helpString += "The pca command parameters are shared, relabund, label, groups and metric. shared or relabund is required unless you have a valid current file.";
helpString += "The label parameter is used to analyze specific labels in your input. Default is the first label in your shared or relabund file. Multiple labels may be separated by dashes.\n";
helpString += "The groups parameter allows you to specify which groups you would like analyzed. Groupnames are separated by dashes.\n";
- helpString += "The metric parameter allows indicate you if would like the pearson correlation coefficient calculated. Default=True";
+ helpString += "The metric parameter allows you to indicate if would like the pearson correlation coefficient calculated. Default=True";
helpString += "Example pca(groups=yourGroups).\n";
helpString += "Example pca(groups=A-B-C).\n";
helpString += "Note: No spaces between parameter labels (i.e. groups), '=' and parameters (i.e.yourGroups).\n";
//get first line of shared file
vector< vector<double> > matrix;
InputData* input;
- if (mode == "shared") {
+ if (mode == "sharedfile") {
input = new InputData(inputFile, "sharedfile");
}else if (mode == "relabund") {
input = new InputData(inputFile, "relabund");
exit(1);
}
}
+
/**********************************************************************************************************************
vector< vector<double> > PCACommand::createMatrix(vector<SharedRAbundFloatVector*> lookupFloat){
try {
}
}*/
//**********************************************************************************************************************
+
int PCACommand::process(vector<SharedRAbundFloatVector*>& lookupFloat){
try {
m->mothurOut("\nProcessing " + lookupFloat[0]->getLabel()); m->mothurOutEndLine();
+
+ int numOTUs = lookupFloat[0]->getNumBins();
+ int numSamples = lookupFloat.size();
- vector< vector<double> > matrix; matrix.resize(lookupFloat.size());
-
- ofstream out;
- string temp = outputDir + "matrix.transpose.out";
- m->openOutputFile(temp, out);
- out << "matrix" << endl;
+ vector< vector<double> > matrix(numSamples);
+ vector<double> colMeans(numOTUs);
- //fill matrix with shared files relative abundances
+
+ //fill matrix with shared relative abundances, re-center
for (int i = 0; i < lookupFloat.size(); i++) {
- for (int j = 0; j < lookupFloat[i]->getNumBins(); j++) {
- matrix[i].push_back(lookupFloat[i]->getAbundance(j));
- out << lookupFloat[i]->getAbundance(j) << '\t';
- }
- out << endl;
- }
- out << endl << endl << "transpose" << endl;
- vector< vector<double> > transposeMatrix; transposeMatrix.resize(matrix[0].size());
- for (int i = 0; i < transposeMatrix.size(); i++) {
- for (int j = 0; j < matrix.size(); j++) {
- transposeMatrix[i].push_back(matrix[j][i]);
- out << matrix[j][i] << '\t';
+ matrix[i].resize(numOTUs, 0);
+
+ for (int j = 0; j < numOTUs; j++) {
+ matrix[i][j] = lookupFloat[i]->getAbundance(j);
+ colMeans[j] += matrix[i][j];
}
- out << endl;
}
- matrix = linearCalc.matrix_mult(matrix, transposeMatrix);
+
+ for(int j=0;j<numOTUs;j++){
+ colMeans[j] = colMeans[j] / (double)numSamples;
+ }
- out << endl << endl << "matrix mult" << endl;
- for (int i = 0; i < matrix.size(); i++) {
- for (int j = 0; j < matrix[i].size(); j++) {
- out << matrix[i][j] << '\t';
+ vector<vector<double> > centered = matrix;
+ for(int i=0;i<numSamples;i++){
+ for(int j=0;j<numOTUs;j++){
+ centered[i][j] = centered[i][j] - colMeans[j];
}
- out << endl;
}
- out.close();
+
+
+ vector< vector<double> > transpose(numOTUs);
+ for (int i = 0; i < numOTUs; i++) {
+ transpose[i].resize(numSamples, 0);
+ for (int j = 0; j < numSamples; j++) {
+ transpose[i][j] = centered[j][i];
+ }
+ }
+
+ vector<vector<double> > crossProduct = linearCalc.matrix_mult(transpose, centered);
- double offset = 0.0000;
vector<double> d;
vector<double> e;
- vector<vector<double> > G = matrix;
- //vector<vector<double> > copy_G;
-
- for(int count=0;count<2;count++){
- linearCalc.recenter(offset, matrix, G); if (m->control_pressed) { return 0; }
- linearCalc.tred2(G, d, e); if (m->control_pressed) { return 0; }
- linearCalc.qtli(d, e, G); if (m->control_pressed) { return 0; }
- offset = d[d.size()-1];
- if(offset > 0.0) break;
- }
+
+ linearCalc.tred2(crossProduct, d, e); if (m->control_pressed) { return 0; }
+ linearCalc.qtli(d, e, crossProduct); if (m->control_pressed) { return 0; }
+
+ vector<vector<double> > X = linearCalc.matrix_mult(centered, crossProduct);
if (m->control_pressed) { return 0; }
string fbase = outputDir + m->getRootName(m->getSimpleName(inputFile));
string outputFileName = fbase + lookupFloat[0]->getLabel();
- output(outputFileName, m->Groups, G, d);
+ output(outputFileName, m->Groups, X, d);
if (metric) {
+ vector<vector<double> > observedEuclideanDistance = linearCalc.getObservedEuclideanDistance(centered);
+
for (int i = 1; i < 4; i++) {
- vector< vector<double> > EuclidDists = linearCalc.calculateEuclidianDistance(G, i); //G is the pca file
+ vector< vector<double> > PCAEuclidDists = linearCalc.calculateEuclidianDistance(X, i); //G is the pca file
if (m->control_pressed) { for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); } return 0; }
-
- double corr = linearCalc.calcPearson(EuclidDists, matrix); //G is the pca file, D is the users distance matrix
-
- m->mothurOut("Pearson's coefficient using " + toString(i) + " axis: " + toString(corr)); m->mothurOutEndLine();
-
+
+ double corr = linearCalc.calcPearson(PCAEuclidDists, observedEuclideanDistance);
+
m->mothurOut("Rsq " + toString(i) + " axis: " + toString(corr * corr)); m->mothurOutEndLine();
if (m->control_pressed) { for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); } return 0; }
void PCACommand::output(string fnameRoot, vector<string> name_list, vector<vector<double> >& G, vector<double> d) {
try {
- int rank = name_list.size();
+
+ int numEigenValues = d.size();
double dsum = 0.0000;
- for(int i=0;i<rank;i++){
+ for(int i=0;i<numEigenValues;i++){
dsum += d[i];
- for(int j=0;j<rank;j++){
- if(d[j] >= 0) { G[i][j] *= pow(d[j],0.5); }
- else { G[i][j] = 0.00000; }
- }
}
ofstream pcaData((fnameRoot+".pca.axes").c_str(), ios::trunc);
outputTypes["loadings"].push_back(fnameRoot+".pca.loadings");
pcaLoadings << "axis\tloading\n";
- for(int i=0;i<rank;i++){
+ for(int i=0;i<numEigenValues;i++){
pcaLoadings << i+1 << '\t' << d[i] * 100.0 / dsum << endl;
}
pcaData << "group";
- for(int i=0;i<rank;i++){
+ for(int i=0;i<numEigenValues;i++){
pcaData << '\t' << "axis" << i+1;
}
pcaData << endl;
- for(int i=0;i<rank;i++){
+ for(int i=0;i<numEigenValues;i++){
pcaData << name_list[i] << '\t';
- for(int j=0;j<rank;j++){
+ for(int j=0;j<numEigenValues;j++){
pcaData << G[i][j] << '\t';
}
pcaData << endl;
projects / mothur.git / commitdiff