+++ /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"
-#include "spline.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() {}
-/***********************************************************/
- //main metastats function
-int MothurMetastats::runMetastats(string outputFileName, vector< vector<double> >& data, int secGroupingStart) {
- try {
- row = data.size(); //numBins
- column = data[0].size(); //numGroups in subset
- secondGroupingStart = secGroupingStart; //g
-
- vector< vector<double> > Pmatrix; Pmatrix.resize(row);
- for (int i = 0; i < row; i++) { Pmatrix[i].resize(column, 0.0); } // the relative proportion matrix
- vector< vector<double> > C1; C1.resize(row);
- for (int i = 0; i < row; i++) { C1[i].resize(3, 0.0); } // statistic profiles for class1 and class 2
- vector< vector<double> > C2; C2.resize(row); // mean[1], variance[2], standard error[3]
- for (int i = 0; i < row; i++) { C2[i].resize(3, 0.0); }
- vector<double> T_statistics; T_statistics.resize(row, 1); // a place to store the true t-statistics
- vector<double> pvalues; pvalues.resize(row, 1); // place to store pvalues
- vector<double> qvalues; qvalues.resize(row, 1); // stores qvalues
-
- //*************************************
- // convert to proportions
- // generate Pmatrix
- //*************************************
- vector<double> totals; totals.resize(column, 0); // sum of columns
- //total[i] = total abundance for group[i]
- for (int i = 0; i < column; i++) {
- for (int j = 0; j < row; j++) {
- totals[i] += data[j][i];
- }
- }
-
- for (int i = 0; i < column; i++) {
- for (int j = 0; j < row; j++) {
- Pmatrix[j][i] = data[j][i]/totals[i];
-
- }
- }
-
- //#********************************************************************************
- //# ************************** STATISTICAL TESTING ********************************
- //#********************************************************************************
-
- if (column == 2){ //# then we have a two sample comparison
- //#************************************************************
- //# generate p values fisher's exact test
- //#************************************************************
- double total1, total2;
- //total for first grouping
- for (int i = 0; i < secondGroupingStart; i++) { total1 += totals[i]; }
-
- //total for second grouping
- for (int i = secondGroupingStart; i < column; i++) { total2 += totals[i]; }
-
- 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; j++) { fish[i] += data[i][j]; }
- for(int j = secondGroupingStart; j < column; j++) { fish2[i] += data[i][j]; }
-
- double f11, f12, f21, f22;
- f11 = fish[i];
- f12 = fish2[i];
- f21 = total1 - fish[i];
- f22 = total2 - fish2[i];
-
- MothurFisher fisher;
- double pre = fisher.fexact(f11, f12, f21, f22);
- if (pre > 0.999999999) { pre = 1.0; }
-
- if (m->control_pressed) { return 1; }
-
- pvalues[i] = pre;
- }
-
- //#*************************************
- //# calculate q values from p values
- //#*************************************
- qvalues = calc_qvalues(pvalues);
-
- }else { //we have multiple subjects per population
-
- //#*************************************
- //# generate statistics mean, var, stderr
- //#*************************************
- for(int i = 0; i < row; i++){ // for each taxa
- //# find the mean of each group
- double g1Total = 0.0; double g2Total = 0.0;
- for (int j = 0; j < secondGroupingStart; j++) { g1Total += Pmatrix[i][j]; }
- C1[i][0] = g1Total/(double)(secondGroupingStart);
- for (int j = secondGroupingStart; j < column; j++) { g2Total += Pmatrix[i][j]; }
- C2[i][0] = g2Total/(double)(column-secondGroupingStart);
-
- //# find the variance of each group
- double g1Var = 0.0; double g2Var = 0.0;
- for (int j = 0; j < secondGroupingStart; j++) { g1Var += pow((Pmatrix[i][j]-C1[i][0]), 2); }
- C1[i][1] = g1Var/(double)(secondGroupingStart-1);
- for (int j = secondGroupingStart; j < column; j++) { g2Var += pow((Pmatrix[i][j]-C2[i][0]), 2); }
- C2[i][1] = g2Var/(double)(column-secondGroupingStart-1);
-
- //# find the std error of each group -std err^2 (will change to std err at end)
- C1[i][2] = C1[i][1]/(double)(secondGroupingStart);
- C2[i][2] = C2[i][1]/(double)(column-secondGroupingStart);
- }
-
- //#*************************************
- //# two sample t-statistics
- //#*************************************
- for(int i = 0; i < row; i++){ // # for each taxa
- double xbar_diff = C1[i][0] - C2[i][0];
- double denom = sqrt(C1[i][2] + C2[i][2]);
- T_statistics[i] = xbar_diff/denom; // calculate two sample t-statistic
- }
-
- /*for (int i = 0; i < row; i++) {
- for (int j = 0; j < 3; j++) {
- cout << "C1[" << i+1 << "," << j+1 << "]=" << C1[i][j] << ";" << endl;
- cout << "C2[" << i+1 << "," << j+1 << "]=" << C2[i][j] << ";" << endl;
- }
- cout << "T_statistics[" << i+1 << "]=" << T_statistics[i] << ";" << endl;
- }*/
- //#*************************************
- //# generate initial permuted p-values
- //#*************************************
- pvalues = permuted_pvalues(Pmatrix, T_statistics, data);
-
- //#*************************************
- //# generate p values for sparse data
- //# using fisher's exact test
- //#*************************************
- double total1, total2;
- //total for first grouping
- for (int i = 0; i < secondGroupingStart; i++) { total1 += totals[i]; }
-
- //total for second grouping
- for (int i = secondGroupingStart; i < column; i++) { total2 += totals[i]; }
-
- 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; j++) { fish[i] += data[i][j]; }
- for(int j = secondGroupingStart; j < column; j++) { fish2[i] += data[i][j]; }
-
- if ((fish[1] < secondGroupingStart) && (fish2[i] < (column-secondGroupingStart))) {
- double f11, f12, f21, f22;
- f11 = fish[i];
- f12 = fish2[i];
- f21 = total1 - fish[i];
- f22 = total2 - fish2[i];
-
- MothurFisher fisher;
- double pre = fisher.fexact(f11, f12, f21, f22);
- if (pre > 0.999999999) { pre = 1.0; }
-
- if (m->control_pressed) { return 1; }
-
- pvalues[i] = pre;
- }
- }
-
- //#*************************************
- //# calculate q values from p values
- //#*************************************
- qvalues = calc_qvalues(pvalues);
-
- //#*************************************
- //# convert stderr^2 to std error
- //#*************************************
- for(int i = 0; i < row; i++){
- C1[i][2] = sqrt(C1[i][2]);
- C2[i][2] = sqrt(C2[i][2]);
- }
- }
-
- // 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;
- 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(group2)\tvariance(group2)\tstderr(group2)\tp-value\tq-value\n";
-
- for(int i = 0; i < row; i++){
- if (m->control_pressed) { out.close(); return 0; }
-
- //if there are binlabels use them otherwise count.
- if (m->binLabelsInFile.size() == row) { out << m->binLabelsInFile[i] << '\t'; }
- else { out << (i+1) << '\t'; }
-
- out << C1[i][0] << '\t' << C1[i][1] << '\t' << C1[i][2] << '\t' << C2[i][0] << '\t' << C2[i][1] << '\t' << C2[i][2] << '\t' << pvalues[i] << '\t' << qvalues[i] << endl;
- }
-
- out << endl << endl;
- out.close();
-
-
-
- return 0;
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "runMetastats");
- exit(1);
- }
-}
-/***********************************************************/
-vector<double> MothurMetastats::permuted_pvalues(vector< vector<double> >& Imatrix, vector<double>& tstats, vector< vector<double> >& Fmatrix) {
- try {
- //# matrix stores tstats for each taxa(row) for each permuted trial(column)
- vector<double> ps; ps.resize(row, 0.0); //# to store the pvalues
- vector< vector<double> > permuted_ttests; permuted_ttests.resize(numPermutations);
- for (int i = 0; i < numPermutations; i++) { permuted_ttests[i].resize(row, 0.0); }
-
- //# calculate null version of tstats using B permutations.
- for (int i = 0; i < numPermutations; i++) {
- permuted_ttests[i] = permute_and_calc_ts(Imatrix);
- }
-
- //# calculate each pvalue using the null ts
- if ((secondGroupingStart) < 8 || (column-secondGroupingStart) < 8){
- vector< vector<double> > cleanedpermuted_ttests; cleanedpermuted_ttests.resize(numPermutations); //# the array pooling just the frequently observed ts
- //# then pool the t's together!
- //# count how many high freq taxa there are
- int hfc = 1;
- for (int i = 0; i < row; i++) { // # for each taxa
- double group1Total = 0.0; double group2Total = 0.0;
- for(int j = 0; j < secondGroupingStart; j++) { group1Total += Fmatrix[i][j]; }
- for(int j = secondGroupingStart; j < column; j++) { group2Total += Fmatrix[i][j]; }
-
- if (group1Total >= secondGroupingStart || group2Total >= (column-secondGroupingStart)){
- hfc++;
- for (int j = 0; j < numPermutations; j++) { cleanedpermuted_ttests[j].push_back(permuted_ttests[j][i]); }
- }
- }
-
- //#now for each taxa
- for (int i = 0; i < row; i++) {
- //number of cleanedpermuted_ttests greater than tstat[i]
- int numGreater = 0;
- for (int j = 0; j < numPermutations; j++) {
- for (int k = 0; k < hfc; k++) {
- if (cleanedpermuted_ttests[j][k] > abs(tstats[i])) { numGreater++; }
- }
- }
-
- ps[i] = (1/(double)(numPermutations*hfc))*numGreater;
- }
- }else{
- for (int i = 0; i < row; i++) {
- //number of permuted_ttests[i] greater than tstat[i] //(sum(permuted_ttests[i,] > abs(tstats[i]))+1)
- int numGreater = 1;
- for (int j = 0; j < numPermutations; j++) { if (permuted_ttests[j][i] > abs(tstats[i])) { numGreater++; } }
- ps[i] = (1/(double)(numPermutations+1))*numGreater;
- }
- }
-
- return ps;
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "permuted_pvalues");
- exit(1);
- }
-}
-/***********************************************************/
-vector<double> MothurMetastats::permute_and_calc_ts(vector< vector<double> >& Imatrix) {
- try {
- vector< vector<double> > permutedMatrix = Imatrix;
-
- //randomize columns, ie group abundances.
- for (int i = 0; i < permutedMatrix.size(); i++) { random_shuffle(permutedMatrix[i].begin(), permutedMatrix[i].end()); }
-
- //calc ts
- vector< vector<double> > C1; C1.resize(row);
- for (int i = 0; i < row; i++) { C1[i].resize(3, 0.0); } // statistic profiles for class1 and class 2
- vector< vector<double> > C2; C2.resize(row); // mean[1], variance[2], standard error[3]
- for (int i = 0; i < row; i++) { C2[i].resize(3, 0.0); }
- vector<double> Ts; Ts.resize(row, 0.0); // a place to store the true t-statistics
-
- //#*************************************
- //# generate statistics mean, var, stderr
- //#*************************************
- for(int i = 0; i < row; i++){ // for each taxa
- //# find the mean of each group
- double g1Total = 0.0; double g2Total = 0.0;
- for (int j = 0; j < secondGroupingStart; j++) { g1Total += permutedMatrix[i][j]; }
- C1[i][0] = g1Total/(double)(secondGroupingStart);
- for (int j = secondGroupingStart; j < column; j++) { g2Total += permutedMatrix[i][j]; }
- C2[i][0] = g2Total/(double)(column-secondGroupingStart);
-
- //# find the variance of each group
- double g1Var = 0.0; double g2Var = 0.0;
- for (int j = 0; j < secondGroupingStart; j++) { g1Var += pow((permutedMatrix[i][j]-C1[i][0]), 2); }
- C1[i][1] = g1Var/(double)(secondGroupingStart-1);
- for (int j = secondGroupingStart; j < column; j++) { g2Var += pow((permutedMatrix[i][j]-C2[i][0]), 2); }
- C2[i][1] = g2Var/(double)(column-secondGroupingStart-1);
-
- //# find the std error of each group -std err^2 (will change to std err at end)
- C1[i][2] = C1[i][1]/(double)(secondGroupingStart);
- C2[i][2] = C2[i][1]/(double)(column-secondGroupingStart);
- }
-
- //#*************************************
- //# two sample t-statistics
- //#*************************************
- for(int i = 0; i < row; i++){ // # for each taxa
- double xbar_diff = C1[i][0] - C2[i][0];
- double denom = sqrt(C1[i][2] + C2[i][2]);
- Ts[i] = abs(xbar_diff/denom); // calculate two sample t-statistic
- }
-
- return Ts;
-
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "permuted_ttests");
- exit(1);
- }
-}
-/***********************************************************/
-vector<double> MothurMetastats::calc_qvalues(vector<double>& pValues) {
- try {
-
- /* cout << "x <- c(" << pValues[0];
- for (int l = 1; l < pValues.size(); l++){
- cout << ", " << pValues[l];
- }
- cout << ")\n";*/
-
- int numRows = pValues.size();
- vector<double> qvalues(numRows, 0.0);
-
- //fill lambdas with 0.00, 0.01, 0.02... 0.95
- vector<double> lambdas(96, 0);
- for (int i = 1; i < lambdas.size(); i++) { lambdas[i] = lambdas[i-1] + 0.01; }
-
- vector<double> pi0_hat(lambdas.size(), 0);
-
- //calculate pi0_hat
- for (int l = 0; l < lambdas.size(); l++){ // for each lambda value
- int count = 0;
- for (int i = 0; i < numRows; i++){ // for each p-value in order
- if (pValues[i] > lambdas[l]){ count++; }
- }
- pi0_hat[l] = count/(double)(numRows*(1-lambdas[l]));
- }
-
- double pi0 = smoothSpline(lambdas, pi0_hat, 3);
-
- //order p-values
- vector<double> ordered_qs = qvalues;
- vector<int> ordered_ps(pValues.size(), 0);
- for (int i = 1; i < ordered_ps.size(); i++) { ordered_ps[i] = ordered_ps[i-1] + 1; }
- vector<double> tempPvalues = pValues;
- OrderPValues(0, numRows-1, tempPvalues, ordered_ps);
-
- ordered_qs[numRows-1] = min((pValues[ordered_ps[numRows-1]]*pi0), 1.0);
- for (int i = (numRows-2); i >= 0; i--){
- double p = pValues[ordered_ps[i]];
- double temp = p*numRows*pi0/(double)(i+1);
-
- ordered_qs[i] = min(temp, ordered_qs[i+1]);
- }
-
- //re-distribute calculated qvalues to appropriate rows
- for (int i = 0; i < numRows; i++){
- qvalues[ordered_ps[i]] = ordered_qs[i];
- }
-
- return qvalues;
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "calc_qvalues");
- exit(1);
- }
-}
-/***********************************************************/
-int MothurMetastats::OrderPValues(int low, int high, vector<double>& p, vector<int>& order) {
- try {
-
- if (low < high) {
- int i = low+1;
- int j = high;
- int pivot = (low+high) / 2;
-
- swapElements(low, pivot, p, order); //puts pivot in final spot
-
- /* compare value */
- double key = p[low];
-
- /* partition */
- while(i <= j) {
- /* find member above ... */
- while((i <= high) && (p[i] <= key)) { i++; }
-
- /* find element below ... */
- while((j >= low) && (p[j] > key)) { j--; }
-
- if(i < j) {
- swapElements(i, j, p, order);
- }
- }
-
- swapElements(low, j, p, order);
-
- /* recurse */
- OrderPValues(low, j-1, p, order);
- OrderPValues(j+1, high, p, order);
- }
-
- return 0;
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "OrderPValues");
- exit(1);
- }
-}
-/***********************************************************/
-int MothurMetastats::swapElements(int i, int j, vector<double>& p, vector<int>& order) {
- try {
-
- double z = p[i];
- p[i] = p[j];
- p[j] = z;
-
- int temp = order[i];
- order[i] = order[j];
- order[j] = temp;
-
- return 0;
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "swapElements");
- exit(1);
- }
-}
-/***********************************************************/
-double MothurMetastats::smoothSpline(vector<double>& x, vector<double>& y, int df) {
- try {
-
- double result = 0.0;
- int n = x.size();
- vector<double> w(n, 1);
- double* xb = new double[n];
- double* yb = new double[n];
- double* wb = new double[n];
- double yssw = 0.0;
- for (int i = 0; i < n; i++) {
- wb[i] = w[i];
- yb[i] = w[i]*y[i];
- yssw += (w[i] * y[i] * y[i]) - wb[i] * (yb[i] * yb[i]);
- xb[i] = (x[i] - x[0]) / (x[n-1] - x[0]);
- }
-
- vector<double> knot = sknot1(xb, n);
- int nk = knot.size() - 4;
-
- double low = -1.5; double high = 1.5; double tol = 1e-04; double eps = 2e-08; int maxit = 500;
- int ispar = 0; int icrit = 3; double dofoff = 3.0;
- double penalty = 1.0;
- int ld4 = 4; int isetup = 0; int ldnk = 1; int ier; double spar = 1.0; double crit;
-
- double* knotb = new double[knot.size()];
- double* coef1 = new double[nk];
- double* lev1 = new double[n];
- double* sz1 = new double[n];
- for (int i = 0; i < knot.size(); i++) { knotb[i] = knot[i]; }
-
- Spline spline;
- spline.sbart(&penalty, &dofoff, &xb[0], &yb[0], &wb[0], &yssw, &n, &knotb[0], &nk, &coef1[0], &sz1[0], &lev1[0], &crit,
- &icrit, &spar, &ispar, &maxit, &low, &high, &tol, &eps, &isetup, &ld4, &ldnk, &ier);
-
- result = coef1[nk-1];
-
- //free memory
- delete [] xb;
- delete [] yb;
- delete [] wb;
- delete [] knotb;
- delete [] coef1;
- delete [] lev1;
- delete [] sz1;
-
- return result;
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "smoothSpline");
- exit(1);
- }
-}
-/***********************************************************/
-vector<double> MothurMetastats::sknot1(double* x, int n) {
- try {
- vector<double> knots;
- int nk = nkn(n);
-
- //R equivalent - rep(x[1L], 3L)
- knots.push_back(x[0]); knots.push_back(x[0]); knots.push_back(x[0]);
-
- //generate a sequence of nk equally spaced values from 1 to n. R equivalent = seq.int(1, n, length.out = nk)
- vector<int> indexes = getSequence(0, n-1, nk);
- for (int i = 0; i < indexes.size(); i++) { knots.push_back(x[indexes[i]]); }
-
- //R equivalent - rep(x[n], 3L)
- knots.push_back(x[n-1]); knots.push_back(x[n-1]); knots.push_back(x[n-1]);
-
- return knots;
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "sknot1");
- exit(1);
- }
-}
-/***********************************************************/
-vector<int> MothurMetastats::getSequence(int start, int end, int length) {
- try {
- vector<int> sequence;
- double increment = (end-start) / (double) (length-1);
-
- sequence.push_back(start);
- for (int i = 1; i < length-1; i++) {
- sequence.push_back(int(i*increment));
- }
- sequence.push_back(end);
-
- return sequence;
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "getSequence");
- exit(1);
- }
-}
-/***********************************************************/
-//not right, havent fully figured out the variable types yet...
-int MothurMetastats::nkn(int n) {
- try {
-
- if (n < 50) { return n; }
- else {
- double a1 = log2(50);
- double a2 = log2(100);
- double a3 = log2(140);
- double a4 = log2(200);
-
- if (n < 200) {
- return (int)pow(2.0, (a1 + (a2 - a1) * (n - 50)/(float)150));
- }else if (n < 800) {
- return (int)pow(2.0, (a2 + (a3 - a2) * (n - 200)/(float)600));
- }else if (n < 3200) {
- return (int)pow(2.0, (a3 + (a4 - a3) * (n - 800)/(float)2400));
- }else {
- return (int)pow((double)(200 + (n - 3200)), 0.2);
- }
- }
-
- return 0;
-
- }catch(exception& e) {
- m->errorOut(e, "MothurMetastats", "nkn");
- exit(1);
- }
-}
-/***********************************************************/
-
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projects / mothur.git / blobdiff