--- /dev/null
+/*
+ * mothurmetastats.cpp
+ * Mothur
+ *
+ * Created by westcott on 7/6/11.
+ * Copyright 2011 Schloss Lab. All rights reserved.
+ *
+ */
+
+#include "mothurmetastats.h"
+#include "mothurfisher.h"
+
+/***********************************************************/
+MothurMetastats::MothurMetastats(double t, int n) {
+ try {
+ m = MothurOut::getInstance();
+ threshold = t;
+ numPermutations = n;
+
+ }catch(exception& e) {
+ m->errorOut(e, "MothurMetastats", "MothurMetastats");
+ exit(1);
+ }
+}
+/***********************************************************/
+MothurMetastats::~MothurMetastats() {
+ try {
+
+
+ }catch(exception& e) {
+ m->errorOut(e, "MothurMetastats", "~MothurMetastats");
+ exit(1);
+ }
+}
+/***********************************************************/
+//main metastats function
+int MothurMetastats::runMetastats(string outputFileName, vector< vector<double> >& data, int secondGroupingStart) {
+ try {
+ int bflag = 0;
+ row = data.size(); //numBins
+ column = data[0].size(); //numGroups in subset
+ int size = row*column;
+
+ //consistent with original, but this should never be true
+ if ((secondGroupingStart >= column) || (secondGroupingStart <= 0)) { m->mothurOut("[ERROR]: Check your g value."); m->mothurOutEndLine(); return 0; }
+
+ //Initialize the matrices
+ vector<double> pmatrix; pmatrix.resize(size, 0.0);
+ vector<double> permuted; permuted.resize(size, 0.0);
+ vector< vector<double> > storage; storage.resize(row);
+ for (int i = 0; i < storage.size(); i++) { storage[i].resize(9, 0.0); }
+
+ //Produces the sum of each column
+ vector<double> total; total.resize(column, 0.0);
+ vector<double> ratio; ratio.resize(column, 0.0);
+ double total1 = 0.0; double total2 = 0.0;
+
+ //total[i] = total abundance for group[i]
+ for (int i = 0; i < column; i++) {
+ for (int j = 0; j < row; j++) {
+ total[i] += data[j][i];
+ }
+ }
+
+ //total for first grouping
+ for (int i = 0; i < (secondGroupingStart-1); i++) { total1 += total[i]; }
+
+ //total for second grouping
+ for (int i = (secondGroupingStart-1); i < column; i++) { total2 += total[i]; }
+
+ //Creates the ratios by first finding the minimum of totals
+ double min = total[0];
+ for (int i = 0; i < total.size(); i++) {
+ if (total[i] < min) { min = total[i]; }
+ }
+
+ //sanity check
+ if (min <= 0.0) { m->mothurOut("[ERROR]: the sum of one of the columns <= 0."); m->mothurOutEndLine(); return 0; }
+
+ //Ratio time...
+ for(int i = 0; i < ratio.size(); i++){ ratio[i] = total[i] / min; }
+
+ //Change matrix into an array as received by R for compatibility - kept to be consistent with original
+ int count = 0;
+ for(int i = 0; i < column; i++){
+ for(int j = 0; j < row; j++){
+ pmatrix[count]=data[j][i];
+ count++;
+ }
+ }
+
+ if(row == 1){
+ for (int i =0; i < column; i++){ pmatrix[i] /= ratio[i]; }
+ }else {
+ count = 0; int j=-1;
+
+ for (int i=0; i < size; i++) {
+ if (count % row == 0) { j++; }
+ pmatrix[i] /= ratio[j];
+ count++;
+ }
+ }
+
+ vector<double> permuted_ttests; permuted_ttests.resize(row, 0.0);
+ vector<double> pvalues; pvalues.resize(row, 0.0);
+ vector<double> tinitial; tinitial.resize(row, 0.0);
+
+ if (m->control_pressed) { return 1; }
+
+ //Find the initial values for the matrix.
+ start(pmatrix, secondGroupingStart, tinitial, storage);
+
+ if (m->control_pressed) { return 1; }
+
+ // Start the calculations.
+ if ( (column == 2) || ((secondGroupingStart-1) < 8) || ((column-secondGroupingStart+1) < 8) ){
+
+ vector<double> fish; fish.resize(row, 0.0);
+ vector<double> fish2; fish2.resize(row, 0.0);
+
+ for(int i = 0; i < row; i++){
+
+ for(int j = 0; j < (secondGroupingStart-1); j++) { fish[i] += data[i][j]; }
+ for(int j = (secondGroupingStart-1); j < column; j++) { fish2[i] += data[i][j]; }
+
+ //vector<double> tempData; tempData.resize(4, 0.0);
+ double f11, f12, f21, f22;
+ f11 = fish[i];
+ f12 = fish2[i];
+ f21 = total1 - fish[i];
+ f22 = total2 - fish2[i];
+
+ double pre = 0.0;
+
+ MothurFisher fisher;
+ pre = fisher.fexact(f11, f12, f21, f22);
+
+ if (m->control_pressed) { return 1; }
+
+ if (pre > 0.999999999) { pre = 1.0; }
+ storage[i][8] = pre;
+ pvalues[i] = pre;
+ }
+
+ }else {
+
+ testp(permuted_ttests, permuted, pmatrix, secondGroupingStart, tinitial, pvalues);
+
+ if (m->control_pressed) { return 1; }
+
+ // Checks to make sure the matrix isn't sparse.
+ vector<double> sparse; sparse.resize(row, 0.0);
+ vector<double> sparse2; sparse2.resize(row, 0.0);
+
+ int c = 0;
+
+ for(int i = 0; i < row; i++){
+
+ for(int j = 0; j < (secondGroupingStart-1); j++){ sparse[i] += data[i][j]; }
+ if(sparse[i] < (double)(secondGroupingStart-1)){ c++; }
+
+ // ?<= for col
+ for(int j = (secondGroupingStart-1); j < column; j++){ sparse2[i] += data[i][j]; }
+ if( (sparse2[i] < (double)(column-secondGroupingStart+1))) { c++; }
+
+ if (c == 2) {
+ c=0;
+ double f11,f12,f21,f22;
+
+ f11=sparse[i]; sparse[i]=0;
+ f12=sparse2[i]; sparse2[i]=0;
+ f21 = total1 - f11;
+ f22 = total2 - f12;
+
+ double pre = 0.0;
+
+ MothurFisher fisher;
+ pre = fisher.fexact(f11, f12, f21, f22);
+
+ if (m->control_pressed) { return 1; }
+
+ if (pre > 0.999999999){
+ pre = 1.0;
+ }
+
+ storage[i][8] = pre;
+ pvalues[i] = pre;
+ }
+ }
+
+ bflag = 1;
+ }
+
+ // Calculates the mean of counts (not normalized)
+ vector< vector<double> > temp; temp.resize(row);
+ for (int i = 0; i < temp.size(); i++) { temp[i].resize(2, 0.0); }
+
+ for (int j = 0; j < row; j++){
+ if (m->control_pressed) { return 1; }
+
+ for (int i = 1; i <= (secondGroupingStart-1); i++){ temp[j][0] += data[j][i-1]; }
+ temp[j][0] /= (double)(secondGroupingStart-1);
+
+ for(int i = secondGroupingStart; i <= column; i++){ temp[j][1] += data[j][i-1]; }
+ temp[j][1] /= (double)(column-secondGroupingStart+1);
+ }
+
+ for(int i = 0; i < row; i++){
+ if (m->control_pressed) { return 1; }
+
+ storage[i][3]=temp[i][0];
+ storage[i][7]=temp[i][1];
+ storage[i][8]=pvalues[i];
+ }
+
+ // BACKUP checks
+ cout.setf(ios::fixed, ios::floatfield); cout.setf(ios::showpoint);
+ for (int i = 0; i < row; i++){
+
+ if (m->control_pressed) { return 1; }
+
+ if(pvalues[i] < threshold){
+ m->mothurOut("Feature " + toString((i+1)) + " is significant, p = ");
+ cout << pvalues[i];
+ m->mothurOutJustToLog(toString(pvalues[i])); m->mothurOutEndLine();
+ }
+ }
+
+ // And now we write the files to a text file.
+ struct tm *local;
+ time_t t; t = time(NULL);
+ local = localtime(&t);
+
+ ofstream out;
+ m->openOutputFile(outputFileName, out);
+ out.setf(ios::fixed, ios::floatfield); out.setf(ios::showpoint);
+
+ out << "Local time and date of test: " << asctime(local) << endl;
+ out << "# rows = " << row << ", # col = " << column << ", g = " << secondGroupingStart << endl << endl;
+ if (bflag == 1){ out << numPermutations << " permutations" << endl << endl; }
+
+ //output column headings - not really sure... documentation labels 9 columns, there are 10 in the output file
+ //storage 0 = meanGroup1 - line 529, 1 = varGroup1 - line 532, 2 = err rate1 - line 534, 3 = mean of counts group1?? - line 291, 4 = meanGroup2 - line 536, 5 = varGroup2 - line 539, 6 = err rate2 - line 541, 7 = mean of counts group2?? - line 292, 8 = pvalues - line 293
+ out << "OTU\tmean(group1)\tvariance(group1)\tstderr(group1)\tmean_of_counts(group1)\tmean(group2)\tvariance(group2)\tstderr(group2)\tmean_of_counts(group1)\tp-value\n";
+
+ for(int i = 0; i < row; i++){
+ if (m->control_pressed) { out.close(); return 0; }
+
+ out << (i+1);
+ for(int j = 0; j < 9; j++){ out << '\t' << storage[i][j]; }
+ out << endl;
+ }
+
+ out << endl << endl;
+ out.close();
+
+ return 0;
+
+ }catch(exception& e) {
+ m->errorOut(e, "MothurMetastats", "runMetastats");
+ exit(1);
+ }
+}
+/***********************************************************/
+//Find the initial values for the matrix
+int MothurMetastats::start(vector<double>& Imatrix, int secondGroupingStart, vector<double>& initial, vector< vector<double> >& storage) {
+ try {
+
+ int a = row; a*=4;
+
+ double xbardiff = 0.0; double denom = 0.0;
+ vector<double> store; store.resize(a, 0.0);
+ vector<double> tool; tool.resize(a, 0.0);
+ vector< vector<double> > C1; C1.resize(row);
+ for (int i = 0; i < C1.size(); i++) { C1[i].resize(3, 0.0); }
+ vector< vector<double> > C2; C2.resize(row);
+ for (int i = 0; i < C2.size(); i++) { C2[i].resize(3, 0.0); }
+
+ meanvar(Imatrix, secondGroupingStart, store);
+
+ if (m->control_pressed) { return 0; }
+
+ //copy store into tool
+ tool = store;
+
+ for (int i = 0; i < row; i++){
+ C1[i][0]=tool[i]; //mean group 1
+ storage[i][0]=C1[i][0];
+ C1[i][1]=tool[i+row+row]; // var group 1
+ storage[i][1]=C1[i][1];
+ C1[i][2]=C1[i][1]/(secondGroupingStart-1);
+ storage[i][2]=sqrt(C1[i][2]);
+
+ C2[i][0]=tool[i+row]; // mean group 2
+ storage[i][4]=C2[i][0];
+ C2[i][1]=tool[i+row+row+row]; // var group 2
+ storage[i][5]=C2[i][1];
+ C2[i][2]=C2[i][1]/(column-secondGroupingStart+1);
+ storage[i][6]=sqrt(C2[i][2]);
+ }
+
+ if (m->control_pressed) { return 0; }
+
+ for (int i = 0; i < row; i++){
+ xbardiff = C1[i][0]-C2[i][0];
+ denom = sqrt(C1[i][2]+C2[i][2]);
+ initial[i]=fabs(xbardiff/denom);
+ }
+
+ return 0;
+
+ }catch(exception& e) {
+ m->errorOut(e, "MothurMetastats", "start");
+ exit(1);
+ }
+}
+/***********************************************************/
+int MothurMetastats::meanvar(vector<double>& pmatrix, int secondGroupingStart, vector<double>& store) {
+ try {
+ vector<double> temp; temp.resize(row, 0.0);
+ vector<double> temp2; temp2.resize(row, 0.0);
+ vector<double> var; var.resize(row, 0.0);
+ vector<double> var2; var2.resize(row, 0.0);
+
+ double a = secondGroupingStart-1;
+ double b = column - a;
+ int m = a * row;
+ int n = row * column;
+
+ for (int i = 0; i < m; i++) { temp[i%row] += pmatrix[i]; }
+ for (int i = 0; i < n; i++) { temp2[i%row]+= pmatrix[i]; }
+ for (int i = 0; i < row; i++) { temp2[i] -= temp[i]; }
+ for (int i = 0; i <= row-1;i++) {
+ store[i] = temp[i]/a;
+ store[i+row]=temp2[i]/b;
+ }
+
+ //That completes the mean calculations.
+
+ for (int i = 0; i < m; i++) { var[i%row] += pow((pmatrix[i]-store[i%row]),2); }
+ for (int i = m; i < n; i++) { var2[i%row]+= pow((pmatrix[i]-store[(i%row)+row]),2); }
+ for (int i = 0; i <= row-1; i++){
+ store[i+2*row]=var[i]/(a-1);
+ store[i+3*row]=var2[i]/(b-1);
+ }
+
+ // That completes var calculations.
+
+ return 0;
+
+ }catch(exception& e) {
+ m->errorOut(e, "MothurMetastats", "meanvar");
+ exit(1);
+ }
+}
+/***********************************************************/
+int MothurMetastats::testp(vector<double>& permuted_ttests, vector<double>& permuted, vector<double>& Imatrix, int secondGroupingStart, vector<double>& Tinitial, vector<double>& ps) {
+ try {
+
+ vector<double> Tvalues; Tvalues.resize(row, 0.0);
+ vector<double> counter; counter.resize(row, 0.0);
+ int a, b, n;
+
+ a = numPermutations;
+ b = row;
+ n = a*b;
+
+ for (int j = 1; j <= row; j++) {
+ if (m->control_pressed) { return 0; }
+ permute_matrix(Imatrix, permuted, secondGroupingStart, Tvalues, Tinitial, counter);
+ }
+
+ for(int j = 0; j < row; j++) {
+ if (m->control_pressed) { return 0; }
+ ps[j] = ((counter[j]+1)/(double)(a+1));
+ }
+
+ return 0;
+
+ }catch(exception& e) {
+ m->errorOut(e, "MothurMetastats", "testp");
+ exit(1);
+ }
+}
+/***********************************************************/
+int MothurMetastats::permute_matrix(vector<double>& Imatrix, vector<double>& permuted, int secondGroupingStart, vector<double>& trial_ts, vector<double>& Tinitial, vector<double>& counter1){
+ try {
+
+ vector<int> y; y.resize(column, 0);
+ for (int i = 1; i <= column; i++){ y[i-1] = i; }
+
+ permute_array(y);
+
+ int f = 0; int c = 0; int k = 0;
+ for (int i = 0; i < column; i++){
+
+ if (m->control_pressed) { return 0; }
+
+ f = y[i]; //column number
+ c = 1;
+ c *= (f-1);
+ c *= row;
+ if (f == 1){ c = 0; } // starting value position in the Imatrix
+
+ for(int j = 1; j <= row; j++){
+ permuted[k] = Imatrix[c];
+ c++; k++;
+ }
+ }
+
+ calc_twosample_ts(permuted, secondGroupingStart, trial_ts, Tinitial, counter1);
+
+ return 0;
+
+ }catch(exception& e) {
+ m->errorOut(e, "MothurMetastats", "permute_matrix");
+ exit(1);
+ }
+}
+/***********************************************************/
+int MothurMetastats::permute_array(vector<int>& array) {
+ try {
+ static int seeded = 0;
+
+ if (! seeded) {
+ seeded = 1;
+ srand(time(NULL));
+ }
+
+ for (int i = 0; i < array.size(); i++) {
+ if (m->control_pressed) { return 0; }
+
+ int selection = rand() % (array.size() - i);
+ int tmp = array[i + selection];
+ array[i + selection] = array[i];
+ array[i] = tmp;
+ }
+
+ return 0;
+
+ }catch(exception& e) {
+ m->errorOut(e, "MothurMetastats", "permute_array");
+ exit(1);
+ }
+}
+/***********************************************************/
+int MothurMetastats::calc_twosample_ts(vector<double>& Pmatrix, int secondGroupingStart, vector<double>& Ts, vector<double>& Tinitial, vector<double>& counter) {
+ try {
+ int a = row * 4;
+
+ vector< vector<double> > C1; C1.resize(row);
+ for (int i = 0; i < C1.size(); i++) { C1[i].resize(3, 0.0); }
+ vector< vector<double> > C2; C2.resize(row);
+ for (int i = 0; i < C2.size(); i++) { C2[i].resize(3, 0.0); }
+ vector<double> storage; storage.resize(row, 0.0);
+ vector<double> tool; tool.resize(row, 0.0);
+ double xbardiff = 0.0; double denom = 0.0;
+
+ meanvar(Pmatrix, secondGroupingStart, storage);
+
+ for(int i = 0;i <= (a-1); i++) {
+ if (m->control_pressed) { return 0; }
+ tool[i] = storage[i];
+ }
+
+ for (int i = 0; i < row; i++){
+ if (m->control_pressed) { return 0; }
+ C1[i][0]=tool[i];
+ C1[i][1]=tool[i+row+row];
+ C1[i][2]=C1[i][1]/(secondGroupingStart-1);
+
+ C2[i][0]=tool[i+row];
+ C2[i][1]=tool[i+row+row+row]; // var group 2
+ C2[i][2]=C2[i][1]/(column-secondGroupingStart+1);
+ }
+
+ for (int i = 0; i < row; i++){
+ if (m->control_pressed) { return 0; }
+ xbardiff = C1[i][0]-C2[i][0];
+ denom = sqrt(C1[i][2]+C2[i][2]);
+ Ts[i]=fabs(xbardiff/denom);
+ if (fabs(Ts[i])>(fabs(Tinitial[i])+.0000000000001)){ //13th place
+ counter[i]++;
+ }
+ }
+
+ return 0;
+
+ }catch(exception& e) {
+ m->errorOut(e, "MothurMetastats", "calc_twosample_ts");
+ exit(1);
+ }
+}
+/***********************************************************/
+
+
projects / mothur.git / blobdiff