqt in pep.TheScans)
{
sw.WriteLine(sqt.FileNameWithScanNumberAndChargeState + "\t" + string.Join("\t", sqt.Quantitation[0]));
}
}
//And now write the Fasta
sw.WriteLine("#Fasta Items");
foreach (FastaItem fastaItem in theFastaItems)
{
sw.WriteLine(">" + fastaItem.SequenceIdentifier + " " + fastaItem.Description);
sw.WriteLine(fastaItem.Sequence);
}
sw.Close();
}
else
{
rp = ResultPackage.Load(textBoxitraqSEPro.Text);
//Peptide Level
if (true)
{
PatternTools.SparseMatrixIndexParserV2 ip = new SparseMatrixIndexParserV2();
List <int> allDims = new List <int>();
List <PeptideResult> peptides = rp.MyProteins.MyPeptideList;
if (checkBoxOnlyUniquePeptides.Checked)
{
int removedPeptides = peptides.RemoveAll(a => a.MyMapableProteins.Count > 1);
Console.WriteLine("Removing {0} peptides for not being unique.", removedPeptides);
}
for (int i = 0; i < peptides.Count; i++)
{
SparseMatrixIndexParserV2.Index index = new SparseMatrixIndexParserV2.Index();
index.Name = peptides[i].PeptideSequence;
index.Description = string.Join(" ", peptides[i].MyMapableProteins);
index.ID = i;
ip.Add(index, true);
allDims.Add(i);
}
SparseMatrix sm = new SparseMatrix();
List <int> dims = ip.allIDs();
for (int l = 0; l < labels.Count; l++)
{
if (labels[l] < 0)
{
continue;
}
sparseMatrixRow smr = new sparseMatrixRow(labels[l]);
List <double> values = new List <double>(dims.Count);
List <int> dimsWithValues = new List <int>();
foreach (int d in dims)
{
List <SQTScan> scns = peptides[d].MyScans.FindAll(a => !object.ReferenceEquals(a.Quantitation, null));
if (scns.Count > 0)
{
double signalSum = scns.FindAll(a => !double.IsNaN(a.Quantitation[0][l])).Sum(a => a.Quantitation[0][l]);
values.Add(signalSum);
dimsWithValues.Add(d);
}
}
smr.Dims = dimsWithValues;
smr.Values = values;
smr.FileName = isobaricMasses[l].ToString();
sm.addRow(smr);
}
PatternLabProject plp = new PatternLabProject(sm, ip, "IsobaricQuant");
plp.Save(textBoxOutputDirectory.Text + "/MyPatternLabProjectPeptides.plp");
}
//Protein Level
if (true)
{
//Generate Index
PatternTools.SparseMatrixIndexParserV2 ip = new SparseMatrixIndexParserV2();
List <MyProtein> theProteins = rp.MyProteins.MyProteinList;
if (checkBoxOnlyUniquePeptides.Checked)
{
int removedProteins = theProteins.RemoveAll(a => !a.PeptideResults.Exists(b => b.NoMyMapableProteins == 1));
Console.WriteLine("{0} removed proteins for not having unique peptides", removedProteins);
}
for (int i = 0; i < theProteins.Count; i++)
{
SparseMatrixIndexParserV2.Index index = new SparseMatrixIndexParserV2.Index();
index.ID = i;
index.Name = theProteins[i].Locus;
index.Description = theProteins[i].Description;
ip.Add(index, false);
}
//SparseMatrix
SparseMatrix sm = new SparseMatrix();
List <int> dims = ip.allIDs();
for (int l = 0; l < labels.Count; l++)
{
if (labels[l] < 0)
{
continue;
}
if (!sm.ClassDescriptionDictionary.ContainsKey(labels[l]))
{
sm.ClassDescriptionDictionary.Add(labels[l], labels[l].ToString());
}
sparseMatrixRow smr = new sparseMatrixRow(labels[l]);
List <double> values = new List <double>(dims.Count);
List <int> dimsToInclude = new List <int>();
foreach (int d in dims)
{
double signalSum = 0;
List <PeptideResult> thePeptides = theProteins[d].PeptideResults;
if (checkBoxOnlyUniquePeptides.Checked)
{
thePeptides.RemoveAll(a => a.MyMapableProteins.Count > 1);
}
foreach (PeptideResult pr in thePeptides)
{
List <SQTScan> scns = pr.MyScans.FindAll(a => !object.ReferenceEquals(a.Quantitation, null));
foreach (SQTScan sqt in scns)
{
if (!double.IsNaN(sqt.Quantitation[0][l]) && !double.IsInfinity(sqt.Quantitation[0][l]))
{
signalSum += sqt.Quantitation[0][l];
}
}
}
if (signalSum > 0)
{
dimsToInclude.Add(d);
values.Add(signalSum);
}
else
{
Console.WriteLine("No signal found for " + theProteins[d].Locus + " on marker " + l);
}
}
smr.Dims = dims;
smr.Values = values;
smr.FileName = isobaricMasses[l].ToString();
sm.addRow(smr);
}
PatternLabProject plp = new PatternLabProject(sm, ip, "IsobaricQuant");
plp.Save(textBoxOutputDirectory.Text + "/MyPatternLabProjectProteins.plp");
}
}
comboBoxSelectFileForGraphs.Enabled = true;
tabControlMain.SelectedIndex = 2;
Console.WriteLine("Done");
buttonGo.Text = "Generate Report";
}
public void ParseTrendQuestACFoldOrIndex(string fileName)
{
FileInfo fi = new FileInfo(fileName);
if (fi.Extension.Equals(".plp"))
{
MyPatternLabProject = new PatternLabProject(fileName);
foreach (var i in MyPatternLabProject.MyIndex.TheIndexes)
{
theResults.Add(i.Name, 1);
AddProtein(i.Name, i.Description);
}
}
else
{
StreamReader sr = new StreamReader(fileName);
List <string> duplicateKeys = new List <string>(); //Used for debugging
string read;
Regex tabSpliter = new Regex(@"\t");
theResults.Clear();
//We first need to know if we are dealing with an ACFold Report or Index file
//We believe that the indexFile contains no "#"
bool indexFile = true;
bool skip = false;
int lineCounter = 0;
while ((read = sr.ReadLine()) != null)
{
lineCounter++;
if (read.StartsWith("#") || read.Length == 0)
{
//This is a comment
if (!skip)
{
indexFile = false;
}
if (read.StartsWith("#TrendQuest"))
{
indexFile = true; //It is a trendquest file
skip = true;
}
}
else
{
string[] theLine = tabSpliter.Split(read);
if (indexFile)
{
//Make sure we are not duplicating keys!
if (theResults.ContainsKey(theLine[1]))
{
duplicateKeys.Add(theLine[1]);
}
else
{
theResults.Add(theLine[1], 1);
AddProtein(theLine[1], theLine[2]);
}
}
else
{
//We are dealing with an ACFold report
if (read.StartsWith("#") || read.Length == 0 || read.StartsWith("\t") || read.StartsWith("\""))
{
continue;
}
theResults.Add(theLine[0], double.Parse(theLine[1]));
AddProtein(theLine[0], theLine[6]);
}
}
}
sr.Close();
}
}
private void buttonLoad_Click(object sender, EventArgs e)
{
openFileDialog1.Filter = "PatternLab Project (*.plp)|*.plp";
openFileDialog1.FileName = "";
if (openFileDialog1.ShowDialog() == DialogResult.Cancel)
{
return;
}
Dictionary <int, string> allClassDescriptions = new Dictionary <int, string>();
try
{
plp = new PatternLabProject(openFileDialog1.FileName);
allClassDescriptions = plp.MySparseMatrix.ClassDescriptionDictionary;
plp.MySparseMatrix.theMatrixInRows.Sort((a, b) => a.Lable.CompareTo(b.Lable));
//Verify if the sparse matrix classes are appropriate
List <int> theLabels = plp.MySparseMatrix.ExtractLabels();
List <int> newLabels = new List <int>();
for (int i = 0; i < theLabels.Count; i++)
{
newLabels.Add(i + 1);
}
if (theLabels.Count != 2 || (theLabels.Count == 2 && (!theLabels.Contains(1) || !theLabels.Contains(-1))))
{
ClassSelection cs = new ClassSelection(theLabels, newLabels, plp.MySparseMatrix, true, 3);
cs.ShowDialog();
}
groupBoxPlotOptions.Enabled = true;
}
catch
{
MessageBox.Show("Problems loading sparse matrix / index files");
return;
}
//Verify if the classes follow the pattern 1,2,3, else we should correct and warn the uses
List <int> lables = plp.MySparseMatrix.ExtractLabels();
Dictionary <int, int> labelConversion = new Dictionary <int, int>();
Dictionary <int, string> newClassDescriptions = new Dictionary <int, string>();
for (int i = 0; i < lables.Count; i++)
{
labelConversion.Add(lables[i], i + 1);
if (lables[i] != i + 1)
{
MessageBox.Show("Matrix lable " + lables[i] + " will be represented as group " + (i + 1));
}
}
foreach (sparseMatrixRow r in plp.MySparseMatrix.theMatrixInRows)
{
r.Lable = labelConversion[r.Lable];
}
//----------------------------------
//Update the descriptions, in case they have the word group
if (textBoxC1Name.Text.Equals("Group") && lables.Count >= 1 && plp.MySparseMatrix.ClassDescriptionDictionary.ContainsKey(lables[0]) || allClassDescriptions.ContainsValue(textBoxC1Name.Text))
{
textBoxC1Name.Text = plp.MySparseMatrix.ClassDescriptionDictionary[lables[0]];
//textBoxC1Name.Text = sMatrix.ClassDescriptionDictionary[1];
}
if (textBoxC2Name.Text.Equals("Group") && lables.Count >= 2 && plp.MySparseMatrix.ClassDescriptionDictionary.ContainsKey(lables[1]) || allClassDescriptions.ContainsValue(textBoxC2Name.Text))
{
textBoxC2Name.Text = plp.MySparseMatrix.ClassDescriptionDictionary[lables[1]];
//textBoxC2Name.Text = sMatrix.ClassDescriptionDictionary[2];
}
if (textBoxC3Name.Text.Equals("Group") && lables.Count >= 3 && plp.MySparseMatrix.ClassDescriptionDictionary.ContainsKey(lables[2]) || allClassDescriptions.ContainsValue(textBoxC3Name.Text))
{
textBoxC3Name.Text = plp.MySparseMatrix.ClassDescriptionDictionary[lables[2]];;
//textBoxC3Name.Text = sMatrix.ClassDescriptionDictionary[3];
}
if (lables.Count != 2)
{
radioButtonFilteringProbability.Enabled = false;
}
}
