private void Plot2()
{
belaGraphControl2.ClearDataBuffer();
Font myFont = new Font(ComboBoxFonts.SelectedValue.ToString(), (float)DoubleUpDownFontSize.Value);
belaGraphControl2.Plot(BelaGraph.BackgroundOption.None, false, true, myFont);
//Lets clone our sparse matrix
SparseMatrix vennSparseMatrixCache = PatternTools.ObjectCopier.Clone(plp.MySparseMatrix);
List <IntersectionResultAnalysis> VennProbabilityDictionary = new List <IntersectionResultAnalysis>();
//Qaulity filterS
if ((bool)RadioButtonMinNoReplicates.IsChecked)
{
//BY NUMBER OF REPLICATES
vennSparseMatrixCache.EliminateIDsThatAreNotPresentAtLeastInXReplicates((int)IntegerUpDownMinNoReplicates.Value, (bool)RadioButtonAllClasses.IsChecked);
}
else
{
//by Probability
PatternTools.VennProbability.VennProbabilityCalculator vpc = new PatternTools.VennProbability.VennProbabilityCalculator(plp.MySparseMatrix);
VennProbabilityDictionary = vpc.GenerateProbabilisticDictionary(4);
string report = PatternTools.VennProbability.ResultPrinter.PrintReport(VennProbabilityDictionary, new List <string> {
"Low", "Medium", "High", "Very High"
});
RichTextBoxLog.Document.Blocks.Clear();
RichTextBoxLog.AppendText(report);
}
venn = new BelaGraph.VennManager(vennSparseMatrixCache);
//--------------------------------
//If we are working with probabilities, we should apply a correction
//The venn diagram with probability only works for two classes
if ((bool)RadioButtonFilteringProbability.IsChecked)
{
//We need to find the ones unique for class 1 and the ones unique for class 2.
//If they are below the probability scores, we need to include them in the other class
//this needs to be done using the dvs
//Step 1, the uniques from each group
List <List <int> > inLabel = new List <List <int> >();
inLabel.Add(new List <int>());
inLabel.Add(new List <int>());
inLabel[0].AddRange(venn.VennDic[1]);
inLabel[1].AddRange(venn.VennDic[2]);
List <int> intersection = inLabel[0].Intersect(inLabel[1]).ToList();
inLabel[0].RemoveAll(a => intersection.Contains(a));
inLabel[1].RemoveAll(a => intersection.Contains(a));
//Find the ones that fail the probability
for (int i = 1; i <= 2; i++)
{
List <int> newIntersectionClass = new List <int>(); //We will put all the new unique guys in here
foreach (int dim in inLabel[i - 1])
{
if (!checkConfidence(dim, i, (double)DoubleUpDownProbability.Value, VennProbabilityDictionary))
{
newIntersectionClass.Add(dim);
}
}
foreach (int k in newIntersectionClass)
{
venn.VennDic[i].Remove(k);
vennSparseMatrixCache.eliminateDim(k, 0, true);
}
}
}
//This is where we find the unique proteins
List <BelaGraph.DataVector> dvs = venn.GetDataVectors();
//Plot
ButtonPlot.Content = "Please wait...";
belaGraphControl2.ClearDataBuffer();
//Lets save this matrix so we can do reports latter
belaGraphControl2.vennSparseMatrixCache = vennSparseMatrixCache;
belaGraphControl2.Title = TextBoxTitle.Text;
belaGraphControl2.VennRadiusOfLargestCircleCorrectionFactor = (double)DoubleUpDownScaleFactor.Value;
if (lables.Count <= 3)
{
for (int i = 0; i < dvs.Count; i++)
{
System.Windows.Media.Color c1 = new System.Windows.Media.Color();
if (i == 0)
{
c1 = Colors.Green;
}
else if (i == 1)
{
c1 = Colors.Yellow;
}
else if (i == 2)
{
c1 = Colors.Blue;
}
System.Windows.Media.Color c2 = System.Windows.Media.Color.FromArgb(c1.A, c1.R, c1.G, c1.B);
System.Windows.Media.SolidColorBrush sb = new System.Windows.Media.SolidColorBrush(c2);
sb.Opacity = double.Parse(DoubleUpDownTransparency.Value.ToString());
dvs[i].MyBrush = sb;
belaGraphControl2.AddDataVector(dvs[i]);
}
BelaGraph.BackgroundOption bg = BelaGraph.BackgroundOption.None;
belaGraphControl2.Plot(bg, false, true, myFont);
}
//Fill out our table;
DataGridPairWise.Columns.Clear();
DataTable dt = new DataTable();
//Add the columns
//Add the info
for (int i = 0; i < dvs.Count; i++)
{
dt.Columns.Add(plp.MySparseMatrix.ClassDescriptionDictionary[int.Parse(dvs[i].Name)]);
}
for (int i = 0; i < dvs.Count; i++)
{
dt.Rows.Add(dvs[i].ThePoints.Select(a => a.Y.ToString()).ToArray());
}
DataGridPairWise.ItemsSource = dt.DefaultView;
if ((bool)CheckBoxPlotLabels.IsChecked && lables.Count <= 3)
{
try
{
belaGraphControl2.PlotVennLabels(venn.VennDic, TextBoxC1Name.Text, TextBoxC2Name.Text, TextBoxC3Name.Text, true, (bool)CheckBoxDetailed.IsChecked, plp.MyIndex, myFont);
}
catch (Exception e2)
{
MessageBox.Show("Make sure your sparse matrix file is labeled correctly. It should contain class 1, 2, and, 3" + "\n" + e2.Message);
}
}
ButtonPlot.Content = "Plot";
}
private void buttonPlot_Click(object sender, EventArgs e)
{
belaGraphControl2.ClearDataBuffer();
belaGraphControl2.Plot(BelaGraph.BackgroundOption.None, false, true, myFont);
//Lets clone our sparse matrix
SparseMatrix vennSparseMatrixCache = PatternTools.ObjectCopier.Clone(plp.MySparseMatrix);
List <IntersectionResultAnalysis> VennProbabilityDictionary = new List <IntersectionResultAnalysis>();
//Qaulity filterS
if (radioButtonFilteringMinNumberOfReplicates.Checked)
{
//BY NUMBER OF REPLICATES
vennSparseMatrixCache.EliminateIDsThatAreNotPresentAtLeastInXReplicates((int)numericUpDownMinimumNumberOfReplicates.Value, radioButtonAllClasses.Checked);
}
else
{
//by Probability
PatternTools.VennProbability.VennProbabilityCalculator vpc = new PatternTools.VennProbability.VennProbabilityCalculator(plp.MySparseMatrix);
VennProbabilityDictionary = vpc.GenerateProbabilisticDictionary(4);
string report = PatternTools.VennProbability.ResultPrinter.PrintReport(VennProbabilityDictionary, new List <string> {
"Low", "Medium", "High", "Very High"
});
richTextBoxLog.Clear();
richTextBoxLog.AppendText(report);
}
venn = new BelaGraph.VennManager(vennSparseMatrixCache);
//--------------------------------
//If we are working with probabilities, we should apply a correction
//The venn diagram with probability only works for two classes
if (radioButtonFilteringProbability.Checked)
{
//We need to find the ones unique for class 1 and the ones unique for class 2.
//If they are below the probability scores, we need to include them in the other class
//this needs to be done using the dvs
//Step 1, the uniques from each group
List <List <int> > inLabel = new List <List <int> >();
inLabel.Add(new List <int>());
inLabel.Add(new List <int>());
inLabel[0].AddRange(venn.VennDic[1]);
inLabel[1].AddRange(venn.VennDic[2]);
List <int> intersection = inLabel[0].Intersect(inLabel[1]).ToList();
inLabel[0].RemoveAll(a => intersection.Contains(a));
inLabel[1].RemoveAll(a => intersection.Contains(a));
//Find the ones that fail the probability
for (int i = 1; i <= 2; i++)
{
List <int> newIntersectionClass = new List <int>(); //We will put all the new unique guys in here
foreach (int dim in inLabel[i - 1])
{
if (!checkConfidence(dim, i, (double)numericUpDownFilteringProbability.Value, VennProbabilityDictionary))
{
newIntersectionClass.Add(dim);
}
}
foreach (int k in newIntersectionClass)
{
venn.VennDic[i].Remove(k);
vennSparseMatrixCache.eliminateDim(k, 0, true);
}
}
}
//This is where we find the unique proteins
List <BelaGraph.DataVector> dvs = venn.GetDataVectors();
//Plot
buttonPlot.Text = "Please wait...";
this.Update();
belaGraphControl2.ClearDataBuffer();
//Lets save this matrix so we can do reports latter
belaGraphControl2.vennSparseMatrixCache = vennSparseMatrixCache;
belaGraphControl2.Title = textBoxTitle.Text;
belaGraphControl2.VennRadiusOfLargestCircleCorrectionFactor = (double)numericUpDownScaleFactor.Value;
if (lables.Count <= 3)
{
for (int i = 0; i < dvs.Count; i++)
{
System.Drawing.Color c1 = new Color();
if (i == 0)
{
c1 = Color.Green;
}
else if (i == 1)
{
c1 = Color.Yellow;
}
else if (i == 2)
{
c1 = Color.Blue;
}
System.Windows.Media.Color c2 = System.Windows.Media.Color.FromArgb(c1.A, c1.R, c1.G, c1.B);
System.Windows.Media.SolidColorBrush sb = new System.Windows.Media.SolidColorBrush(c2);
sb.Opacity = double.Parse(numericUpDownOpacity.Value.ToString());
dvs[i].MyBrush = sb;
belaGraphControl2.AddDataVector(dvs[i]);
}
BelaGraph.BackgroundOption bg = (BelaGraph.BackgroundOption)Enum.Parse(typeof(BelaGraph.BackgroundOption), comboBoxBackground.SelectedItem.ToString());
belaGraphControl2.Plot(bg, false, true, myFont);
}
//Fill out our table;
dataGridView1.Columns.Clear();
//Add the columns
foreach (var r in dvs)
{
DataGridViewLinkColumn col = new DataGridViewLinkColumn();
col.Name = r.Name;
col.HeaderText = r.Name;
dataGridView1.Columns.Add(col);
}
//Add the info
for (int i = 0; i < dvs.Count; i++)
{
dataGridView1.Rows.Add();
dataGridView1.Rows[i].HeaderCell.Value = dvs[i].Name;
for (int j = 0; j < dvs[i].ThePoints.Count; j++)
{
DataGridViewLinkCell cell = new DataGridViewLinkCell();
cell.Value = dvs[i].ThePoints[j].Y;
dataGridView1.Rows[i].Cells[j] = cell;
}
}
if (checkBoxPlotLabels.Checked && lables.Count <= 3)
{
try
{
belaGraphControl2.PlotVennLabels(venn.VennDic, textBoxC1Name.Text, textBoxC2Name.Text, textBoxC3Name.Text, true, checkBoxDetailedLabel.Checked, plp.MyIndex, myFont);
}
catch (Exception e2)
{
MessageBox.Show("Make sure your sparse matrix file is labeled correctly. It should contain class 1, 2, and, 3" + "\n" + e2.Message);
}
}
buttonPlot.Text = "Plot";
}
