public void SetSymbolNumberMatrix(string name, double[,] value)
{
clsRCmdLog.LogRComment(string.Format("RdnConnectorClass.SetSymbolNumberMatrix:{0}", name));
var sym = _engine.CreateNumericMatrix(value);
_engine.SetSymbol(name, sym);
}
/// <summary>
/// Run PCA in R engine
/// </summary>
static void PCAR()
{
const string directoryPath = @"C:\Users\Káťa\Desktop\Diplomka\mesh\meshes";
var matrix = loadFacesR(directoryPath);
REngine.SetEnvironmentVariables();
REngine engine = REngine.GetInstance();
NumericMatrix group1 = engine.CreateNumericMatrix(matrix);
engine.SetSymbol("group1", group1);
GenericVector testResult = engine.Evaluate("pr.out=prcomp(group1, scale=TRUE)").AsList();
var eigenVec = testResult["x"].AsNumericMatrix();
engine.Evaluate("pr.out$sdev");
engine.Evaluate("pr.var = pr.out$sdev ^ 2");
engine.Evaluate("pr.var");
engine.Evaluate("pve = pr.var / sum(pr.var)");
engine.Evaluate("pve");
engine.Evaluate("plot(cumsum(pve), xlab='Principal Component', ylab='Cumulative Proportion of Variance Explained', ylim=c(0,1),type='b')");
saveVector(eigenVec);
// var eigenVec = testResult["pr.out&x"].AsNumericMatrix();
Console.ReadLine();
}
static void Main(string[] args)
{
const string directoryPath = @"C:\Users\Káťa\Desktop\Diplomka\mesh\meshes";
var matrix = loadFacesR(directoryPath);
REngine.SetEnvironmentVariables();
REngine engine = REngine.GetInstance();
NumericMatrix group1 = engine.CreateNumericMatrix(matrix);
//List<float>[] faceMatrix = new List<float>[] { };
//double[,] squere = new double[,] { { 13, -4, 2 },
// {-4 , 11 , -2}, {2 , -2 , 8 } };
//runR(squere);
//int[] i = new int[] {};
// pokus();
// var matrix = getDataMatrix(directoryPath);
// runR();
//REngine.SetEnvironmentVariables();
//REngine engine = REngine.GetInstance();
//// REngine requires explicit initialization.
//// You can set some parameters.
//engine.Initialize();
//NumericMatrix group1 = engine.CreateNumericMatrix(new double[,] {
// { 0, 0, 0, 0, 1 },
// { 0, 0, 0, 1, 1 },
// { 0, 0, 1, 1, 1 },
// { 0, 0, 0, 1, 1 },
// { 0, 0, 0, 0, 1 }
//});
//engine.SetSymbol("group1", group1);
//GenericVector testResult = engine.Evaluate("eigen(group1)").AsList();
//Console.WriteLine("P-value = {0}", group1);
// //.NET Framework array to R vector.
//NumericVector group1 = engine.CreateNumericVector(new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99 });
//engine.SetSymbol("group1", group1);
//// Direct parsing from R script.
//NumericVector group2 = engine.Evaluate("group2 <- c(29.89, 29.93, 29.72, 29.98, 30.02, 29.98)").AsNumeric();
//// Test difference of mean and get the P-value.
//GenericVector testResult = engine.Evaluate("t.test(group1, group2)").AsList();
//double p = testResult["p.value"].AsNumeric().First();
//Console.WriteLine("Group1: [{0}]", string.Join(", ", group1));
//Console.WriteLine("Group2: [{0}]", string.Join(", ", group2));
//Console.WriteLine("P-value = {0:0.000}", p);
//you should always dispose of the REngine properly.
// After disposing of the engine, you cannot reinitialize nor reuse it
// engine.Dispose();
Console.ReadLine();
}
public static SymbolicExpression CreateDiffTimeMatrix(this REngine engine, TimeSpan[,] data)
{
var numeric = data.FromTimeSpan();
var sexp = engine.CreateNumericMatrix(numeric);
return(sexp.AddDiffTimeAttributes());
}
public static SymbolicExpression CreatePosixctMatrix(this REngine engine, DateTime[,] data)
{
var numeric = data.ToTicks(out var tzone);
var sexp = engine.CreateNumericMatrix(numeric);
return(sexp.AddPosixctAttributes(tzone));
}
private void SetupDotNetToRConverters()
{
SetupDotNetToRConverter(typeof(void), p => null);
SetupDotNetToRConverter(typeof(string), p => engine.CreateCharacter((string)p));
SetupDotNetToRConverter(typeof(string[]), p => engine.CreateCharacterVector((string[])p));
SetupDotNetToRConverter(typeof(List <string>), p => engine.CreateCharacterVector((IEnumerable <string>)p));
SetupDotNetToRConverter(typeof(IList <string>), p => engine.CreateCharacterVector((IEnumerable <string>)p));
SetupDotNetToRConverter(typeof(ICollection <string>), p => engine.CreateCharacterVector((IEnumerable <string>)p));
SetupDotNetToRConverter(typeof(IEnumerable <string>), p => engine.CreateCharacterVector((IEnumerable <string>)p));
SetupDotNetToRConverter(typeof(string[, ]), p => engine.CreateCharacterMatrix((string[, ])p));
SetupDotNetToRConverter(typeof(int), p => engine.CreateInteger((int)p));
SetupDotNetToRConverter(typeof(int[]), p => engine.CreateIntegerVector((int[])p));
SetupDotNetToRConverter(typeof(List <int>), p => engine.CreateIntegerVector((IEnumerable <int>)p));
SetupDotNetToRConverter(typeof(IList <int>), p => engine.CreateIntegerVector((IEnumerable <int>)p));
SetupDotNetToRConverter(typeof(ICollection <int>), p => engine.CreateIntegerVector((IEnumerable <int>)p));
SetupDotNetToRConverter(typeof(IEnumerable <int>), p => engine.CreateIntegerVector((IEnumerable <int>)p));
SetupDotNetToRConverter(typeof(int[, ]), p => engine.CreateIntegerMatrix((int[, ])p));
SetupDotNetToRConverter(typeof(bool), p => engine.CreateLogical((bool)p));
SetupDotNetToRConverter(typeof(bool[]), p => engine.CreateLogicalVector((bool[])p));
SetupDotNetToRConverter(typeof(List <bool>), p => engine.CreateLogicalVector((IEnumerable <bool>)p));
SetupDotNetToRConverter(typeof(IList <bool>), p => engine.CreateLogicalVector((IEnumerable <bool>)p));
SetupDotNetToRConverter(typeof(ICollection <bool>), p => engine.CreateLogicalVector((IEnumerable <bool>)p));
SetupDotNetToRConverter(typeof(IEnumerable <bool>), p => engine.CreateLogicalVector((IEnumerable <bool>)p));
SetupDotNetToRConverter(typeof(bool[, ]), p => engine.CreateLogicalMatrix((bool[, ])p));
SetupDotNetToRConverter(typeof(double), p => engine.CreateNumeric((double)p));
SetupDotNetToRConverter(typeof(double[]), p => engine.CreateNumericVector((double[])p));
SetupDotNetToRConverter(typeof(List <double>), p => engine.CreateNumericVector((IEnumerable <double>)p));
SetupDotNetToRConverter(typeof(IList <double>), p => engine.CreateNumericVector((IEnumerable <double>)p));
SetupDotNetToRConverter(typeof(ICollection <double>), p => engine.CreateNumericVector((IEnumerable <double>)p));
SetupDotNetToRConverter(typeof(IEnumerable <double>), p => engine.CreateNumericVector((IEnumerable <double>)p));
SetupDotNetToRConverter(typeof(double[, ]), p => engine.CreateNumericMatrix((double[, ])p));
SetupDotNetToRConverter(typeof(DateTime), p => engine.CreatePosixct((DateTime)p));
SetupDotNetToRConverter(typeof(DateTime[]), p => engine.CreatePosixctVector((DateTime[])p));
SetupDotNetToRConverter(typeof(List <DateTime>), p => engine.CreatePosixctVector((IEnumerable <DateTime>)p));
SetupDotNetToRConverter(typeof(IList <DateTime>), p => engine.CreatePosixctVector((IEnumerable <DateTime>)p));
SetupDotNetToRConverter(typeof(ICollection <DateTime>), p => engine.CreatePosixctVector((IEnumerable <DateTime>)p));
SetupDotNetToRConverter(typeof(IEnumerable <DateTime>), p => engine.CreatePosixctVector((IEnumerable <DateTime>)p));
SetupDotNetToRConverter(typeof(DateTime[, ]), p => engine.CreatePosixctMatrix((DateTime[, ])p));
SetupDotNetToRConverter(typeof(TimeSpan), p => engine.CreateDiffTime((TimeSpan)p));
SetupDotNetToRConverter(typeof(TimeSpan[]), p => engine.CreateDiffTimeVector((TimeSpan[])p));
SetupDotNetToRConverter(typeof(List <TimeSpan>), p => engine.CreateDiffTimeVector((IEnumerable <TimeSpan>)p));
SetupDotNetToRConverter(typeof(IList <TimeSpan>), p => engine.CreateDiffTimeVector((IEnumerable <TimeSpan>)p));
SetupDotNetToRConverter(typeof(ICollection <TimeSpan>), p => engine.CreateDiffTimeVector((IEnumerable <TimeSpan>)p));
SetupDotNetToRConverter(typeof(IEnumerable <TimeSpan>), p => engine.CreateDiffTimeVector((IEnumerable <TimeSpan>)p));
SetupDotNetToRConverter(typeof(TimeSpan[, ]), p => engine.CreateDiffTimeMatrix((TimeSpan[, ])p));
}
private double[] BhattaDist(int intNClasses, int intNFeatures, IFeatureCursor pFCursor, int intEstIdx, int intVarIdx)
{
try
{
frmProgress pfrmProgress = new frmProgress();
if (intNFeatures > MaxFeaturesforPGB)
{
pfrmProgress.lblStatus.Text = "Calculate Bhattacharyya distance";
pfrmProgress.pgbProgress.Style = ProgressBarStyle.Blocks;
pfrmProgress.pgbProgress.Value = 0;
pfrmProgress.Show();
}
double[] Cs = new double[intNClasses + 1];
arrEst = new double[intNFeatures];
double[] arrVar = new double[intNFeatures];
double[,] arrResults = new double[intNFeatures, intNFeatures];
IFeature pFeature = pFCursor.NextFeature();
int k = 0;
while (pFeature != null)
{
arrEst[k] = Convert.ToDouble(pFeature.get_Value(intEstIdx));
arrVar[k] = Convert.ToDouble(pFeature.get_Value(intVarIdx));
k++;
pFeature = pFCursor.NextFeature();
}
for (int i = 0; i < intNFeatures; i++)
{
double dblsquaredVar1 = Math.Pow(arrVar[i], 2);
for (int j = 0; j < intNFeatures; j++)
{
double dblsquaredVar2 = Math.Pow(arrVar[j], 2);
double dblVarComp = Math.Log(0.25 * ((dblsquaredVar1 / dblsquaredVar2) + (dblsquaredVar2 / dblsquaredVar1) + 2));
double dblMeanComp = Math.Pow(arrEst[i] - arrEst[j], 2) / (dblsquaredVar1 + dblsquaredVar2);
arrResults[i, j] = 0.25 * (dblVarComp + dblMeanComp);
}
if (intNFeatures > MaxFeaturesforPGB)
{
pfrmProgress.pgbProgress.Value = (i * 100) / intNFeatures;
}
}
if (intNFeatures > MaxFeaturesforPGB)
{
pfrmProgress.pgbProgress.Value = 100;
pfrmProgress.lblStatus.Text = "Show Results";
}
NumericMatrix pBhattaDist = pEngine.CreateNumericMatrix(arrResults);
pEngine.SetSymbol("Bhatta.diss", pBhattaDist);
pEngine.Evaluate("library(cluster)");
pEngine.Evaluate("kmed.result <- pam(as.dist(Bhatta.diss), diss=TRUE, " + intNClasses.ToString() + ")");
NumericVector pClustering = pEngine.Evaluate("kmed.result$clustering").AsNumeric();
Cs[0] = arrEst.Min();
Cs[intNClasses] = arrEst.Max();
//double[] tempCs = new double[intNClasses + 1];
//tempCs[0] = Cs[0];
//tempCs[intNClasses] = Cs[intNClasses];
for (int i = 0; i < intNFeatures; i++)
{
for (int j = 1; j < intNClasses; j++)
{
if (pClustering[i] == j)
{
double tempClassMax = arrEst[i];
if (tempClassMax > Cs[j])
{
Cs[j] = tempClassMax;
}
}
}
}
pfrmProgress.Close();
return(Cs);
}
catch (Exception ex)
{
MessageBox.Show(this.Handle.ToString() + " Error:" + ex.Message);
return(null);
}
}
private void button3_Click(object sender, EventArgs e)
{
int FeatureNum = app.FeaName.GetLength(0);
int SampleNum = app.SamName.GetLength(0);
REngine.SetEnvironmentVariables();
REngine PCA = REngine.GetInstance();
PCA.Initialize();
NumericMatrix Freq = PCA.CreateNumericMatrix(app.CountMatrix);
PCA.SetSymbol("Freq", Freq);
CharacterVector SampleName = PCA.CreateCharacterVector(app.SamName);
CharacterVector FeatureName = PCA.CreateCharacterVector(app.FeaName);
PCA.SetSymbol("FeatureName", FeatureName);
PCA.SetSymbol("SampleName", SampleName);
PCA.Evaluate("library(stats)");
PCA.Evaluate("pr <- prcomp(t(Freq),cor = TRUE)");
PCA.Evaluate("score <- predict(pr)");
double[,] Count = PCA.GetSymbol("score").AsNumericMatrix().ToArray();
app.Score = new double[SampleNum, 2];
for (int i = 0; i < SampleNum; i++)
{
app.Score[i, 0] = Count[i, 0];
app.Score[i, 1] = Count[i, 1];
}
if (this.radioButton1.Checked)
{
PCA.Evaluate("windows()");
PCA.Evaluate("plot(score[,1:2],main=\"PCA\", type=\"p\")");
}
else
{
if ((app.cluster == null) || (app.cluster.Length != SampleNum))
{
MessageBox.Show("Sample number in input data is not equal to that in cluster information!!", "Warning!!!", MessageBoxButtons.OK);
}
else
{
IntegerVector cluster = PCA.CreateIntegerVector(app.cluster);
PCA.SetSymbol("cluster", cluster);
PCA.Evaluate("clusterNum <- max(cluster)");
PCA.Evaluate("clustermin <- min(cluster)");
app.clusterNum = (int)PCA.GetSymbol("clusterNum").AsNumeric().First();
int clustermin = (int)PCA.GetSymbol("clustermin").AsNumeric().First();
if (app.clusterNum > 10)
{
MessageBox.Show("Too many clusters!!", "WARNING!");
}
else if (clustermin < 0)
{
MessageBox.Show("Illegal cluster number!!!", "WARNING!");
}
else
{
PCA_whole_Output plot = new PCA_whole_Output();
plot.MdiParent = this.MdiParent;
plot.Show();
}
}
}
this.Close();
}
/// <summary>
/// Does Arian's PCA-ANOVA idea.
/// </summary>
public double PcaAnova(IntensityMatrix source, Peak peak, Core core, List <GroupInfo> types, List <int> replicates)
{
Range times = GetOverlappingTimeRange(core, source, types);
// Create a matrix thusly:
// Control Replicate 1: <day1> <day2> <day3> ...
// Control Replicate 2: <day1> <day2> <day3> ...
// Control Replicate 3: <day1> <day2> <day3> ...
// Drought Replicate 1: <day1> <day2> <day3> ...
// Drought Replicate 2: <day1> <day2> <day3> ...
// ...
// Create and clear the matrix
int rowCount = types.Count * replicates.Count;
int colCount = times.Count;
double[,] matrix = new double[rowCount, colCount];
for (int r = 0; r < rowCount; r++)
{
for (int c = 0; c < colCount; c++)
{
matrix[r, c] = double.NaN;
}
}
// Create the group vector
double[] groups = new double[rowCount];
for (int r = 0; r < rowCount; r++)
{
groups[r] = types[r / replicates.Count].Order;
}
IReadOnlyList <double> raw = source.Find(peak).Values; // TODO: Dirty
// Fill out the values we know
for (int i = 0; i < core.Observations.Count; i++)
{
ObservationInfo o = core.Observations[i];
int typeIndex = types.IndexOf(o.Group);
int repIndex = replicates.IndexOf(o.Rep);
if (times.Contains(o.Time) && typeIndex != -1 && repIndex != -1)
{
int timeIndex = o.Time - times.Min;
int row = typeIndex * replicates.Count + repIndex;
UiControls.Assert(double.IsNaN(matrix[row, timeIndex]), "Duplicate day/time/rep observations in dataset are not allowed.");
matrix[row, timeIndex] = raw[i];
}
}
// Guess missing values
for (int r = 0; r < rowCount; r++)
{
for (int c = 0; c < colCount; c++)
{
if (double.IsNaN(matrix[r, c]))
{
// Missing values - average other values for this point
int repIndex = r % replicates.Count;
int typeStart = r - repIndex;
double total = 0;
int count = 0;
for (int rep = 0; rep < replicates.Count; rep++)
{
int newRow = typeStart + rep;
if (!double.IsNaN(matrix[newRow, c]))
{
total += matrix[newRow, c];
count += 1;
}
}
matrix[r, c] = total / count;
}
}
}
// Now do that R stuff...
var rMatrix = _r.CreateNumericMatrix(matrix);
var rVector = _r.CreateNumericVector(groups);
_r.SetSymbol("a", rMatrix);
_r.SetSymbol("g", rVector);
//R.Evaluate("write.csv(a, file = \"E:/MJR/Project/05. PEAS/AbstressData/Leaf/Positive/CCor/LP1131.cs.csv\")");
try
{
double result = _r.Evaluate(
@"p = prcomp(a)
f = data.frame(y = p$x[,1], group = factor(g))
fit = lm(y ~ group, f)
an = anova(fit)
pval = an$""Pr(>F)""[1]").AsNumeric()[0];
return(result);
}
catch (Exception ex)
{
throw new Exception("Something went wrong calculating PCA-ANOVA statistics. See inner exception for details. (Note that this error can occur if only 1 replicate is specified and PCA-ANOVA is calculated with missing values - make sure the replicates are specified correctly.)", ex);
}
}
private void button4_Click(object sender, EventArgs e)
{
int FeatureNum = app.FeaName.GetLength(0);
int SampleNum = app.SamName.GetLength(0);
List <double> prob = new List <double>();
List <double> stat = new List <double>();
List <double> pvalue = new List <double>();
double[] bonferroni = new double[FeatureNum];
double[] fdr = new double[FeatureNum];
//int length;
int NAnum = 0;
REngine.SetEnvironmentVariables();
REngine MSS = REngine.GetInstance();
MSS.Initialize();
NumericMatrix Freq = MSS.CreateNumericMatrix(app.FreqMatrix);
MSS.SetSymbol("Freq", Freq);
NumericMatrix Count = MSS.CreateNumericMatrix(app.CountMatrix);
MSS.SetSymbol("Count", Count);
CharacterVector SampleName = MSS.CreateCharacterVector(app.SamName);
CharacterVector FeatureName = MSS.CreateCharacterVector(app.FeaName);
MSS.SetSymbol("FeatureName", FeatureName);
MSS.SetSymbol("SampleName", SampleName);
List <string> SampleNameFreq = new List <string>();
for (int i = 0; i < SampleNum; i++)
{
SampleNameFreq.Add(SampleName[i] + "Freq");
}
IntegerVector RFeaNum = MSS.CreateInteger(FeatureNum);
NumericVector Ralpha = MSS.CreateNumeric(double.Parse(this.textBox1.Text.ToString()));
MSS.SetSymbol("n", RFeaNum);
MSS.SetSymbol("alpha", Ralpha);
List <List <double> > Freqtemp = new List <List <double> >();
List <double> FreqSum = new List <double>();
MSS.Evaluate("leastnum = ceiling ( qnorm(alpha/2)^2 )");
double leastnum = MSS.Evaluate("leastnum").AsNumeric().First();
if (this.comboBox1.SelectedIndex == 0)
{
for (int i = 0; i < FeatureNum; i++)
{
pvalue.Add(100);
for (int j = 0; j < SampleNum - 1; j++)
{
for (int k = j + 1; k < SampleNum; k++)
{
double probtemp = (app.CountMatrix[i, j] + app.CountMatrix[i, k]) / (app.SampleTotal[j] + app.SampleTotal[k]);
double stattemp = (app.FreqMatrix[i, j] - app.FreqMatrix[i, k]) / Math.Sqrt(probtemp * (1 - probtemp) * (1 / app.SampleTotal[j] + 1 / app.SampleTotal[k]));
if (double.IsNaN(stattemp))
{
stattemp = 0;
}
NumericVector Rstat = MSS.CreateNumeric(stattemp);
MSS.SetSymbol("stat", Rstat);
MSS.Evaluate("p.value <- 2*(pnorm(-abs(stat)))");
double pvaluetemp;
if ((this.comboBox2.SelectedIndex == 1) && (app.CountMatrix[i, j] < leastnum) && (app.CountMatrix[i, k] < leastnum))
{
pvaluetemp = 100;
}
else
{
pvaluetemp = MSS.GetSymbol("p.value").AsNumeric().First();
}
if (pvaluetemp != 100)
{
pvalue[i] = Math.Min((double)pvalue[i], (double)pvaluetemp);
}
}
}
}
NumericVector Rpvalue = MSS.CreateNumericVector(pvalue);
MSS.SetSymbol("p.value", Rpvalue);
MSS.Evaluate("NAnum = length(p.value[p.value == 100])");
NAnum = Convert.ToInt32(MSS.GetSymbol("NAnum").AsNumeric().First());
MSS.Evaluate("p.value[p.value == 100] = NA");
MSS.Evaluate("bonferroni.p <- p.adjust(p.value,\"bonferroni\")");
MSS.Evaluate("bonferroni.p[which(bonferroni.p == NA)] = 100");
MSS.Evaluate("fdr.p <- p.adjust(p.value,\"fdr\")");
MSS.Evaluate("fdr.p[which(fdr.p == NA)] = 100");
for (int i = 0; i < FeatureNum; i++)
{
bonferroni[i] = MSS.GetSymbol("bonferroni.p").AsNumeric()[i];
fdr[i] = MSS.GetSymbol("fdr.p").AsNumeric()[i];
}
}
else if (this.comboBox1.SelectedIndex == 1)
{
for (int i = 0; i < FeatureNum; i++)
{
pvalue.Add(100);
}
for (int j = 0; j < SampleNum - 1; j++)
{
for (int k = 1; k < SampleNum; k++)
{
double Sum1 = 0;
double Sum2 = 0;
for (int i = 0; i < FeatureNum; i++)
{
Sum1 = Sum1 + app.CountMatrix[i, j];
Sum2 = Sum2 + app.CountMatrix[i, k];
}
for (int i = 0; i < FeatureNum; i++)
{
NumericVector n11 = MSS.CreateNumeric(app.CountMatrix[i, j]);
NumericVector n21 = MSS.CreateNumeric(app.CountMatrix[i, k]);
NumericVector n12 = MSS.CreateNumeric(Sum1 - app.CountMatrix[i, j]);
NumericVector n22 = MSS.CreateNumeric(Sum2 - app.CountMatrix[i, k]);
MSS.SetSymbol("n11", n11);
MSS.SetSymbol("n12", n12);
MSS.SetSymbol("n21", n21);
MSS.SetSymbol("n22", n22);
MSS.Evaluate("compare <- matrix(c(n11,n12,n21,n22),nr=2)");
MSS.Evaluate("p.value <- fisher.test(compare)$p.value");
double pvaluetemp = MSS.GetSymbol("p.value").AsNumeric().First();
pvalue[i] = Math.Min((double)pvalue[i], (double)pvaluetemp);
}
}
}
MSS.Evaluate("bonferroni.p <- p.adjust(p.value,\"bonferroni\")");
MSS.Evaluate("fdr.p <- p.adjust(p.value,\"fdr\")");
for (int i = 0; i < FeatureNum; i++)
{
bonferroni[i] = MSS.GetSymbol("bonferroni.p").AsNumeric()[i];
fdr[i] = MSS.GetSymbol("fdr.p").AsNumeric()[i];
}
}
List <string> Annotation = new List <string>();
if (this.checkBox1.Checked)
{
if (this.radioButton2.Checked)
{
string strConnCOG;
strConnCOG = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/COG.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnCOG = new OleDbConnection(strConnCOG);
OleConnCOG.Open();
String sqlCOG = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelCOG = new OleDbDataAdapter(sqlCOG, OleConnCOG);
app.OleDsExcleCOG = new DataSet();
OleDaExcelCOG.Fill(app.OleDsExcleCOG, "Sheet1");
OleConnCOG.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExcleCOG.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExcleCOG.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExcleCOG.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
else if (this.radioButton1.Checked)
{
string strConnPFAM;
strConnPFAM = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/PFAM.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnPFAM = new OleDbConnection(strConnPFAM);
OleConnPFAM.Open();
String sqlPFAM = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelPFAM = new OleDbDataAdapter(sqlPFAM, OleConnPFAM);
app.OleDsExclePFAM = new DataSet();
OleDaExcelPFAM.Fill(app.OleDsExclePFAM, "Sheet1");
OleConnPFAM.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExclePFAM.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExclePFAM.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExclePFAM.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
}
DataTable dt = new DataTable();
dt.Columns.Add("Feature", typeof(string));
for (int i = 0; i < SampleNum; i++)
{
dt.Columns.Add(app.SamName[i], typeof(double));
}
dt.Columns.Add("p.value", typeof(double));
dt.Columns.Add("bonferroni.p", typeof(double));
dt.Columns.Add("fdr.p", typeof(double));
dt.Columns.Add("Annotation", typeof(string));
for (int i = 0; i < SampleNum; i++)
{
dt.Columns.Add(SampleNameFreq[i], typeof(double));
}
for (int i = 0; i < FeatureNum; i++)
{
DataRow dr = dt.NewRow();
dr[0] = FeatureName[i];
for (int j = 1; j < SampleNum + 1; j++)
{
dr[j] = app.CountMatrix[i, j - 1];
}
if (pvalue[i] == 100)
{
dr[SampleNum + 1] = DBNull.Value;
dr[SampleNum + 2] = DBNull.Value;
dr[SampleNum + 3] = DBNull.Value;
}
else
{
dr[SampleNum + 1] = pvalue[i];
dr[SampleNum + 2] = bonferroni[i];
dr[SampleNum + 3] = fdr[i];
}
if (this.checkBox1.Checked)
{
dr[SampleNum + 4] = Annotation[i];
}
else
{
dr[SampleNum + 4] = null;
}
for (int j = 0; j < SampleNum; j++)
{
dr[j + SampleNum + 5] = app.FreqMatrix[i, j];
}
dt.Rows.Add(dr);
}
DataTable dtCopy = dt.Copy();
DataTable dttemp = dt.Copy();
dttemp.Clear();
DataView dv = dt.DefaultView;
dv.Sort = "p.value";
dtCopy = dv.ToTable();
for (int i = 0; i < NAnum; i++)
{
DataRow row = dtCopy.Rows[i];
dttemp.Rows.Add(row.ItemArray);
}
for (int i = 0; i < NAnum; i++)
{
dtCopy.Rows.RemoveAt(0);
}
dtCopy.Merge(dttemp);
Microsoft.Office.Interop.Excel.Application xlApp = new Microsoft.Office.Interop.Excel.Application();
System.Globalization.CultureInfo CurrentCI = System.Threading.Thread.CurrentThread.CurrentCulture;
System.Threading.Thread.CurrentThread.CurrentCulture = new System.Globalization.CultureInfo("en-US");
Microsoft.Office.Interop.Excel.Workbooks workbooks = xlApp.Workbooks;
Microsoft.Office.Interop.Excel.Workbook workbook = workbooks.Add(Microsoft.Office.Interop.Excel.XlWBATemplate.xlWBATWorksheet);
Microsoft.Office.Interop.Excel.Worksheet worksheet = (Microsoft.Office.Interop.Excel.Worksheet)workbook.Worksheets[1];
Microsoft.Office.Interop.Excel.Range range;
long totalCount = dtCopy.Rows.Count;
long rowRead = 0;
float percent = 0;
for (int i = 0; i < dtCopy.Columns.Count - SampleNum; i++)
{
worksheet.Cells[1, i + 1] = dtCopy.Columns[i].ColumnName;
range = (Microsoft.Office.Interop.Excel.Range)worksheet.Cells[1, i + 1];
range.Interior.ColorIndex = 15;
range.Font.Bold = true;
}
for (int r = 0; r < dtCopy.Rows.Count; r++)
{
for (int i = 0; i < dtCopy.Columns.Count - SampleNum; i++)
{
worksheet.Cells[r + 2, i + 1] = dtCopy.Rows[r][i].ToString();
}
rowRead++;
percent = ((float)(100 * rowRead)) / totalCount;
}
xlApp.Visible = true;
int pnum = 0;
for (int i = 0; i < FeatureNum; i++)
{
try
{
if (double.Parse(dtCopy.Rows[i][SampleNum].ToString()) < double.Parse(this.textBox1.Text.ToString()))
{
pnum++;
}
}
catch
{ }
}
double[,] df = new double[Math.Min(10, FeatureNum), SampleNum];
for (int i = 0; i < Math.Min(10, FeatureNum); i++)
{
for (int j = 0; j < SampleNum; j++)
{
df[i, j] = double.Parse(dtCopy.Rows[i][SampleNum + 5 + j].ToString());
}
}
if (this.checkBox2.Checked)
{
string[] rownamesdf = new string[Math.Min(10, FeatureNum)];
for (int i = 0; i < Math.Min(10, FeatureNum); i++)
{
rownamesdf[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownamesdf = MSS.CreateCharacterVector(rownamesdf);
MSS.SetSymbol("Rownamedf", Rrownamesdf);
NumericMatrix Rdf = MSS.CreateNumericMatrix(df);
MSS.SetSymbol("Freqdf", Rdf);
NumericVector RRow = MSS.CreateNumeric(Math.Min(10, FeatureNum));
MSS.SetSymbol("selrow", RRow);
MSS.Evaluate("Freqdf <- as.data.frame(Freqdf)");
MSS.Evaluate("rownames(Freqdf) <- Rownamedf");
MSS.Evaluate("colnames(Freqdf) <- SampleName");
MSS.Evaluate("colournum <- rainbow(dim(Freqdf)[2])");
MSS.Evaluate("plotdata <- t(Freqdf)");
MSS.Evaluate("windows()");
MSS.Evaluate("barplot(plotdata,main=\"features with top varition\",ylab=\"Freq\",beside=TRUE,horiz=FALSE, cex.names=0.6,col=colournum)");
MSS.Evaluate("legend(\"topright\",SampleName,fill=colournum)");
}
if (pnum > 0)
{
double[,] dfall = new double[pnum, SampleNum];
for (int i = 0; i < pnum; i++)
{
for (int j = 0; j < SampleNum; j++)
{
dfall[i, j] = double.Parse(dtCopy.Rows[i][SampleNum + 5 + j].ToString());
}
}
string[] rownamesall = new string[pnum];
for (int i = 0; i < pnum; i++)
{
rownamesall[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownamesall = MSS.CreateCharacterVector(rownamesall);
MSS.SetSymbol("Rownameall", Rrownamesall);
NumericMatrix Rdfall = MSS.CreateNumericMatrix(dfall);
MSS.SetSymbol("Freqdfall", Rdfall);
NumericVector RRowall = MSS.CreateNumeric(pnum);
MSS.SetSymbol("selrowall", RRowall);
MSS.Evaluate("Freqdfall <- as.data.frame(Freqdfall)");
MSS.Evaluate("rownames(Freqdfall) <- Rownameall");
MSS.Evaluate("colnames(Freqdfall) <- SampleName");
MSS.Evaluate("distance <- as.dist(1-abs(cor(Freqdfall)))");
if (this.checkBox3.Checked)
{
MSS.Evaluate("fit <- cmdscale(distance, eig=TRUE, k=2)");
MSS.Evaluate("x <- fit$points[,1]");
MSS.Evaluate("y <- fit$points[,2]");
MSS.Evaluate("minx <- min(x)");
MSS.Evaluate("miny <- min(y)");
MSS.Evaluate("maxx <- max(x)");
MSS.Evaluate("maxy <- max(y)");
MSS.Evaluate("randx <- maxx - minx");
MSS.Evaluate("randy <- maxy - miny");
MSS.Evaluate("llimx <- minx - randx/10");
MSS.Evaluate("hlimx <- maxx + randx/3");
MSS.Evaluate("llimy <- miny - randy/10");
MSS.Evaluate("hlimy <- maxy + randy/3");
MSS.Evaluate("windows()");
MSS.Evaluate("plot(x,y,xlab=\"Coordinate 1\",ylab=\"Coordinate 2\",main=\"MDS\", pch=c(0,1,2,5,6), col=rainbow(7), type=\"p\",xlim = c(llimx,hlimx), ylim = c(llimy,hlimy))");
if (this.comboBox3.SelectedIndex == 0)
{
MSS.Evaluate("legend(\"topright\",colnames(Freqdfall),pch=c(0,1,2,5,6),col=rainbow(7),cex = 0.8)");
}
else if (this.comboBox3.SelectedIndex == 1)
{
MSS.Evaluate("text(x,y,labels=SampleName,pos=4)");
}
}
if (this.checkBox4.Checked)
{
MSS.Evaluate("windows()");
MSS.Evaluate("plot(hclust(distance),main =\"Samples Clust\")");
}
}
else
{
MessageBox.Show("No differntially abundant features!!");
}
if (this.checkBox5.Checked)
{
int Rownum = 0;
for (int i = 0; i < FeatureNum; i++)
{
double tempSum = 0;
double tempMean = 0;
for (int j = 0; j < SampleNum; j++)
{
tempSum = tempSum + app.FreqMatrix[i, j];
}
tempMean = tempSum / (SampleNum);
if (tempSum > 0)
{
FreqSum.Add(tempSum);
List <double> tempRow = new List <double>();
for (int j = 0; j < SampleNum; j++)
{
tempRow.Add(app.FreqMatrix[i, j] / tempMean);
}
Freqtemp.Add(tempRow);
Rownum = Rownum + 1;
}
}
for (int i = 0; i < Rownum; i++)
{
for (int j = 0; j < SampleNum; j++)
{
Freqtemp[i][j] = Math.Log(Freqtemp[i][j], 2);
if (Freqtemp[i][j] > 1)
{
Freqtemp[i][j] = 1;
}
else if (Freqtemp[i][j] < -1)
{
Freqtemp[i][j] = -1;
}
}
}
double[,] dfhm = new double[Math.Min(500, Rownum), SampleNum];
for (int i = 0; i < Math.Min(500, Rownum); i++)
{
for (int j = 0; j < SampleNum; j++)
{
dfhm[i, j] = double.Parse(Freqtemp[i][j].ToString());
}
}
string[] rownameshm = new string[Math.Min(500, Rownum)];
for (int i = 0; i < Math.Min(500, Rownum); i++)
{
rownameshm[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownameshm = MSS.CreateCharacterVector(rownameshm);
MSS.SetSymbol("Rownamehm", Rrownameshm);
NumericMatrix Rdfhm = MSS.CreateNumericMatrix(dfhm);
MSS.SetSymbol("Freqdfhm", Rdfhm);
NumericVector RRowhm = MSS.CreateNumeric(Math.Min(500, Rownum));
MSS.SetSymbol("plotnum", RRowhm);
MSS.Evaluate("Freqdfhm <- as.data.frame(Freqdfhm)");
MSS.Evaluate("rownames(Freqdfhm) <- Rownamehm");
MSS.Evaluate("colnames(Freqdfhm) <- SampleName");
MSS.Evaluate("Freqdfhm <- as.matrix(Freqdfhm)");
MSS.Evaluate("library(pheatmap)");
MSS.Evaluate("windows()");
if (this.checkBox6.Checked)
{
if (this.checkBox7.Checked)
{
MSS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=T,cluster_rows=T)");
}
else
{
MSS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=F,cluster_rows=T)");
}
}
else
{
if (this.checkBox7.Checked)
{
MSS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=T,cluster_rows=F)");
}
else
{
MSS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=F,cluster_rows=F)");
}
}
}
this.Close();
}
static void runR(double[,] square)
{
REngine.SetEnvironmentVariables();
REngine engine = REngine.GetInstance();
// REngine requires explicit initialization.
// You can set some parameters.
engine.Initialize();
NumericMatrix group1 = engine.CreateNumericMatrix(square);
//NumericMatrix group1 = engine.CreateNumericMatrix(new double[,] {
// { 0, 0, 0, 0, 1 },
// { 0, 0, 0, 1, 1 },
// { 0, 0, 1, 1, 1 },
// { 0, 0, 0, 1, 1 },
// { 0, 0, 0, 0, 1 }
//});
engine.SetSymbol("group1", group1);
GenericVector testResult = engine.Evaluate("eigen(group1)").AsList();
Console.WriteLine(testResult);
var hokus = testResult["values"].AsIntegerMatrix();
var eigenValues = testResult["values"].AsNumeric().ToArray();
var vectorOfVectors = testResult["vectors"].AsNumeric();
int indexNextEigenVector = 0;
// double[] eigenVectors
//for (int i = 0; i < eigenVectors.Length; i++)
//{
// if ()
//}
//Array.Sort(eigenValues, eigenVectors);
//_allStatInfo.Sort(new Comparison<StatInfo>((x, y) => DateTime.Compare(x.date, y.date)));
double sum = eigenValues.Sum();
double[] kumulative = new double[eigenValues.Length];
//
bool firstOccurrence = false;
int thresholdIndex = -1;
double thresholdValue = 0.6;
for (int i = 0; i < eigenValues.Length; i++)
{
double normalize = eigenValues[i] / sum;
if (i == 0)
{
kumulative[i] = normalize;
}
else
{
kumulative[i] = normalize + kumulative[i - 1];
}
if (kumulative[i] >= thresholdValue && firstOccurrence == false)
{
firstOccurrence = true;
thresholdIndex = i;
}
}
// engine.Evaluate("plot(group2, type = 'l', col = 'red', lwd = 10,ylab='eigen values')");
var normalizeEigenVaules = engine.CreateNumericVector(kumulative);
engine.SetSymbol("v", normalizeEigenVaules);
// ylim = c(0, 1),type = 'b')
engine.Evaluate("plot(v, type = 'l', col = 'red', lwd = 10,ylab='eigen values',type = 'b')");
var hokz = testResult["values"].AsList().ToArray();
Console.WriteLine("P-value = {0}", hokus[1, 0]);
// return eigenValues;
}
private void btnGenerate_Click(object sender, EventArgs e)
{
try
{
frmProgress pfrmProgress = new frmProgress();
pfrmProgress.lblStatus.Text = "Processing:";
pfrmProgress.Show();
int intProgress = 0;
REngine pEngine = mForm.pEngine;
//Declare constants
double dblDistError = Convert.ToDouble(txtDistance.Text);
double dblDirection = Convert.ToDouble(txtDirections.Text);
int intNSimulation = Convert.ToInt32(txtNSimulation.Text);
string strX = cboX.Text;
string strY = cboY.Text;
mForm.strValue = cboFieldName.Text;
//Load Source Layer
string strLayerName = cboSourceLayer.Text;
int intLIndex = pSnippet.GetIndexNumberFromLayerName(pActiveView, strLayerName);
ILayer pLayer = mForm.axMapControl1.get_Layer(intLIndex);
IFeatureLayer pFLayer = pLayer as IFeatureLayer;
IFeatureClass pFClass = pFLayer.FeatureClass;
int nFeature = pFClass.FeatureCount(null);
IFeatureCursor pFCursor = pFLayer.Search(null, true);
IFeature pFeature = pFCursor.NextFeature();
//Load Target Layer
mForm.strTargetLayerName = cboTargetLayer.Text;
int intTargetIndex = pSnippet.GetIndexNumberFromLayerName(pActiveView, mForm.strTargetLayerName);
ILayer pTargetLayer = mForm.axMapControl1.get_Layer(intTargetIndex);
IFeatureLayer pFTargetLayer = pTargetLayer as IFeatureLayer;
IFeatureClass pFTargetClass = pFTargetLayer.FeatureClass;
int nTargetFeature = pFTargetClass.FeatureCount(null);
//Store X, Y and Value to array
double[] vecValue = new double[nFeature];
double[,] arrXY = new double[nFeature, 2];
int intXIdx = pFClass.FindField(strX);
int intYIdx = pFClass.FindField(strY);
int intVIdx = pFClass.FindField(mForm.strValue);
int i = 0;
while (pFeature != null)
{
vecValue[i] = Convert.ToDouble(pFeature.get_Value(intVIdx));
arrXY[i, 0] = Convert.ToDouble(pFeature.get_Value(intXIdx));
arrXY[i, 1] = Convert.ToDouble(pFeature.get_Value(intYIdx));
i++;
pFeature = pFCursor.NextFeature();
}
pFCursor.Flush();
////Plot command for R
//StringBuilder plotCommmand = new StringBuilder();
//Get the file path and name to create spatial weight matrix
string strNameR = pSnippet.FilePathinRfromLayer(pFTargetLayer);
if (strNameR == null)
{
return;
}
//Load Library and Data
pEngine.Evaluate("library(maptools);library(foreign);library(sp);library(circular);");
pEngine.Evaluate("ct.shp <- readShapePoly('" + strNameR + "')");
//Create Shapepointdataframe in R
NumericMatrix nmXY = pEngine.CreateNumericMatrix(arrXY);
pEngine.SetSymbol("xy.coord.old", nmXY);
NumericVector nvValue = pEngine.CreateNumericVector(vecValue);
pEngine.SetSymbol("LEAD", nvValue);
pEngine.Evaluate("v.bll <- as.data.frame(LEAD)");
pEngine.Evaluate("old.points <- SpatialPointsDataFrame(xy.coord.old, v.bll)");
string strOverMethod = "";
if (cboStat.Text == "Mean")
{
strOverMethod = ", fn=mean";
}
//Save Original value
pEngine.Evaluate("n.obs <- nrow(v.bll)");
pEngine.Evaluate("ori.value <- as.matrix(over(ct.shp, old.points" + strOverMethod + "))");
NumericVector nvOriValue = pEngine.Evaluate("ori.value").AsNumeric();
mForm.arrOrivalue = new double[nTargetFeature];
nvOriValue.CopyTo(mForm.arrOrivalue, nTargetFeature);
//Create Matrix to store simulation results
pEngine.Evaluate("results.matrix <- matrix(NA, nrow=nrow(ct.shp), ncol=" + intNSimulation.ToString() + ")");
//Simulation
for (int j = 1; j <= intNSimulation; j++)
{
//Progress bar
intProgress = j * 100 / intNSimulation;
pfrmProgress.pgbProgress.Value = intProgress;
//pfrmProgress.pgbProgress.Refresh();
pfrmProgress.lblStatus.Text = "Processing (" + intProgress.ToString() + "%)";
//pfrmProgress.lblStatus.Refresh();
pfrmProgress.Invalidate();
//Create Direction from vonmises distribution
pEngine.Evaluate("dir.new <- as.numeric(rvonmises(n.obs, mu=circular(" + txtDirections.Text + ", units='degrees'), kappa=" + txtConcetration.Text + "))");
//Add distance error
if (rbtRandom.Checked == true)
{
pEngine.Evaluate("dist.error <- runif(n.obs, 0, " + txtDistance.Text + ")");
}
else
{
pEngine.Evaluate("dist.error <- " + txtDistance.Text);
}
//Create New points and save to SpatialPointsDataFrame
pEngine.Evaluate("x.new <- xy.coord.old[,1] + cos(dir.new)*dist.error; y.new <- xy.coord.old[,2]+ sin(dir.new)*dist.error");
pEngine.Evaluate("xy.coord <- cbind(x.new, y.new)");
pEngine.Evaluate("new.points <- SpatialPointsDataFrame(xy.coord, v.bll)");
//Save results <- have to insert options for summary statistics, (only possilbe by Mean) 1/29/15
pEngine.Evaluate("results.matrix[," + j.ToString() + "] <- as.matrix(over(ct.shp, new.points" + strOverMethod + "))");
}
//Save Simulation results to Array
NumericMatrix nmResults = pEngine.Evaluate("results.matrix").AsNumericMatrix();
mForm.arrSimuResults = new double[nTargetFeature, intNSimulation];
nmResults.CopyTo(mForm.arrSimuResults, nTargetFeature, intNSimulation);
//Create matrix to save summary results(mean, var, mean/var)
pfrmProgress.lblStatus.Text = "Creating Summary";
pfrmProgress.Invalidate();
pEngine.Evaluate("sum.mat <- matrix(NA,nrow=nrow(ct.shp), ncol=6)");
//Calculate mean and variance of simulation
for (int j = 1; j <= nTargetFeature; j++)
{
pEngine.Evaluate("sum.mat[" + j.ToString() + ",1] <- mean(results.matrix[" + j.ToString() + ",])");
pEngine.Evaluate("sum.mat[" + j.ToString() + ",2] <- var(results.matrix[" + j.ToString() + ",])");
pEngine.Evaluate("sum.mat[" + j.ToString() + ",5] <- min(results.matrix[" + j.ToString() + ",])");
pEngine.Evaluate("sum.mat[" + j.ToString() + ",6] <- max(results.matrix[" + j.ToString() + ",])");
pEngine.Evaluate("diff.ori <- (results.matrix[" + j.ToString() + ",]- ori.value[" + j.ToString() + "])^2"); //Needs to be changed
pEngine.Evaluate("sum.mat[" + j.ToString() + ",4] <- sum(diff.ori)/" + intNSimulation.ToString());
}
//Calculate mean/variance
pEngine.Evaluate("sum.mat[,3] <- sum.mat[,2]/sum.mat[,1]");
//Save summary results to array
NumericMatrix nmSummary = pEngine.Evaluate("sum.mat").AsNumericMatrix();
arrSummary = new double[nTargetFeature, 6];
nmSummary.CopyTo(arrSummary, nTargetFeature, 6);
//Remove all memory
pEngine.Evaluate("rm(list=ls(all=TRUE))");
//Enable to the histogram identify tool for simulation results
if (mForm.arrSimuResults != null)
{
chkEnable.Enabled = true;
}
//Add Summary data to Target feature Class
//Create filed to store results
if (pFTargetClass.FindField("Ori_Mean") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Ori_Mean";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("Mean_val") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Mean_val";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("Var_val") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Var_val";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("Mean_Var") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Mean_Var";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("Var_Ori") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Var_Ori";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("MIN") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "MIN";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("MAX") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "MAX";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
//Save summary results to Target shapefile
int intOriMeanIdx = pFTargetClass.FindField("Ori_Mean");
int intMeanIdx = pFTargetClass.FindField("Mean_val");
int intVarIdx = pFTargetClass.FindField("Var_val");
int intMVIdx = pFTargetClass.FindField("Mean_Var");
int intVOIdx = pFTargetClass.FindField("Var_Ori");
int intMaxIdx = pFTargetClass.FindField("MAX");
int intMinIdx = pFTargetClass.FindField("MIN");
IFeatureCursor pTFUCursor = pFTargetClass.Update(null, false);
IFeature pTFeature = pTFUCursor.NextFeature();
i = 0;
while (pTFeature != null)
{
pTFeature.set_Value(intOriMeanIdx, mForm.arrOrivalue[i]);
pTFeature.set_Value(intMeanIdx, arrSummary[i, 0]);
pTFeature.set_Value(intVarIdx, arrSummary[i, 1]);
pTFeature.set_Value(intMVIdx, arrSummary[i, 2]);
pTFeature.set_Value(intVOIdx, arrSummary[i, 3]);
pTFeature.set_Value(intMinIdx, arrSummary[i, 4]);
pTFeature.set_Value(intMaxIdx, arrSummary[i, 5]);
pTFeature.Store();
i++;
pTFeature = pTFUCursor.NextFeature();
}
pfrmProgress.Close();
MessageBox.Show("Done");
}
catch (Exception ex)
{
MessageBox.Show(this.Handle.ToString() + " Error:" + ex.Message);
return;
}
}
private void button4_Click(object sender, EventArgs e)
{
int FeatureNum = app.FeaName.GetLength(0);
int SampleNum = app.SamName.GetLength(0);
List <double> prob = new List <double>();
List <double> stat = new List <double>();
List <double> pvalue = new List <double>();
double[] bonferroni = new double[FeatureNum];
double[] fdr = new double[FeatureNum];
int NAnum = 0;
if (SampleNum == 2)
{
REngine.SetEnvironmentVariables();
REngine TSS = REngine.GetInstance();
TSS.Initialize();
NumericMatrix Freq = TSS.CreateNumericMatrix(app.FreqMatrix);
TSS.SetSymbol("Freq", Freq);
NumericMatrix Count = TSS.CreateNumericMatrix(app.CountMatrix);
TSS.SetSymbol("Count", Count);
CharacterVector SampleName = TSS.CreateCharacterVector(app.SamName);
CharacterVector FeatureName = TSS.CreateCharacterVector(app.FeaName);
TSS.SetSymbol("FeatureName", FeatureName);
TSS.SetSymbol("SampleName", SampleName);
List <string> SampleNameFreq = new List <string>();
for (int i = 0; i < SampleNum; i++)
{
SampleNameFreq.Add(SampleName[i] + "Freq");
}
IntegerVector RFeaNum = TSS.CreateInteger(FeatureNum);
NumericVector Ralpha = TSS.CreateNumeric(double.Parse(this.textBox1.Text.ToString()));
TSS.SetSymbol("n", RFeaNum);
TSS.SetSymbol("alpha", Ralpha);
TSS.Evaluate("leastnum = ceiling ( qnorm(alpha/2)^2 )");
double leastnum = TSS.Evaluate("leastnum").AsNumeric().First();
if (this.comboBox1.SelectedIndex == 0)
{
for (int i = 0; i < FeatureNum; i++)
{
double temp = app.FreqMatrix[i, 0] - app.FreqMatrix[i, 1];
prob.Add(temp);
double P = (app.CountMatrix[i, 0] + app.CountMatrix[i, 1]) / (app.SampleTotal[0] + app.SampleTotal[1]);
double S = Math.Sqrt(P * (1 - P) * (1 / app.SampleTotal[0] + 1 / app.SampleTotal[1]));
double temp0 = prob[i] / S;
if (double.IsNaN(temp0))
{
temp0 = 0;
}
stat.Add(temp0);
}
for (int i = 0; i < FeatureNum; i++)
{
NumericVector Rstat = TSS.CreateNumeric(stat[i]);
TSS.SetSymbol("stat", Rstat);
TSS.Evaluate("p.value <- 2*(pnorm(-abs(stat)))");
if ((this.comboBox2.SelectedIndex == 1) && (app.CountMatrix[i, 0] < leastnum) && (app.CountMatrix[i, 1] < leastnum))
{
pvalue.Add(100);
}
else
{
pvalue.Add(TSS.GetSymbol("p.value").AsNumeric().First());
}
}
NumericVector Rpvalue = TSS.CreateNumericVector(pvalue);
TSS.SetSymbol("p.value", Rpvalue);
TSS.Evaluate("NAnum = length(p.value[p.value == 100])");
NAnum = Convert.ToInt32(TSS.GetSymbol("NAnum").AsNumeric().First());
TSS.Evaluate("p.value[p.value == 100] = NA");
TSS.Evaluate("bonferroni.p <- p.adjust(p.value,\"bonferroni\")");
TSS.Evaluate("bonferroni.p[which(bonferroni.p == NA)] = 100");
//double[] temp1 = (double[])hc1.GetSymbol("bonferroni.p");
TSS.Evaluate("fdr.p <- p.adjust(p.value,\"fdr\")");
TSS.Evaluate("fdr.p[which(fdr.p == NA)] = 100");
for (int i = 0; i < FeatureNum; i++)
{
bonferroni[i] = TSS.GetSymbol("bonferroni.p").AsNumeric()[i];
fdr[i] = TSS.GetSymbol("fdr.p").AsNumeric()[i];
}
}
else if (this.comboBox1.SelectedIndex == 1)
{
double Sum1 = 0;
double Sum2 = 0;
for (int i = 0; i < FeatureNum; i++)
{
Sum1 = Sum1 + app.CountMatrix[i, 0];
Sum2 = Sum2 + app.CountMatrix[i, 1];
}
for (int i = 0; i < FeatureNum; i++)
{
NumericVector n11 = TSS.CreateNumeric(app.CountMatrix[i, 0]);
NumericVector n21 = TSS.CreateNumeric(app.CountMatrix[i, 1]);
NumericVector n12 = TSS.CreateNumeric(Sum1 - app.CountMatrix[i, 0]);
NumericVector n22 = TSS.CreateNumeric(Sum2 - app.CountMatrix[i, 1]);
TSS.SetSymbol("n11", n11);
TSS.SetSymbol("n12", n12);
TSS.SetSymbol("n21", n21);
TSS.SetSymbol("n22", n22);
TSS.Evaluate("compare <- matrix(c(n11,n12,n21,n22),nr=2)");
TSS.Evaluate("p.value <- fisher.test(compare)$p.value");
pvalue.Add(TSS.GetSymbol("p.value").AsNumeric().First());
}
NumericVector Rpvalue = TSS.CreateNumericVector(pvalue);
TSS.SetSymbol("p.value", Rpvalue);
TSS.Evaluate("bonferroni.p <- p.adjust(p.value,\"bonferroni\")");
TSS.Evaluate("fdr.p <- p.adjust(p.value,\"fdr\")");
for (int i = 0; i < FeatureNum; i++)
{
bonferroni[i] = TSS.GetSymbol("bonferroni.p").AsNumeric()[i];
fdr[i] = TSS.GetSymbol("fdr.p").AsNumeric()[i];
}
}
List <string> Annotation = new List <string>();
if (this.checkBox1.Checked)
{
if (this.radioButton1.Checked)
{
string strConnCOG;
strConnCOG = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/COG.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnCOG = new OleDbConnection(strConnCOG);
OleConnCOG.Open();
String sqlCOG = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelCOG = new OleDbDataAdapter(sqlCOG, OleConnCOG);
app.OleDsExcleCOG = new DataSet();
OleDaExcelCOG.Fill(app.OleDsExcleCOG, "Sheet1");
OleConnCOG.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExcleCOG.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExcleCOG.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExcleCOG.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
else if (this.radioButton2.Checked)
{
string strConnPFAM;
strConnPFAM = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/PFAM.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnPFAM = new OleDbConnection(strConnPFAM);
OleConnPFAM.Open();
String sqlPFAM = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelPFAM = new OleDbDataAdapter(sqlPFAM, OleConnPFAM);
app.OleDsExclePFAM = new DataSet();
OleDaExcelPFAM.Fill(app.OleDsExclePFAM, "Sheet1");
OleConnPFAM.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExclePFAM.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExclePFAM.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExclePFAM.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
}
DataTable dt = new DataTable();
dt.Columns.Add("Feature", typeof(string));
for (int i = 0; i < SampleNum; i++)
{
dt.Columns.Add(app.SamName[i], typeof(double));
}
dt.Columns.Add("p.value", typeof(double));
dt.Columns.Add("bonferroni.p", typeof(double));
dt.Columns.Add("fdr.p", typeof(double));
dt.Columns.Add("Annotation", typeof(string));
for (int i = 0; i < SampleNum; i++)
{
dt.Columns.Add(SampleNameFreq[i], typeof(double));
}
for (int i = 0; i < FeatureNum; i++)
{
DataRow dr = dt.NewRow();
dr[0] = FeatureName[i];
for (int j = 1; j < SampleNum + 1; j++)
{
dr[j] = app.CountMatrix[i, j - 1];
}
if (pvalue[i] == 100)
{
dr[3] = DBNull.Value;
dr[4] = DBNull.Value;
dr[5] = DBNull.Value;
}
else
{
dr[3] = pvalue[i];
dr[4] = bonferroni[i];
dr[5] = fdr[i];
}
if (this.checkBox1.Checked)
{
dr[SampleNum + 4] = Annotation[i];
}
else
{
dr[SampleNum + 4] = null;
}
for (int j = 0; j < SampleNum; j++)
{
dr[j + SampleNum + 5] = app.FreqMatrix[i, j];
}
dt.Rows.Add(dr);
}
DataTable dtCopy = dt.Copy();
DataTable dttemp = dt.Copy();
dttemp.Clear();
DataView dv = dt.DefaultView;
dv.Sort = "p.value";
dtCopy = dv.ToTable();
for (int i = 0; i < NAnum; i++)
{
DataRow row = dtCopy.Rows[i];
dttemp.Rows.Add(row.ItemArray);
}
for (int i = 0; i < NAnum; i++)
{
dtCopy.Rows.RemoveAt(0);
}
Microsoft.Office.Interop.Excel.Application xlApp = new Microsoft.Office.Interop.Excel.Application();
System.Globalization.CultureInfo CurrentCI = System.Threading.Thread.CurrentThread.CurrentCulture;
System.Threading.Thread.CurrentThread.CurrentCulture = new System.Globalization.CultureInfo("en-US");
Microsoft.Office.Interop.Excel.Workbooks workbooks = xlApp.Workbooks;
Microsoft.Office.Interop.Excel.Workbook workbook = workbooks.Add(Microsoft.Office.Interop.Excel.XlWBATemplate.xlWBATWorksheet);
Microsoft.Office.Interop.Excel.Worksheet worksheet = (Microsoft.Office.Interop.Excel.Worksheet)workbook.Worksheets[1];
Microsoft.Office.Interop.Excel.Range range;
long totalCount = dtCopy.Rows.Count;
long rowRead = 0;
float percent = 0;
for (int i = 0; i < dtCopy.Columns.Count - SampleNum; i++)
{
worksheet.Cells[1, i + 1] = dtCopy.Columns[i].ColumnName;
range = (Microsoft.Office.Interop.Excel.Range)worksheet.Cells[1, i + 1];
range.Interior.ColorIndex = 15;
range.Font.Bold = true;
}
for (int r = 0; r < dtCopy.Rows.Count; r++)
{
for (int i = 0; i < dtCopy.Columns.Count - SampleNum; i++)
{
worksheet.Cells[r + 2, i + 1] = dtCopy.Rows[r][i].ToString();
}
rowRead++;
percent = ((float)(100 * rowRead)) / totalCount;
}
xlApp.Visible = true;
double[,] df = new double[Math.Min(10, FeatureNum), SampleNum];
for (int i = 0; i < Math.Min(10, FeatureNum); i++)
{
for (int j = 0; j < SampleNum; j++)
{
df[i, j] = double.Parse(dtCopy.Rows[i][SampleNum + 5 + j].ToString());
}
}
string[] rownames = new string[Math.Min(10, FeatureNum)];
for (int i = 0; i < Math.Min(10, FeatureNum); i++)
{
rownames[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownames = TSS.CreateCharacterVector(rownames);
TSS.SetSymbol("Rowname", Rrownames);
NumericMatrix Rdf = TSS.CreateNumericMatrix(df);
TSS.SetSymbol("Freqdf", Rdf);
NumericVector RRow = TSS.CreateNumeric(Math.Min(10, FeatureNum));
TSS.SetSymbol("selrow", RRow);
TSS.Evaluate("Freqdf <- as.data.frame(Freqdf)");
TSS.Evaluate("rownames(Freqdf) <- Rowname");
TSS.Evaluate("colnames(Freqdf) <- SampleName");
TSS.Evaluate("colournum <- rainbow(dim(Freqdf)[2])");
TSS.Evaluate("plotdata <- t(Freqdf)");
TSS.Evaluate("windows()");
TSS.Evaluate("barplot(plotdata,main=\"features with top varition\",ylab=\"Freq\",beside=TRUE,horiz=FALSE, cex.names=0.6,col=colournum)");
TSS.Evaluate("legend(\"topright\",SampleName,fill=colournum)");
}
else
{
MessageBox.Show("Sample number is more than two!!");
}
this.Close();
}
private void button4_Click(object sender, EventArgs e)
{
if (this.textBox3.Text == "")
{
MessageBox.Show("Please enter the sample number of Group I!", "WARNIMG");
Two_Groups_Ana TwoGroupsTest = new Two_Groups_Ana();
TwoGroupsTest.MdiParent = this.MdiParent;
TwoGroupsTest.Show();
this.Close();
}
else
{
int FeatureNum = app.FeaName.GetLength(0);
int SampleNum = app.SamName.GetLength(0);
List <double> prob = new List <double>();
List <double> stat = new List <double>();
List <double> pvalue = new List <double>();
double[] bonferroni = new double[FeatureNum];
double[] fdr = new double[FeatureNum];
int NAnum = 0;
REngine.SetEnvironmentVariables();
REngine TGS = REngine.GetInstance();
TGS.Initialize();
NumericMatrix Freq = TGS.CreateNumericMatrix(app.FreqMatrix);
TGS.SetSymbol("Freq", Freq);
NumericMatrix Count = TGS.CreateNumericMatrix(app.CountMatrix);
TGS.SetSymbol("Count", Count);
NumericVector RFeatureNum = TGS.CreateNumeric(FeatureNum);
NumericVector RSampleNum = TGS.CreateNumeric(SampleNum);
TGS.SetSymbol("FeatureNum", RFeatureNum);
TGS.SetSymbol("SampleNum", RSampleNum);
CharacterVector SampleName = TGS.CreateCharacterVector(app.SamName);
CharacterVector FeatureName = TGS.CreateCharacterVector(app.FeaName);
TGS.SetSymbol("FeatureName", FeatureName);
TGS.SetSymbol("SampleName", SampleName);
List <string> SampleNameFreq = new List <string>();
List <double?> OddRatio = new List <double?>();
List <double?> absOddRatio = new List <double?>();
List <List <double> > Freqtemp = new List <List <double> >();
List <double> FreqSum = new List <double>();
int Correct1 = 0;
int Correct2 = 0;
int method = 0;
int GroupNum = int.Parse(this.textBox3.Text.ToString());
NumericVector RGroupNum = TGS.CreateNumeric(GroupNum);
TGS.SetSymbol("Groupsep", RGroupNum);
for (int i = 0; i < SampleNum; i++)
{
SampleNameFreq.Add(SampleName[i] + "Freq");
}
if (this.comboBox1.SelectedIndex == 0)
{
int effNum1, effNum2;
TGS.Evaluate("FeatureSums1 <- rowSums(Count[,1:Groupsep])");
TGS.Evaluate("FeatureSums2 <- rowSums(Count[,Groupsep:SampleNum])");
TGS.Evaluate("effnum1 <- length(FeatureSums1[FeatureSums1 > Groupsep])");
TGS.Evaluate("effnum2 <- length(FeatureSums2[FeatureSums2 > (SampleNum - Groupsep)])");
effNum1 = Convert.ToInt32(TGS.GetSymbol("effnum1").AsNumeric().First());
effNum2 = Convert.ToInt32(TGS.GetSymbol("effnum2").AsNumeric().First());
for (int i = 0; i < FeatureNum; i++)
{
double rowsums1 = 0;
double rowsums2 = 0;
double[] rowsCount1 = new double[GroupNum];
for (int j = 0; j < GroupNum; j++)
{
rowsums1 = rowsums1 + app.CountMatrix[i, j];
rowsCount1[j] = app.CountMatrix[i, j];
}
if (rowsums1 > GroupNum)
{
NumericVector rowscount1 = TGS.CreateNumericVector(rowsCount1);
TGS.SetSymbol("rowcount1", rowscount1);
TGS.Evaluate("result1 <- ks.test(rowcount1,\"pnorm\",mean(rowcount1),sd(rowcount1))");
TGS.Evaluate("factor1 <- result1$p");
double prows1 = TGS.GetSymbol("factor1").AsNumeric().First();
if (prows1 < 0.05)
{
Correct1 = Correct1 + 1;
}
}
double[] rowsCount2 = new double[SampleNum - GroupNum];
for (int j = GroupNum; j < SampleNum; j++)
{
rowsums2 = rowsums2 + app.CountMatrix[i, j];
rowsCount2[j - GroupNum] = app.CountMatrix[i, j];
}
if (rowsums2 > (SampleNum - GroupNum))
{
NumericVector rowscount2 = TGS.CreateNumericVector(rowsCount2);
TGS.SetSymbol("rowcount2", rowscount2);
TGS.Evaluate("result2 <- ks.test(rowcount2,\"pnorm\",mean(rowcount2),sd(rowcount2))");
TGS.Evaluate("factor2 <- result2$p");
double prows2 = TGS.GetSymbol("factor2").AsNumeric().First();
if (prows2 < 0.05)
{
Correct2 = Correct2 + 1;
}
}
}
bool condition1 = (Correct1 >= effNum1 * 0.5) && (Correct2 >= effNum2 * 0.5);
bool condition2 = GroupNum == SampleNum - GroupNum;
if (condition1)
{
if (condition2)
{
method = 2;
}
else
{
method = 1;
}
}
else
{
if (condition2)
{
method = 4;
}
else
{
method = 3;
}
}
switch (method)
{
case 1:
MessageBox.Show("Statistical Method : t-test");
break;
case 2:
MessageBox.Show("Statistical Method : Pair t-test");
break;
case 3:
MessageBox.Show("Statistical Method : Mann-Whitney U test");
break;
case 4:
MessageBox.Show("Statistical Method : Wilcoxon sign-rank test");
break;
default:
break;
}
}
TGS.Evaluate("FreqMatrix <- as.data.frame(Freq)");
TGS.Evaluate("names(FreqMatrix) <- SampleName");
TGS.Evaluate("samp1_mean <- apply(FreqMatrix[,1:Groupsep],1,mean)");
TGS.Evaluate("samp2_mean <- apply(FreqMatrix[,(Groupsep+1):SampleNum],1,mean)");
TGS.Evaluate("samp1_sd <- apply(FreqMatrix[,1:Groupsep],1,sd)");
TGS.Evaluate("samp2_sd <- apply(FreqMatrix[,(Groupsep+1):SampleNum],1,sd)");
TGS.Evaluate("samp1_stat <- paste(samp1_mean,samp1_sd,sep=\"±\")");
TGS.Evaluate("samp2_stat <- paste(samp2_mean,samp2_sd,sep=\"±\")");
string[] s1_stat = (string[])TGS.GetSymbol("samp1_stat").AsCharacter().ToArray();
string[] s2_stat = (string[])TGS.GetSymbol("samp2_stat").AsCharacter().ToArray();
if (this.comboBox1.SelectedIndex != 6)
{
switch (this.comboBox1.SelectedIndex + method)
{
case 1:
for (int i = 1; i <= FeatureNum; i++)
{
TGS.SetSymbol("i", TGS.CreateNumeric(i));
TGS.Evaluate("group1_freq <- as.numeric(FreqMatrix[i,1:Groupsep])");
TGS.Evaluate("group2_freq <- as.numeric(FreqMatrix[i,(Groupsep+1):SampleNum])");
TGS.Evaluate("p.value <- t.test(group1_freq,group2_freq, paired=FALSE)$p.value");
pvalue.Add(TGS.GetSymbol("p.value").AsNumeric().First());
}
break;
case 2:
if (GroupNum != SampleNum - GroupNum)
{
MessageBox.Show("This statistical test must have same number samples in each category!", "WARNIMG");
break;
}
else
{
for (int i = 1; i <= FeatureNum; i++)
{
TGS.SetSymbol("i", TGS.CreateNumeric(i));
TGS.Evaluate("group1_freq <- as.numeric(FreqMatrix[i,1:Groupsep])");
TGS.Evaluate("group2_freq <- as.numeric(FreqMatrix[i,(Groupsep+1):SampleNum])");
TGS.Evaluate("p.value <- t.test(group1_freq,group2_freq, paired=TRUE)$p.value");
pvalue.Add(TGS.GetSymbol("p.value").AsNumeric().First());
}
break;
}
case 3:
for (int i = 1; i <= FeatureNum; i++)
{
TGS.SetSymbol("i", TGS.CreateNumeric(i));
TGS.Evaluate("group1_freq <- as.numeric(FreqMatrix[i,1:Groupsep])");
TGS.Evaluate("group2_freq <- as.numeric(FreqMatrix[i,(Groupsep+1):SampleNum])");
TGS.Evaluate("p.value <- wilcox.test(group1_freq,group2_freq,exact = FALSE)$p.value");
pvalue.Add(TGS.GetSymbol("p.value").AsNumeric().First());
}
break;
case 4:
if (GroupNum != SampleNum - GroupNum)
{
MessageBox.Show("This statistical test must have same number samples in each category!", "WARNIMG");
break;
}
else
{
for (int i = 1; i <= FeatureNum; i++)
{
TGS.SetSymbol("i", TGS.CreateNumeric(i));
TGS.Evaluate("group1_freq <- as.numeric(FreqMatrix[i,1:Groupsep])");
TGS.Evaluate("group2_freq <- as.numeric(FreqMatrix[i,(Groupsep+1):SampleNum])");
TGS.Evaluate("p.value <- wilcox.test(group1_freq,group2_freq,paired=TRUE,exact = FALSE)$p.value");
pvalue.Add(TGS.GetSymbol("p.value").AsNumeric().First());
}
break;
}
case 5:
double Sum1 = 0;
double Sum2 = 0;
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < GroupNum; j++)
{
Sum1 = Sum1 + app.CountMatrix[i, j];
}
for (int j = GroupNum; j < SampleNum; j++)
{
Sum2 = Sum2 + app.CountMatrix[i, j];
}
}
for (int i = 0; i < FeatureNum; i++)
{
double n11 = 0;
double n12 = 0;
double n21 = 0;
double n22 = 0;
for (int j = 0; j < GroupNum; j++)
{
n11 = n11 + app.CountMatrix[i, j];
}
for (int j = GroupNum; j < SampleNum; j++)
{
n21 = n21 + app.CountMatrix[i, j];
}
n12 = Sum1 - n11;
n22 = Sum2 - n21;
NumericVector Rn11 = TGS.CreateNumeric(n11);
NumericVector Rn21 = TGS.CreateNumeric(n21);
NumericVector Rn12 = TGS.CreateNumeric(n12);
NumericVector Rn22 = TGS.CreateNumeric(n22);
TGS.SetSymbol("n11", Rn11);
TGS.SetSymbol("n12", Rn12);
TGS.SetSymbol("n21", Rn21);
TGS.SetSymbol("n22", Rn22);
TGS.Evaluate("compare <- matrix(c(n11,n12,n21,n22),nr=2)");
TGS.Evaluate("p.value <- fisher.test(compare)$p.value");
pvalue.Add(TGS.GetSymbol("p.value").AsNumeric().First());
}
break;
default:
break;
}
NumericVector Rpvalue = TGS.CreateNumericVector(pvalue);
TGS.SetSymbol("p.value", Rpvalue);
TGS.Evaluate("NAnum = length(p.value[is.nan(p.value)])");
NAnum = Convert.ToInt32(TGS.GetSymbol("NAnum").AsNumeric().First());
TGS.Evaluate("bonferroni.p <- p.adjust(p.value,\"bonferroni\")");
TGS.Evaluate("fdr.p <- p.adjust(p.value,\"fdr\")");
for (int i = 0; i < FeatureNum; i++)
{
bonferroni[i] = TGS.GetSymbol("bonferroni.p").AsNumeric()[i];
fdr[i] = TGS.GetSymbol("fdr.p").AsNumeric()[i];
}
List <string> Annotation = new List <string>();
if (this.checkBox1.Checked)
{
if (this.radioButton1.Checked)
{
string strConnCOG;
strConnCOG = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/COG.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnCOG = new OleDbConnection(strConnCOG);
OleConnCOG.Open();
String sqlCOG = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelCOG = new OleDbDataAdapter(sqlCOG, OleConnCOG);
app.OleDsExcleCOG = new DataSet();
OleDaExcelCOG.Fill(app.OleDsExcleCOG, "Sheet1");
OleConnCOG.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExcleCOG.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExcleCOG.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExcleCOG.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
else if (this.radioButton2.Checked)
{
string strConnPFAM;
strConnPFAM = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/PFAM.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnPFAM = new OleDbConnection(strConnPFAM);
OleConnPFAM.Open();
String sqlPFAM = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelPFAM = new OleDbDataAdapter(sqlPFAM, OleConnPFAM);
app.OleDsExclePFAM = new DataSet();
OleDaExcelPFAM.Fill(app.OleDsExclePFAM, "Sheet1");
OleConnPFAM.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExclePFAM.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExclePFAM.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExclePFAM.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
}
DataTable dt = new DataTable();
dt.Columns.Add("Feature", typeof(string));
for (int i = 0; i < SampleNum; i++)
{
dt.Columns.Add(SampleName[i], typeof(double));;
}
dt.Columns.Add("group1", typeof(string));
dt.Columns.Add("group2", typeof(string));
dt.Columns.Add("p.value", typeof(double));
dt.Columns.Add("bonferroni.p", typeof(double));
dt.Columns.Add("fdr.p", typeof(double));
dt.Columns.Add("Annotation", typeof(string));
for (int i = 0; i < SampleNum; i++)
{
dt.Columns.Add(SampleNameFreq[i], typeof(double));
}
for (int i = 0; i < FeatureNum; i++)
{
DataRow dr = dt.NewRow();
dr[0] = FeatureName[i];
for (int j = 1; j <= SampleNum; j++)
{
dr[j] = app.CountMatrix[i, j - 1];
}
dr[SampleNum + 1] = s1_stat[i];
dr[SampleNum + 2] = s2_stat[i];
if (double.IsNaN(pvalue[i]))
{
dr[SampleNum + 3] = DBNull.Value;
dr[SampleNum + 4] = DBNull.Value;
dr[SampleNum + 5] = DBNull.Value;
}
else
{
dr[SampleNum + 3] = pvalue[i];
dr[SampleNum + 4] = bonferroni[i];
dr[SampleNum + 5] = fdr[i];
}
if (this.checkBox1.Checked)
{
dr[SampleNum + 6] = Annotation[i];
}
else
{
dr[SampleNum + 6] = null;
}
for (int j = 0; j < SampleNum; j++)
{
dr[j + SampleNum + 7] = app.FreqMatrix[i, j];
}
dt.Rows.Add(dr);
}
DataTable dtCopy = dt.Copy();
DataTable dttemp = dt.Copy();
dttemp.Clear();
DataView dv = dt.DefaultView;
dv.Sort = "p.value";
dtCopy = dv.ToTable();
for (int i = 0; i < NAnum; i++)
{
DataRow row = dtCopy.Rows[i];
dttemp.Rows.Add(row.ItemArray);
}
for (int i = 0; i < NAnum; i++)
{
dtCopy.Rows.RemoveAt(0);
}
Microsoft.Office.Interop.Excel.Application xlApp = new Microsoft.Office.Interop.Excel.Application();
System.Globalization.CultureInfo CurrentCI = System.Threading.Thread.CurrentThread.CurrentCulture;
System.Threading.Thread.CurrentThread.CurrentCulture = new System.Globalization.CultureInfo("en-US");
Microsoft.Office.Interop.Excel.Workbooks workbooks = xlApp.Workbooks;
Microsoft.Office.Interop.Excel.Workbook workbook = workbooks.Add(Microsoft.Office.Interop.Excel.XlWBATemplate.xlWBATWorksheet);
Microsoft.Office.Interop.Excel.Worksheet worksheet = (Microsoft.Office.Interop.Excel.Worksheet)workbook.Worksheets[1];
Microsoft.Office.Interop.Excel.Range range;
long totalCount = dtCopy.Rows.Count;
long rowRead = 0;
float percent = 0;
for (int i = 0; i < dtCopy.Columns.Count - SampleNum; i++)
{
worksheet.Cells[1, i + 1] = dtCopy.Columns[i].ColumnName;
range = (Microsoft.Office.Interop.Excel.Range)worksheet.Cells[1, i + 1];
range.Interior.ColorIndex = 15;
range.Font.Bold = true;
}
for (int r = 0; r < dtCopy.Rows.Count; r++)
{
for (int i = 0; i < dtCopy.Columns.Count - SampleNum; i++)
{
worksheet.Cells[r + 2, i + 1] = dtCopy.Rows[r][i].ToString();
}
rowRead++;
percent = ((float)(100 * rowRead)) / totalCount;
}
xlApp.Visible = true;
int pnum = 0;
for (int i = 0; i < FeatureNum; i++)
{
try
{
if (double.Parse(dtCopy.Rows[i][SampleNum + 3].ToString()) < double.Parse(this.textBox1.Text.ToString()))
{
pnum++;
}
}
catch
{ }
}
double[,] df = new double[Math.Min(10, FeatureNum), SampleNum];
for (int i = 0; i < Math.Min(10, FeatureNum); i++)
{
for (int j = 0; j < SampleNum; j++)
{
df[i, j] = double.Parse(dtCopy.Rows[i][SampleNum + 7 + j].ToString());
}
}
if (this.checkBox2.Checked)
{
string[] rownamesdf = new string[Math.Min(10, FeatureNum)];
for (int i = 0; i < Math.Min(10, FeatureNum); i++)
{
rownamesdf[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownamesdf = TGS.CreateCharacterVector(rownamesdf);
TGS.SetSymbol("Rownamedf", Rrownamesdf);
NumericMatrix Rdf = TGS.CreateNumericMatrix(df);
TGS.SetSymbol("Freqdf", Rdf);
NumericVector RRow = TGS.CreateNumeric(Math.Min(10, FeatureNum));
TGS.SetSymbol("selrow", RRow);
TGS.Evaluate("Freqdf <- as.data.frame(Freqdf)");
TGS.Evaluate("rownames(Freqdf) <- Rownamedf");
TGS.Evaluate("colnames(Freqdf) <- SampleName");
TGS.Evaluate("colournum <- rainbow(dim(Freqdf)[2])");
TGS.Evaluate("plotdata <- t(Freqdf)");
TGS.Evaluate("windows()");
TGS.Evaluate("barplot(plotdata,main=\"features with top varition\",ylab=\"Freq\",beside=TRUE,horiz=FALSE, cex.names=0.6,col=colournum)");
TGS.Evaluate("legend(\"topright\",SampleName,fill=colournum)");
}
if (pnum > 0)
{
double[,] dfall = new double[pnum, SampleNum];
for (int i = 0; i < pnum; i++)
{
for (int j = 0; j < SampleNum; j++)
{
dfall[i, j] = double.Parse(dtCopy.Rows[i][SampleNum + 7 + j].ToString());
}
}
string[] rownamesall = new string[pnum];
for (int i = 0; i < pnum; i++)
{
rownamesall[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownamesall = TGS.CreateCharacterVector(rownamesall);
TGS.SetSymbol("Rownameall", Rrownamesall);
NumericMatrix Rdfall = TGS.CreateNumericMatrix(dfall);
TGS.SetSymbol("Freqdfall", Rdfall);
NumericVector RRowall = TGS.CreateNumeric(pnum);
TGS.SetSymbol("selrowall", RRowall);
TGS.Evaluate("Freqdfall <- as.data.frame(Freqdfall)");
TGS.Evaluate("rownames(Freqdfall) <- Rownameall");
TGS.Evaluate("colnames(Freqdfall) <- SampleName");
TGS.Evaluate("distance <- as.dist(1-abs(cor(Freqdfall)))");
if (this.checkBox3.Checked)
{
TGS.Evaluate("fit <- cmdscale(distance, eig=TRUE, k=2)");
TGS.Evaluate("x <- fit$points[,1]");
TGS.Evaluate("y <- fit$points[,2]");
TGS.Evaluate("minx <- min(x)");
TGS.Evaluate("miny <- min(y)");
TGS.Evaluate("maxx <- max(x)");
TGS.Evaluate("maxy <- max(y)");
TGS.Evaluate("randx <- maxx - minx");
TGS.Evaluate("randy <- maxy - miny");
TGS.Evaluate("llimx <- minx - randx/10");
TGS.Evaluate("hlimx <- maxx + randx/3");
TGS.Evaluate("llimy <- miny - randy/10");
TGS.Evaluate("hlimy <- maxy + randy/3");
TGS.Evaluate("windows()");
TGS.Evaluate("plot(x,y,xlab=\"Coordinate 1\",ylab=\"Coordinate 2\",main=\"MDS\", pch=c(rep(0,Groupsep),rep(1,(length(SampleName)-Groupsep))),col=c(rep(\"red\",Groupsep),rep(\"green\",(length(SampleName)-Groupsep))), type=\"p\",xlim = c(llimx,hlimx), ylim = c(llimy,hlimy))");
if (this.comboBox3.SelectedIndex == 0)
{
TGS.Evaluate("legend(\"topright\",colnames(Freqdfall),pch=c(rep(0,Groupsep),rep(1,(length(SampleName)-Groupsep))),col=c(rep(\"red\",Groupsep),rep(\"green\",(length(SampleName)-Groupsep))),cex = 0.8)");
}
else if (this.comboBox3.SelectedIndex == 1)
{
TGS.Evaluate("text(x,y,labels=SampleName,pos=4)");
}
}
if (this.checkBox4.Checked)
{
TGS.Evaluate("windows()");
TGS.Evaluate("plot(hclust(distance),main =\"Samples Clust\")");
}
}
else
{
MessageBox.Show("No differntially abundant features!!");
}
if (this.checkBox5.Checked)
{
int Rownum = 0;
for (int i = 0; i < FeatureNum; i++)
{
double tempSum = 0;
double tempMean = 0;
for (int j = 0; j < SampleNum; j++)
{
tempSum = tempSum + app.FreqMatrix[i, j];
}
tempMean = tempSum / (SampleNum);
if (tempSum > 0)
{
FreqSum.Add(tempSum);
List <double> tempRow = new List <double>();
for (int j = 0; j < SampleNum; j++)
{
tempRow.Add(app.FreqMatrix[i, j] / tempMean);
}
Freqtemp.Add(tempRow);
Rownum = Rownum + 1;
}
}
for (int i = 0; i < Rownum; i++)
{
for (int j = 0; j < SampleNum; j++)
{
Freqtemp[i][j] = Math.Log(Freqtemp[i][j], 2);
if (Freqtemp[i][j] > 1)
{
Freqtemp[i][j] = 1;
}
else if (Freqtemp[i][j] < -1)
{
Freqtemp[i][j] = -1;
}
}
}
double[,] dfhm = new double[Math.Min(500, Rownum), SampleNum];
for (int i = 0; i < Math.Min(500, Rownum); i++)
{
for (int j = 0; j < SampleNum; j++)
{
dfhm[i, j] = double.Parse(Freqtemp[i][j].ToString());
}
}
string[] rownameshm = new string[Math.Min(500, Rownum)];
for (int i = 0; i < Math.Min(500, Rownum); i++)
{
rownameshm[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownameshm = TGS.CreateCharacterVector(rownameshm);
TGS.SetSymbol("Rownamehm", Rrownameshm);
NumericMatrix Rdfhm = TGS.CreateNumericMatrix(dfhm);
TGS.SetSymbol("Freqdfhm", Rdfhm);
NumericVector RRowhm = TGS.CreateNumeric(Math.Min(500, Rownum));
TGS.SetSymbol("plotnum", RRowhm);
TGS.Evaluate("Freqdfhm <- as.data.frame(Freqdfhm)");
TGS.Evaluate("rownames(Freqdfhm) <- Rownamehm");
TGS.Evaluate("colnames(Freqdfhm) <- SampleName");
TGS.Evaluate("Freqdfhm <- as.matrix(Freqdfhm)");
TGS.Evaluate("library(pheatmap)");
TGS.Evaluate("windows()");
if (this.checkBox6.Checked)
{
if (this.checkBox7.Checked)
{
TGS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=T,cluster_rows=T)");
}
else
{
TGS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=F,cluster_rows=T)");
}
}
else
{
if (this.checkBox7.Checked)
{
TGS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=T,cluster_rows=F)");
}
else
{
TGS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=F,cluster_rows=F)");
}
}
}
}
else if (this.comboBox1.SelectedIndex == 6)
{
double Sum1 = 0;
double Sum2 = 0;
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < GroupNum; j++)
{
Sum1 = Sum1 + app.CountMatrix[i, j];
}
for (int j = GroupNum; j < SampleNum - 1; j++)
{
Sum2 = Sum2 + app.CountMatrix[i, j];
}
}
TGS.SetSymbol("Sum1", TGS.CreateNumeric(Sum1));
TGS.SetSymbol("Sum2", TGS.CreateNumeric(Sum2));
TGS.Evaluate("R <- Sum1/Sum2");
TGS.Evaluate("treatadd <- R/(R+1)");
TGS.Evaluate("controladd <- 1/(R+1)");
for (int i = 0; i < FeatureNum; i++)
{
double n11 = 0;
double n12 = 0;
double n21 = 0;
double n22 = 0;
for (int j = 0; j < GroupNum; j++)
{
n11 = n11 + app.Count[i][j];
}
for (int j = GroupNum; j < SampleNum - 1; j++)
{
n21 = n21 + app.Count[i][j];
}
if ((n11 < GroupNum) && (n21 < SampleNum - 1 - GroupNum))
{
OddRatio.Add(null);
absOddRatio.Add(null);
}
else
{
n12 = Sum1 - n11;
n22 = Sum2 - n21;
TGS.SetSymbol("n11", TGS.CreateNumeric(n11));
TGS.SetSymbol("n12", TGS.CreateNumeric(n12));
TGS.SetSymbol("n21", TGS.CreateNumeric(n21));
TGS.SetSymbol("n22", TGS.CreateNumeric(n22));
TGS.Evaluate("odd_ratio <- log2(((n11+treatadd)*(n22+controladd))/((n21+controladd)*(n12+treatadd )))");
OddRatio.Add(double.Parse(TGS.GetSymbol("odd_ratio").ToString()));
absOddRatio.Add(Math.Abs(double.Parse(TGS.GetSymbol("odd_ratio").ToString())));
}
}
List <string> Annotation = new List <string>();
if (this.checkBox1.Checked)
{
if (this.radioButton1.Checked)
{
string strConnCOG;
strConnCOG = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/COG.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnCOG = new OleDbConnection(strConnCOG);
OleConnCOG.Open();
String sqlCOG = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelCOG = new OleDbDataAdapter(sqlCOG, OleConnCOG);
app.OleDsExcleCOG = new DataSet();
OleDaExcelCOG.Fill(app.OleDsExcleCOG, "Sheet1");
OleConnCOG.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExcleCOG.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExcleCOG.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExcleCOG.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
else if (this.radioButton2.Checked)
{
string strConnPFAM;
strConnPFAM = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/PFAM.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnPFAM = new OleDbConnection(strConnPFAM);
OleConnPFAM.Open();
String sqlPFAM = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelPFAM = new OleDbDataAdapter(sqlPFAM, OleConnPFAM);
app.OleDsExclePFAM = new DataSet();
OleDaExcelPFAM.Fill(app.OleDsExclePFAM, "Sheet1");
OleConnPFAM.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExclePFAM.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExclePFAM.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExclePFAM.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
}
DataTable dt = new DataTable();
dt.Columns.Add("Feature", typeof(string));
for (int i = 0; i < SampleNum; i++)
{
dt.Columns.Add(SampleName[i], typeof(double));;
}
dt.Columns.Add("Odd_Ratio", typeof(double));
dt.Columns.Add("abs_Odd_Ratio", typeof(double));
dt.Columns.Add("Annotation", typeof(string));
for (int i = 0; i < FeatureNum; i++)
{
DataRow dr = dt.NewRow();
dr[0] = FeatureName[i];
for (int j = 1; j <= SampleNum; j++)
{
dr[j] = app.CountMatrix[i, j - 1];
}
if (OddRatio[i] == null)
{
dr[SampleNum] = DBNull.Value;
dr[SampleNum + 1] = DBNull.Value;
}
else
{
dr[SampleNum] = OddRatio[i];
dr[SampleNum + 1] = absOddRatio[i];
}
if (this.checkBox1.Checked)
{
dr[SampleNum + 2] = Annotation[i];
}
else
{
dr[SampleNum + 2] = null;
}
dt.Rows.Add(dr);
}
DataTable dtCopy = dt.Copy();
DataView dv = dt.DefaultView;
dv.Sort = "abs_Odd_Ratio DESC";
dtCopy = dv.ToTable();
Microsoft.Office.Interop.Excel.Application xlApp = new Microsoft.Office.Interop.Excel.Application();
System.Globalization.CultureInfo CurrentCI = System.Threading.Thread.CurrentThread.CurrentCulture;
System.Threading.Thread.CurrentThread.CurrentCulture = new System.Globalization.CultureInfo("en-US");
Microsoft.Office.Interop.Excel.Workbooks workbooks = xlApp.Workbooks;
Microsoft.Office.Interop.Excel.Workbook workbook = workbooks.Add(Microsoft.Office.Interop.Excel.XlWBATemplate.xlWBATWorksheet);
Microsoft.Office.Interop.Excel.Worksheet worksheet = (Microsoft.Office.Interop.Excel.Worksheet)workbook.Worksheets[1];
Microsoft.Office.Interop.Excel.Range range;
long totalCount = dtCopy.Rows.Count;
long rowRead = 0;
float percent = 0;
for (int i = 0; i < dtCopy.Columns.Count; i++)
{
worksheet.Cells[1, i + 1] = dtCopy.Columns[i].ColumnName;
range = (Microsoft.Office.Interop.Excel.Range)worksheet.Cells[1, i + 1];
range.Interior.ColorIndex = 15;
range.Font.Bold = true;
}
for (int r = 0; r < dtCopy.Rows.Count; r++)
{
for (int i = 0; i < dtCopy.Columns.Count; i++)
{
worksheet.Cells[r + 2, i + 1] = dtCopy.Rows[r][i].ToString();
}
rowRead++;
percent = ((float)(100 * rowRead)) / totalCount;
}
xlApp.Visible = true;
}
this.Close();
}
}
private void button1_Click(object sender, EventArgs e)
{
string[] FeatureName = (string[])app.FeaName;
string[] SampleName = (string[])app.SamName;
int FeatureNum = FeatureName.GetLength(0);
int SampleNum = SampleName.GetLength(0);
REngine.SetEnvironmentVariables();
REngine CLU = REngine.GetInstance();
CLU.Initialize();
NumericMatrix Freq = CLU.CreateNumericMatrix(app.FreqMatrix);
CLU.SetSymbol("Freq", Freq);
NumericMatrix Count = CLU.CreateNumericMatrix(app.CountMatrix);
CLU.SetSymbol("Count", Count);
NumericVector RFeatureNum = CLU.CreateNumeric(FeatureNum);
NumericVector RSampleNum = CLU.CreateNumeric(SampleNum);
CLU.SetSymbol("FeatureNum", RFeatureNum);
CLU.SetSymbol("SampleNum", RSampleNum);
CharacterVector RSampleName = CLU.CreateCharacterVector(app.SamName);
CharacterVector RFeatureName = CLU.CreateCharacterVector(app.FeaName);
CLU.SetSymbol("FeatureName", RFeatureName);
CLU.SetSymbol("SampleName", RSampleName);
CLU.Evaluate("CountMatrix <- as.data.frame(Count)");
CLU.Evaluate("names(CountMatrix) <- SampleName");
switch (this.comboBox1.SelectedIndex)
{
case 0:
CLU.Evaluate("d <- dist(t(CountMatrix),method = \"euclidean\")");
break;
case 1:
CLU.Evaluate("d <- dist(t(CountMatrix),method = \"maximum\")");
break;
case 2:
CLU.Evaluate("d <- dist(t(CountMatrix),method = \"manhattan\")");
break;
case 3:
CLU.Evaluate("d <- dist(t(CountMatrix),method = \"canberra\")");
break;
case 4:
CLU.Evaluate("d <- dist(t(CountMatrix),method = \"binary\")");
break;
case 5:
CLU.Evaluate("d <- dist(t(CountMatrix),method = \"minkowski\")");
break;
default:
break;
}
switch (this.comboBox2.SelectedIndex)
{
case 0:
CLU.Evaluate("hc <- hclust(d,method = \"ward\")");
break;
case 1:
CLU.Evaluate("hc <- hclust(d,method = \"single\")");
break;
case 2:
CLU.Evaluate("hc <- hclust(d,method = \"complete\")");
break;
case 3:
CLU.Evaluate("hc <- hclust(d,method = \"average\")");
break;
case 4:
CLU.Evaluate("hc <- hclust(d,method = \"mcquitty\")");
break;
case 5:
CLU.Evaluate("hc <- hclust(d,method = \"median\")");
break;
case 6:
CLU.Evaluate("hc <- hclust(d,method = \"centroid\")");
break;
default:
break;
}
CLU.Evaluate("plot(hc)");
this.Close();
}