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/*
* mothurmetastats.cpp
* Mothur
*
* Created by westcott on 7/6/11.
* Copyright 2011 Schloss Lab. All rights reserved.
*
*/
#include "mothurmetastats.h"
#include "mothurfisher.h"
#include "utils.hpp"
/***********************************************************/
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() = default;
/***********************************************************/
//main metastats function
int MothurMetastats::runMetastats(string outputFileName, vector< vector<double> >& data, int secGroupingStart, vector<string> currentLabels, bool fillProps) {
try {
numOTUs = data.size(); //numBins
numSamples = data[0].size(); //numGroups in subset
secondGroupingStart = secGroupingStart; //g number of samples in group 1
vector< vector<double> > Pmatrix; Pmatrix.resize(numOTUs);
for (int i = 0; i < numOTUs; i++) { Pmatrix[i].resize(numSamples, 0.0); } // the relative proportion matrix
vector< vector<double> > C1; C1.resize(numOTUs);
for (int i = 0; i < numOTUs; i++) { C1[i].resize(3, 0.0); } // statistic profiles for class1 and class 2
vector< vector<double> > C2; C2.resize(numOTUs); // mean[1], variance[2], standard error[3]
for (int i = 0; i < numOTUs; i++) { C2[i].resize(3, 0.0); }
vector<double> T_statistics; T_statistics.resize(numOTUs, 1); // a place to store the true t-statistics
vector<double> pvalues; pvalues.resize(numOTUs, 1); // place to store pvalues
//*************************************
// convert to proportions
// generate Pmatrix
//*************************************
vector<double> totals; totals.resize(numSamples, 0); // sum of numSampless / samples -> numSeqs for each sample
//total[i] = total abundance for group[i]
for (int i = 0; i < numSamples; i++) { //each sample
for (int j = 0; j < numOTUs; j++) { //each otu
totals[i] += data[j][i];
}
}
for (int i = 0; i < numSamples; i++) { //sample
for (int j = 0; j < numOTUs; j++) { //otu
if (fillProps) { Pmatrix[j][i] = data[j][i]/totals[i]; }
else { Pmatrix[j][i] = data[j][i]; }
}
}
//#********************************************************************************
//# ************************** STATISTICAL TESTING ********************************
//#********************************************************************************
if (numSamples == 2){ //# then we have a two sample comparison
//#************************************************************
//# generate p values fisher's exact test
//#************************************************************
double total1, total2; total1 = 0; total2 = 0;
//total for first grouping
for (int i = 0; i < secondGroupingStart; i++) { total1 += totals[i]; }
//total for second grouping
for (int i = secondGroupingStart; i < numSamples; i++) { total2 += totals[i]; }
vector<double> fish; fish.resize(numOTUs, 0.0);
vector<double> fish2; fish2.resize(numOTUs, 0.0);
//vector<string> currentLabels = m->getCurrentSharedBinLabels();
for(int i = 0; i < numOTUs; i++){ //numBins
for(int j = 0; j < secondGroupingStart; j++) { fish[i] += data[i][j]; }
for(int j = secondGroupingStart; j < numSamples; 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];
if (fillProps) { f11 = floor(f11); f12 = floor(f12); f21 = floor(f21); f22 = floor(f22); }
MothurFisher fisher;
double pre = fisher.fexact(f11, f12, f21, f22, currentLabels[i]);
if (pre > 0.999999999) { pre = 1.0; }
if (m->getControl_pressed()) { return 1; }
pvalues[i] = pre;
}
}else { //we have multiple subjects per population
//#*************************************
//# generate statistics mean, var, stderr
//#*************************************
for(int i = 0; i < numOTUs; 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 < numSamples; j++) { g2Total += Pmatrix[i][j]; }
C2[i][0] = g2Total/(double)(numSamples-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 < numSamples; j++) { g2Var += pow((Pmatrix[i][j]-C2[i][0]), 2); }
C2[i][1] = g2Var/(double)(numSamples-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)(numSamples-secondGroupingStart);
}
//#*************************************
//# two sample t-statistics
//#*************************************
for(int i = 0; i < numOTUs; 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
}
if (m->getDebug()) {
for (int i = 0; i < numOTUs; 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;
}
for (int i = 0; i < numOTUs; i++) {
for (int j = 0; j < numSamples; j++) {
cout << "Fmatrix[" << i+1 << "," << j+1 << "]=" << data[i][j] << ";" << endl;
}
}
}
//#*************************************
//# generate initial permuted p-values
//#*************************************
pvalues = permuted_pvalues(Pmatrix, T_statistics, data);
if (m->getDebug()) { for (int i = 0; i < numOTUs; i++) { m->mothurOut("[DEBUG]: " + currentLabels[i] + " pvalue = " + toString(pvalues[i]) + "\n"); } }
//#*************************************
//# generate p values for sparse data
//# using fisher's exact test
//#*************************************
double total1, total2; total1 = 0; total2 = 0;
//total for first grouping
for (int i = 0; i < secondGroupingStart; i++) { total1 += totals[i]; } //total all seqs in first set
//total for second grouping
for (int i = secondGroupingStart; i < numSamples; i++) { total2 += totals[i]; } //total all seqs in second set
vector<double> fish; fish.resize(numOTUs, 0.0);
vector<double> fish2; fish2.resize(numOTUs, 0.0);
for(int i = 0; i < numOTUs; i++){ //numBins
for(int j = 0; j < secondGroupingStart; j++) { fish[i] += data[i][j]; }
for(int j = secondGroupingStart; j < numSamples; j++) { fish2[i] += data[i][j]; }
if ((fish[i] < secondGroupingStart) && (fish2[i] < (numSamples-secondGroupingStart))) {
double f11, f12, f21, f22;
f11 = fish[i]; if (f11 < 0) { f11 *= -1.0; } f11 = floor(f11);
f12 = fish2[i]; if (f12 < 0) { f12 *= -1.0; } f12 = floor(f11);
f21 = total1 - fish[i]; if (f21 < 0) { f21 *= -1.0; } f21 = floor(f21);
f22 = total2 - fish2[i]; if (f22 < 0) { f22 *= -1.0; } f22 = floor(f22);
if (fillProps) { f11 = floor(f11); f12 = floor(f12); f21 = floor(f21); f22 = floor(f22); }
MothurFisher fisher;
if (m->getDebug()) { m->mothurOut("[DEBUG]: about to run fisher for Otu " + currentLabels[i] + " F11, F12, F21, F22 = " + toString(f11) + " " + toString(f12) + " " + toString(f21) + " " + toString(f22) + " " + "\n"); }
double pre = fisher.fexact(f11, f12, f21, f22, currentLabels[i]);
if (m->getDebug()) { m->mothurOut("[DEBUG]: about to completed fisher for Otu " + currentLabels[i] + " pre = " + toString(pre) + "\n"); }
if (pre > 0.999999999) { pre = 1.0; }
if (m->getControl_pressed()) { return 1; }
pvalues[i] = pre;
}
}
//#*************************************
//# convert stderr^2 to std error
//#*************************************
for(int i = 0; i < numOTUs; 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(nullptr);
local = localtime(&t);
ofstream out;
Utils util; util.openOutputFile(outputFileName, out);
out.setf(ios::fixed, ios::floatfield); out.setf(ios::showpoint);
out << "Local time and date of test: " << asctime(local) << endl;
out << "# numOTUss = " << numOTUs << ", # col = " << numSamples << ", g = " << secondGroupingStart << endl << endl;
out << numPermutations << " permutations" << endl << endl;
//output numSamples headings - not really sure... documentation labels 9 numSampless, 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\n";
for(int i = 0; i < numOTUs; i++){
if (m->getControl_pressed()) { out.close(); return 0; }
//if there are binlabels use them otherwise count.
if (i < currentLabels.size()) { out << currentLabels[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] << endl;
//if (pvalues[i] < 0.05) { cout << currentLabels[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(numOTUs) for each permuted trial(numSamples)
vector<double> ps; ps.resize(numOTUs, 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(numOTUs, 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 || (numSamples-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 < numOTUs; 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 < numSamples; j++) { group2Total += Fmatrix[i][j]; }
if (group1Total >= secondGroupingStart || group2Total >= (numSamples-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 < numOTUs; i++) {
//number of cleanedpermuted_ttests greater than tstat[i]
int numGreater = 0;
for (int j = 0; j < numPermutations; j++) {
for (int k = 0; k < cleanedpermuted_ttests[j].size(); k++) {
if (cleanedpermuted_ttests[j][k] > abs(tstats[i])) { numGreater++; }
}
}
ps[i] = (1/(double)(numPermutations*hfc))*numGreater;
}
}else{
for (int i = 0; i < numOTUs; 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 numSampless, ie group abundances.
map<int, int> randomMap;
vector<int> randoms;
for (int i = 0; i < numSamples; i++) { randoms.push_back(i); }
util.mothurRandomShuffle(randoms);
for (int i = 0; i < randoms.size(); i++) { randomMap[i] = randoms[i]; }
//calc ts
vector< vector<double> > C1; C1.resize(numOTUs);
for (int i = 0; i < numOTUs; i++) { C1[i].resize(3, 0.0); } // statistic profiles for class1 and class 2
vector< vector<double> > C2; C2.resize(numOTUs); // mean[1], variance[2], standard error[3]
for (int i = 0; i < numOTUs; i++) { C2[i].resize(3, 0.0); }
vector<double> Ts; Ts.resize(numOTUs, 0.0); // a place to store the true t-statistics
//#*************************************
//# generate statistics mean, var, stderr
//#*************************************
for(int i = 0; i < numOTUs; 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][randomMap[j]]; }
C1[i][0] = g1Total/(double)(secondGroupingStart);
for (int j = secondGroupingStart; j < numSamples; j++) { g2Total += permutedMatrix[i][randomMap[j]]; }
C2[i][0] = g2Total/(double)(numSamples-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][randomMap[j]]-C1[i][0]), 2); }
C1[i][1] = g1Var/(double)(secondGroupingStart-1);
for (int j = secondGroupingStart; j < numSamples; j++) { g2Var += pow((permutedMatrix[i][randomMap[j]]-C2[i][0]), 2); }
C2[i][1] = g2Var/(double)(numSamples-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)(numSamples-secondGroupingStart);
}
//#*************************************
//# two sample t-statistics
//#*************************************
for(int i = 0; i < numOTUs; 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);
}
}
/***********************************************************/
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);
}
}
/***********************************************************/
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);
}
}
/***********************************************************/
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