void PreCalculate() {
if (dataset == null)
return;
// Extract the relevant data table.
var bs = dataset.BodyList;
INumberTable nt = dataset.GetNumberTableView();
toIdx = Enumerable.Range(0, bs.Count).Where(i => !bs[i].Disabled).ToArray(); // Indexes of enabled bodies.
int N = nt.Rows - pcaSamples;
int[] enabledRows = toIdx.Where(i => i < N).ToArray();
if (enabledRows.Length == 0)
throw new TException("No data available!");
P = new float[enabledRows.Length][];
MT.Loop(0, P.Length, row => {
float[] R = P[row] = new float[nt.Columns];
double[] dsR = nt.Matrix[enabledRows[row]] as double[];
for (int col = 0; col < nt.Columns; col++)
R[col] = (float)dsR[col];
});
// Reverse toIdx;
int[] rIdx = Enumerable.Repeat(-1, bs.Count).ToArray();
for (int i = 0; i < toIdx.Length; i++) rIdx[toIdx[i]] = i;
toIdx = rIdx;
using (var gpu = new VisuMap.DxShader.GpuDevice())
dtP = DualAffinity.DotProduct(gpu, P, false);
float[] singValues = new float[pcaMax];
float[][] PC = FastPca.DoFastReduction(P, pcaMax, singValues, true);
P = VisuMap.MathUtil.NewMatrix<float>(PC.Length, pcaSamples); // P now links data points with the injected points on the main PCA axis.
float span = 4.0f * singValues[0];
stepSize = span / (pcaSamples - 1);
float x0 = - 0.5f * span;
MT.ForEach(PC, (R, row) => {
double yy = R.Skip(1).Sum(v => v * v);
for (int col = 0; col < pcaSamples; col++) {
double x = R[0] - (x0 + col * stepSize);
P[row][col] = (float)Math.Sqrt(x * x + yy);
}
});
}
void PreCalculate()
{
if (dataset != null)
{
var bs = dataset.BodyList;
INumberTable nt = dataset.GetNumberTableView();
int nrows = nt.Rows - nt.Columns;
toIdx = Enumerable.Range(0, bs.Count).Where(i => !bs[i].Disabled).ToArray(); // Indexes of enabled bodies.
int[] selectedRows = toIdx.Where(i => i < nrows).ToArray();
int[] selectedColumns = toIdx.Where(i => i >= nrows).Select(i => i - nrows).ToArray();
if ((selectedColumns.Length == 0) || (selectedRows.Length == 0))
{
throw new TException("No-zero number of genes and cells must be selected!");
}
P = new float[selectedRows.Length][];
MT.Loop(0, P.Length, row => {
float[] R = P[row] = new float[selectedColumns.Length];
double[] dsR = nt.Matrix[selectedRows[row]] as double[];
for (int col = 0; col < selectedColumns.Length; col++)
{
R[col] = (float)dsR[selectedColumns[col]];
}
});
// Reverse toIdx;
int[] rIdx = Enumerable.Repeat(-1, bs.Count).ToArray();
for (int i = 0; i < toIdx.Length; i++)
{
rIdx[toIdx[i]] = i;
}
toIdx = rIdx;
}
float[][] Q = Transpose(P); // Q is the transpose of P.
// Calculate the distance tables for P and Q.
bool isPCor = (metricMode == MetricMode.CorCor) || (metricMode == MetricMode.CorEuc);
bool isQCor = (metricMode == MetricMode.CorCor) || (metricMode == MetricMode.EucCor);
if (isPCor)
{
Normalize(P);
}
if (isQCor)
{
Normalize(Q);
}
using (var gpu = new VisuMap.DxShader.GpuDevice())
using (var cc = gpu.CreateConstantBuffer <ShaderConstant>(0))
using (var sd = gpu.LoadShader("SingleCellAnalysis.DotProduct.cso", Assembly.GetExecutingAssembly())) {
dtP = CallShadere(gpu, cc, sd, P, isPCor);
dtQ = CallShadere(gpu, cc, sd, Q, isQCor);
}
/*
* affinity-propagation enhances structure if colums or rows within clusters are
* light occupied with high randomness. AP however dilutes clusters with few members.
* For instance, singlton gene cluster (with only gene) will suppressed by AP due to
* its aggregation with neighboring genes (which should be consdiered as separate
* clusters.)
*
* ProbagateAffinity(dtQ, P); // Column<->Column affinity => Column->Row affinity
* ProbagateAffinity(dtP, Q); // Row<->Row affinity => Row->Column affinity.
*/
// Calculates the distance between rows and columns into P.
P = AffinityToDistance(P, Q);
Q = null;
var app = SingleCellPlugin.App.ScriptApp;
double linkBias = app.GetPropertyAsDouble("SingleCell.Separation", 1.0);
double pScale = app.GetPropertyAsDouble("SingleCell.CellScale", 1.0);
double qScale = app.GetPropertyAsDouble("SingleCell.GeneScale", 1.0);
//PowerMatrix(P, linkBias);
// Scaling dtP, dtQ to the range of P.
Func <float[][], float> aFct = AverageSqrt;
double av = aFct(P);
pScale *= av / aFct(dtP);
qScale *= av / aFct(dtQ);
ScaleMatrix(dtP, pScale);
ScaleMatrix(dtQ, qScale);
//ScaleMatrix(P, linkBias);
ShiftMatrix(P, (float)(linkBias * av));
}