//First method, run supervised learning with composition hypothesis.
public MS1ResultsViewer(OpenFileDialog oFDDeconData, String ComHypoLink)
{
InitializeComponent();
DeconData = oFDDeconData;
CompositionHypothesisTabbedForm ct = new CompositionHypothesisTabbedForm();
CompositionHypothesisList = ct.getCompHypo(ComHypoLink);
//This part is used to show the "Please Wait" box while running the code.
BackgroundWorker bw = new BackgroundWorker();
bw.DoWork += new DoWorkEventHandler(bw_DoWork);
bw.RunWorkerCompleted += new RunWorkerCompletedEventHandler(bw_RunWorkerCompleted);
//set lable and your waiting text in this form
try
{
bw.RunWorkerAsync();//this will run the DoWork code at background thread
WaitForm.ShowDialog();//show the please wait box
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
}
var elementsAndMolecules = GroupingResults.GetElementsAndMolecules(ResultGroups[0]);
ElementIDs = elementsAndMolecules.Item1;
MoleculeNames = elementsAndMolecules.Item2;
//ElementIDs.Clear();
//MoleculeNames.Clear();
//for (int i = 0; i < ResultGroups[0].Count(); i++)
//{
// if (ResultGroups[0][i].PredictedComposition.ElementNames.Count() > 0)
// {
// for (int j = 0; j < ResultGroups[0][i].PredictedComposition.ElementNames.Count(); j++)
// {
// ElementIDs.Add(ResultGroups[0][i].PredictedComposition.ElementNames[j]);
// }
// for (int j = 0; j < ResultGroups[0][i].PredictedComposition.MoleculeNames.Count(); j++)
// {
// MoleculeNames.Add(ResultGroups[0][i].PredictedComposition.MoleculeNames[j]);
// }
// }
//}
comboBox1.DataSource = oFDDeconData.FileNames;
comboBox1.SelectedIndex = 0;
VolumeHistogramFileSelector.DataSource = oFDDeconData.FileNames;
VolumeHistogramFileSelector.SelectedIndex = 0;
ResultsGridView.DataSource = ToDataTable(ResultGroups, 0);
ResultsGridView.ReadOnly = true;
}
private void oFDComposition_FileOk(object sender, CancelEventArgs e)
{
//First, get the path to the file.
String currentpath = oFDComposition.FileName;
//Second, populate the Form.
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
this.populateForm(currentpath);
}
private List<CompositionHypothesisEntry> obtainFalse(GlycanHypothesisCombinatorialGenerator GD, int numofLines, OpenFileDialog oFDPPMSD)
{
//obtain a random list with the correct bounds but the elemental composition randomized:
List<GlycanCompositionTable> randomCT = randomList(GD.comTable);
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
GD.comTable = randomCT;
List<CompositionHypothesisEntry> CTAns = comp.GenerateHypothesis(GD);
List<int> rangeone = Enumerable.Range(0, CTAns.Count()).ToList();
rangeone.Shuffle();
//restricting them to numperlist per list, which is half of the number of lines of the original composition hypothesis divided by numofFile Int32 numperlist = (numofLines / 2) / numofFiles;
List<CompositionHypothesisEntry> finalAns = new List<CompositionHypothesisEntry>();
for (int j = 0; j < numofLines; j++)
{
finalAns.Add(CTAns[rangeone[j]]);
}
Int32 Length = finalAns.Count();
//Next, mix it with Protein Prospector MS-digest file, if it exists.
Stream mystream = null;
try
{
if ((mystream = oFDPPMSD.OpenFile()) != null)
{
if (!String.IsNullOrEmpty(oFDPPMSD.FileName))
{
List<CompositionHypothesisEntry> FinalAns = getPPhypo(finalAns, finalAns, genFalsePPMSD(oFDPPMSD.FileName));
List<int> rangethree = Enumerable.Range(0, FinalAns.Count).ToList();
rangethree.Shuffle();
finalAns.Clear();
for (int i = 0; i < Length; i++)
{
finalAns.Add(FinalAns[rangethree[i]]);
}
}
}
}
catch
{
}
for (int i = 0; i < finalAns.Count(); i++)
{
finalAns[i].IsDecoy = false;
}
return finalAns;
}
private List<Peptide> genFalsePPMSD(String path)
{
Features fea = new Features();
List<Peptide> PP = fea.readtablim(path);
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
String sequence = comp.GetSequenceFromCleavedPeptides(PP);
List<int> forRandom = new List<int>
{
{0},{1},{2}
};
List<int> forlength = new List<int>
{
{4},{5},{6},{7},{8},{9},{10},{11}
};
List<Peptide> finalAns = new List<Peptide>();
Int32 StartAA = 0;
Int32 EndAA = 0;
while (EndAA != sequence.Count())
{
forRandom.Shuffle();
if (forRandom[0] == 1)
{
//add in this fragment
Peptide Ans = new Peptide();
forlength.Shuffle();
Int32 length = forlength[0];
EndAA = StartAA + length;
if (EndAA > sequence.Count())
EndAA = sequence.Count();
String Fra = "";
Ans.StartAA = Convert.ToInt32(StartAA + 1);
Ans.EndAA = Convert.ToInt32(EndAA + 1);
for (int i = StartAA; i < EndAA; i++)
{
Fra = Fra + sequence[i];
}
StartAA = StartAA + length;
Ans.Selected = true;
Ans.PeptideIndex = 1;
Ans.Mass = getFragmentMass(Fra);
Ans.Charge = 0;
Ans.Modifications = "";
Ans.MissedCleavages = 0;
Ans.PreviousAA = "";
Ans.NextAA = "";
forRandom.Shuffle();
Ans.NumGlycosylations = Convert.ToInt32(forRandom[0]);
finalAns.Add(Ans);
}
else
{
//Skip this fragment
forlength.Shuffle();
Int32 length = forlength[0];
EndAA = StartAA + length;
if (EndAA > sequence.Count())
EndAA = sequence.Count();
StartAA = StartAA + length;
}
if (EndAA == sequence.Count() && finalAns.Count() == 0)
{
EndAA = 0;
StartAA = 0;
}
}
return finalAns;
}
//This function draws the ROC curve by the TP and FP rates calculated from the score.
private void scoreBasedGraph()
{
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
SupervisedLearner SL = new SupervisedLearner();
Feature Fea = readFeature(oFDAddFeatureFiles.FileName);
//Create the list of random composition hypotesis for testing FDR.
//ObtainTrue Position data.
List<CompositionHypothesisEntry> CH = comp.getCompHypo(oFDCompositionTest.FileName);
List<ResultsGroup>[] TrueDATA = SL.EvaluateFeature(oFDTest, CH, Fea);
this.drawGraph(TrueDATA, " Loaded Features");
//Checkbox1 is default features.####################################
List<ResultsGroup>[] DefaultFeature = new List<ResultsGroup>[oFDTest.FileNames.Count()];
String path = Application.StartupPath + "\\FeatureDefault.fea";
Feature DeFea = readFeature(path);
if (checkBox1.Checked == true)
{
List<ResultsGroup>[] TrueDATADefault = SL.EvaluateFeature(oFDTest, CH, DeFea);
this.drawGraph(TrueDATADefault, " Default Features");
}
//################################################
//Checkbox2 is unsupervised Learning. It is a bit different from supervised learning, so it is hard-coded here.
if (checkBox2.Checked == true)
{
UnsupervisedLearner UL = new UnsupervisedLearner();
List<ResultsGroup>[] USLTrueDATA = UL.evaluate(oFDTest, Fea);
//ROC curve needs match to perform, so we will use the match list from Supervised learning and apply them to USLDATA.
for (int i = 0; i < oFDTest.FileNames.Count(); i++)
{
USLTrueDATA[i] = USLTrueDATA[i].OrderByDescending(b => b.DeconRow.MonoisotopicMassWeight).ToList();
int USllasttruematch = 0;
for (int j = 0; j < TrueDATA[i].Count; j++)
{
if (TrueDATA[i][j].Match == true)
{
for (int k = USllasttruematch; k < USLTrueDATA[i].Count; k++)
{
if (USLTrueDATA[i][k].DeconRow.MonoisotopicMassWeight < TrueDATA[i][j].DeconRow.MonoisotopicMassWeight)
{
USllasttruematch = k;
break;
}
if (USLTrueDATA[i][k].DeconRow.MonoisotopicMassWeight == TrueDATA[i][j].DeconRow.MonoisotopicMassWeight)
{
USLTrueDATA[i][k].Match = true;
USLTrueDATA[i][k].PredictedComposition = TrueDATA[i][j].PredictedComposition;
USllasttruematch = k + 1;
break;
}
if (USLTrueDATA[i][k].DeconRow.MonoisotopicMassWeight > TrueDATA[i][j].DeconRow.MonoisotopicMassWeight)
{
USLTrueDATA[i][k].Match = false;
}
}
}
}
}
//Now that both of the data got their matchs, draw the graph
this.drawGraph(USLTrueDATA, " Unsupervised Learning + Loaded Features");
}
//#############################unsupervised learning part ends#################
//Finally populate the Resulting datagridview and the combobox1
comboBox1.Invoke(new MethodInvoker(delegate
{
for (int i = 0; i < TF.Count; i++)
{
comboBox1.Items.Add(TF[i].TableName);
}
comboBox1.SelectedIndex = 0;
}));
dataGridView1.Invoke(new MethodInvoker(delegate
{
dataGridView1.DataSource = TF[0];
}));
}
void bw_DoWork(object sender, DoWorkEventArgs e)
{
//Obtain Feature
Feature Ans = new Feature();
SupervisedLearner SL = new SupervisedLearner();
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
List<CompositionHypothesisEntry> comhy = comp.getCompHypo(oFDComposition.FileName);
Ans = SL.obtainFeatures(oFDTrain, comhy);
//Write Features to File
String path = Application.StartupPath + "\\FeatureCurrent.fea";
FileStream FS = new FileStream(path, FileMode.Create, FileAccess.Write);
StreamWriter Write1 = new StreamWriter(FS);
Write1.Write(Ans.Initial + "," + Ans.numChargeStates + "," + Ans.ScanDensity + "," + Ans.numModiStates + "," + Ans.totalVolume + "," + Ans.ExpectedA + "," + Ans.CentroidScan + "," + Ans.numOfScan + "," + Ans.avgSigNoise);
Write1.Flush();
Write1.Close();
FS.Close();
}
//Generate GlycanCompositions Hypothesis Button
private void button5_Click(object sender, EventArgs e)
{
CompositionHypothesisTabbedForm opencompo = new CompositionHypothesisTabbedForm();
opencompo.Show();
}
private void oFDGenerator_FileOk(object sender, CancelEventArgs e)
{
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
button3.Enabled = true;
}
//This function draws the ROC curve by the TP and FP rates calculated from the score.
private void scoreBasedGraph()
{
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
Features FT = new Features();
String currentpath = Application.StartupPath + "\\FeatureCurrent.fea";
Feature Fea = FT.readFeature(currentpath);
//Create the list of random composition hypotesis for testing FDR.
//ObtainTrue Position data.
this.drawGraph(AllFinalResults, "");
//Finally populate the Resulting datagridview and the combobox1
comboBox2.Invoke(new MethodInvoker(delegate
{
for (int i = 0; i < TF.Count; i++)
{
comboBox2.Items.Add(TF[i].TableName);
}
comboBox2.SelectedIndex = 0;
}));
dataGridView2.Invoke(new MethodInvoker(delegate
{
dataGridView2.DataSource = TF[0];
}));
}
//This function draws the ROC curve by the TP and FP rates calculated from the false data set
private void DecoyDataROC()
{
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
SupervisedLearner SL = new SupervisedLearner();
Features featuresMenu = new Features();
String currentpath = Application.StartupPath + "\\FeatureCurrent.fea";
Feature currentFeatures = featuresMenu.readFeature(currentpath);
//Create the list of random composition hypotesis for testing FDR.
//ObtainTrue Position data.
falseDataset fD = new falseDataset();
List<CompositionHypothesisEntry> falseCH = fD.genFalse(oFDcposTest.FileName, CompositionHypothesisList, oFDPPMSD);
List<ResultsGroup>[] decoyData = SL.EvaluateFeature(DeconData, falseCH, currentFeatures);
this.drawGraph(decoyData, " ", 0);
//Checkbox1 is default features.####################################
List<ResultsGroup>[] DefaultFeature = new List<ResultsGroup>[DeconData.FileNames.Count()];
String path = Application.StartupPath + "\\FeatureDefault.fea";
Feature DeFea = featuresMenu.readFeature(path);
if (checkBox1.Checked == true)
{
List<ResultsGroup>[] decoyDataDefault = SL.EvaluateFeature(DeconData, falseCH, DeFea);
this.drawGraph(decoyDataDefault, " Default Features", 0);
}
//################################################
//Checkbox2 is unsupervised Learning. It is a bit different from supervised learning, so it is hard-coded here.
UnsupervisedLearner UL = new UnsupervisedLearner();
if (checkBox2.Checked == true)
{
List<ResultsGroup>[] USLFalseDATA = UL.evaluate(DeconData, currentFeatures);
//ROC curve needs match to perform, so we will use the match list from Supervised learning and apply them to USLDATA.
for (int i = 0; i < DeconData.FileNames.Count(); i++)
{
decoyData[i] = decoyData[i].OrderByDescending(a => a.DeconRow.MonoisotopicMassWeight).ToList();
USLFalseDATA[i] = USLFalseDATA[i].OrderByDescending(b => b.DeconRow.MonoisotopicMassWeight).ToList();
int USllasttruematch = 0;
for (int j = 0; j < decoyData[i].Count; j++)
{
if (decoyData[i][j].Match == true)
{
for (int k = USllasttruematch; k < USLFalseDATA[i].Count; k++)
{
if (USLFalseDATA[i][k].DeconRow.MonoisotopicMassWeight < decoyData[i][j].DeconRow.MonoisotopicMassWeight)
{
USllasttruematch = k;
break;
}
if (USLFalseDATA[i][k].DeconRow.MonoisotopicMassWeight == decoyData[i][j].DeconRow.MonoisotopicMassWeight)
{
USLFalseDATA[i][k].Match = true;
USLFalseDATA[i][k].PredictedComposition = decoyData[i][j].PredictedComposition;
USllasttruematch = k + 1;
break;
}
if (USLFalseDATA[i][k].DeconRow.MonoisotopicMassWeight > decoyData[i][j].DeconRow.MonoisotopicMassWeight)
{
USLFalseDATA[i][k].Match = false;
}
}
}
}
}
//Now that both of the data got their matchs, draw the graph
this.drawGraph(USLFalseDATA, " Unsupervised Learning", 0);
}
//#############################unsupervised learning part ends#################
//Finally populate the Resulting datagridview and the combobox1
comboBox2.Invoke(new MethodInvoker(delegate
{
for (int i = 0; i < TF.Count; i++)
{
comboBox2.Items.Add(TF[i].TableName);
}
comboBox2.SelectedIndex = 0;
}));
dataGridView2.Invoke(new MethodInvoker(delegate
{
dataGridView2.DataSource = TF[0];
}));
}
