//**********************************************************************************************************************
vector<string> CooccurrenceCommand::setParameters() {
try {
- CommandParameter pshared("shared", "InputTypes", "", "", "none", "none", "none",false,true); parameters.push_back(pshared);
- CommandParameter pmetric("metric", "Multiple", "cscore-checker-combo-vratio", "cscore", "", "", "",false,false); parameters.push_back(pmetric);
- CommandParameter pmatrix("matrixmodel", "Multiple", "sim1-sim2-sim3-sim4-sim5-sim6-sim7-sim8-sim9", "sim2", "", "", "",false,false); parameters.push_back(pmatrix);
- CommandParameter pruns("iters", "Number", "", "1000", "", "", "",false,false); parameters.push_back(pruns);
- CommandParameter pinputdir("inputdir", "String", "", "", "", "", "",false,false); parameters.push_back(pinputdir);
- CommandParameter poutputdir("outputdir", "String", "", "", "", "", "",false,false); parameters.push_back(poutputdir);
- CommandParameter plabel("label", "String", "", "", "", "", "",false,false); parameters.push_back(plabel);
- CommandParameter pgroups("groups", "String", "", "", "", "", "",false,false); parameters.push_back(pgroups);
+ CommandParameter pshared("shared", "InputTypes", "", "", "none", "none", "none","summary",false,true,true); parameters.push_back(pshared);
+ CommandParameter pmetric("metric", "Multiple", "cscore-checker-combo-vratio", "cscore", "", "", "","",false,false); parameters.push_back(pmetric);
+ CommandParameter pmatrix("matrixmodel", "Multiple", "sim1-sim2-sim3-sim4-sim5-sim6-sim7-sim8-sim9", "sim2", "", "", "","",false,false); parameters.push_back(pmatrix);
+ CommandParameter pruns("iters", "Number", "", "1000", "", "", "","",false,false); parameters.push_back(pruns);
+ CommandParameter pinputdir("inputdir", "String", "", "", "", "", "","",false,false); parameters.push_back(pinputdir);
+ CommandParameter poutputdir("outputdir", "String", "", "", "", "", "","",false,false); parameters.push_back(poutputdir);
+ CommandParameter plabel("label", "String", "", "", "", "", "","",false,false); parameters.push_back(plabel);
+ CommandParameter pgroups("groups", "String", "", "", "", "", "","",false,false); parameters.push_back(pgroups);
vector<string> myArray;
for (int i = 0; i < parameters.size(); i++) { myArray.push_back(parameters[i].name); }
}
}
//**********************************************************************************************************************
+string CooccurrenceCommand::getOutputPattern(string type) {
+ try {
+ string pattern = "";
+
+ if (type == "summary") { pattern = "[filename],cooccurence.summary"; }
+ else { m->mothurOut("[ERROR]: No definition for type " + type + " output pattern.\n"); m->control_pressed = true; }
+
+ return pattern;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "CooccurrenceCommand", "getOutputPattern");
+ exit(1);
+ }
+}
+//**********************************************************************************************************************
CooccurrenceCommand::CooccurrenceCommand(){
try {
abort = true; calledHelp = true;
set<string> userLabels = labels;
ofstream out;
- string outputFileName = outputDir + m->getRootName(m->getSimpleName(sharedfile)) + "cooccurence.summary";
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(sharedfile));
+ string outputFileName = getOutputFileName("summary", variables);
m->openOutputFile(outputFileName, out);
outputNames.push_back(outputFileName); outputTypes["summary"].push_back(outputFileName);
out.setf(ios::fixed, ios::floatfield); out.setf(ios::showpoint);
- out << "metric\tlabel\tScore\tpValue\n";
+ out << "metric\tlabel\tScore\tzScore\tstandardDeviation\tnp_Pvalue\n";
//as long as you are not at the end of the file or done wih the lines you want
while((lookup[0] != NULL) && ((allLines == 1) || (userLabels.size() != 0))) {
//**********************************************************************************************************************
int CooccurrenceCommand::getCooccurrence(vector<SharedRAbundVector*>& thisLookUp, ofstream& out){
- try {
+ try {
int numOTUS = thisLookUp[0]->getNumBins();
- vector< vector<int> > initmatrix; initmatrix.resize(thisLookUp.size());
+
+ if(numOTUS < 2) {
+ m->mothurOut("Not enough OTUs for co-occurrence analysis, skipping"); m->mothurOutEndLine();
+ return 0;
+ }
+
vector< vector<int> > co_matrix; co_matrix.resize(thisLookUp[0]->getNumBins());
for (int i = 0; i < thisLookUp[0]->getNumBins(); i++) { co_matrix[i].resize((thisLookUp.size()), 0); }
- for (int i = 0; i < thisLookUp.size(); i++) { initmatrix[i].resize((thisLookUp[i]->getNumBins()), 0); }
vector<int> columntotal; columntotal.resize(thisLookUp.size(), 0);
vector<int> rowtotal; rowtotal.resize(numOTUS, 0);
- for (int i = 0; i < thisLookUp.size(); i++) {
- for (int j = 0; j < thisLookUp[i]->getNumBins(); j++) {
- if (m->control_pressed) { return 0; }
- int abund = thisLookUp[i]->getAbundance(j);
-
- if(abund > 0) {
- initmatrix[i][j] = 1;
+ for (int i = 0; i < thisLookUp.size(); i++) { //nrows in the shared file
+ for (int j = 0; j < thisLookUp[i]->getNumBins(); j++) { //cols of original shared file
+ if (m->control_pressed) { return 0; }
+ int abund = thisLookUp[i]->getAbundance(j);
+
+ if(abund > 0) {
co_matrix[j][i] = 1;
rowtotal[j]++;
columntotal[i]++;
- }
- }
+ }
+ }
}
//nrows is ncols of inital matrix. All the functions need this value. They assume the transposition has already taken place and nrows and ncols refer to that matrix.
int nrows = numOTUS;//rows of inital matrix
int ncols = thisLookUp.size();//groups
double initscore = 0.0;
-
- vector<int> initcolumntotal; initcolumntotal.resize(ncols, 0);
- vector<int> initrowtotal; initrowtotal.resize(nrows, 0);
+
vector<double> stats;
-
+ vector<double> probabilityMatrix; probabilityMatrix.resize(ncols * nrows, 0);
+ vector<vector<int> > nullmatrix(nrows, vector<int>(ncols, 0));
+
TrialSwap2 trial;
- initcolumntotal = rowtotal;
- initrowtotal = columntotal;
- trial.update_row_col_totals(co_matrix, rowtotal, columntotal);
+ int n = accumulate( columntotal.begin(), columntotal.end(), 0 );
- if (metric == "cscore") { initscore = trial.calc_c_score(co_matrix, rowtotal); }
- else if (metric == "checker") { initscore = trial.calc_checker(co_matrix, rowtotal); }
- else if (metric == "vratio") { initscore = trial.calc_vratio(rowtotal, columntotal); }
- else if (metric == "combo") { initscore = trial.calc_combo(co_matrix); }
- else { m->mothurOut("[ERROR]: No metric selected!\n"); m->control_pressed = true; return 1; }
+ //============================================================
- m->mothurOut("Initial c score: " + toString(initscore)); m->mothurOutEndLine();
+ //generate a probability matrix. Only do this once.
+ float start = 0.0;
- //nullmatrix burn in
- for(int i=0;i<10000;i++) {
- if (m->control_pressed) { return 0; }
- if (matrix == "sim1") {
- trial.sim1(co_matrix);
- }else if (matrix == "sim2") {
- trial.sim2(co_matrix);
- }else if (matrix == "sim3") {
- trial.sim3(initmatrix);
- co_matrix = initmatrix;
- }else if (matrix == "sim4") {
- trial.sim4(columntotal, rowtotal, co_matrix);
- }else if (matrix == "sim5") {
- trial.sim5(initcolumntotal, initrowtotal, initmatrix);
- trial.transpose_matrix(initmatrix,co_matrix);
- }else if (matrix == "sim6") {
- trial.sim6(columntotal, co_matrix);
- }else if (matrix == "sim7") {
- trial.sim7(initcolumntotal, initmatrix);
- co_matrix = initmatrix;
- }else if (matrix == "sim8") {
- trial.sim8(columntotal, rowtotal, co_matrix);
- }else if (matrix == "sim9") {
- trial.swap_checkerboards (co_matrix);
- }else{
- m->mothurOut("[ERROR]: No model selected! \n");
- m->control_pressed = true;
+ if (matrix == "sim1") {
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + 1/double(nrows*ncols);
+ start = start + 1/double(nrows*ncols);
+ }
}
}
-
- //run
- for(int i=0;i<runs;i++) {
- if (m->control_pressed) { return 0; }
- //calc metric of nullmatrix
- if (matrix == "sim1") {
- trial.sim1(co_matrix);
- }else if (matrix == "sim2") {
- trial.sim2(co_matrix);
- }else if (matrix == "sim3") {
- trial.sim3(initmatrix);
- co_matrix = initmatrix;
- }else if (matrix == "sim4") {
- trial.sim4(columntotal, rowtotal, co_matrix);
- }else if (matrix == "sim5") {
- trial.sim5(initcolumntotal, initrowtotal, initmatrix);
- trial.transpose_matrix(initmatrix,co_matrix);
- }else if (matrix == "sim6") {
- trial.sim6(columntotal, co_matrix);
- }else if (matrix == "sim7") {
- trial.sim7(initcolumntotal, initmatrix);
- co_matrix = initmatrix;
- }else if (matrix == "sim8") {
- trial.sim8(columntotal, rowtotal, co_matrix);
- }else if (matrix == "sim9") {
- trial.swap_checkerboards (co_matrix);
- }else{
- m->mothurOut("[ERROR]: No model selected! \n");
- m->control_pressed = true;
+ //don't need a prob matrix because we just shuffle the rows, may use this in the future
+ else if (matrix == "sim2") { }
+// for(int i=0;i<nrows;i++) {
+// start = 0.0;
+// for(int j=0;j<ncols;j++) {
+// probabilityMatrix[ncols * i + j] = start + 1/double(ncols);
+// start = start + 1/double(ncols);
+// }
+// }
+// }
+
+ else if (matrix == "sim3") {
+ for(int j=0;j<ncols;j++) {
+ start = 0.0;
+ for(int i=0;i<nrows;i++) {
+ probabilityMatrix[ncols * i + j] = start + 1/double(nrows);
+ start = start + 1/double(nrows);
+ }
+ }
+ }
+
+ else if (matrix == "sim4") {
+ for(int i=0;i<nrows;i++) {
+ start = 0.0;
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + columntotal[j]/double(n);
+ start = start + columntotal[j]/double(n);
+ }
+ }
+ }
+
+ else if (matrix == "sim5") {
+ for(int j=0;j<ncols;j++) {
+ start = 0.0;
+ for(int i=0;i<nrows;i++) {
+ probabilityMatrix[ncols * i + j] = start + rowtotal[i]/double(n);
+ start = start + rowtotal[i]/double(n);
+ }
+ }
+ }
+
+ else if (matrix == "sim6") {
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + columntotal[j]/double(n*nrows);
+ start = start + columntotal[j]/double(n*nrows);
+ }
+ }
+ }
+
+
+ else if (matrix == "sim7") {
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + rowtotal[i]/double(n*ncols);
+ start = start + rowtotal[i]/double(n*ncols);
+ }
+ }
+ }
+
+ else if (matrix == "sim8") {
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + (rowtotal[i]*columntotal[j])/double(n*n);
+ start = start + (rowtotal[i]*columntotal[j])/double(n*n);
+ }
+ }
+ }
+ else if (matrix == "sim9" || matrix == "sim2") { }
+ else {
+ m->mothurOut("[ERROR]: No model selected! \n");
+ m->control_pressed = true;
+ }
+
+
+ //co_matrix is the transposed shared file, initmatrix is the original shared file
+ if (metric == "cscore") { initscore = trial.calc_c_score(co_matrix, rowtotal, ncols, nrows); }
+ else if (metric == "checker") { initscore = trial.calc_checker(co_matrix, rowtotal, ncols, nrows); }
+ else if (metric == "vratio") { initscore = trial.calc_vratio(nrows, ncols, rowtotal, columntotal); }
+ else if (metric == "combo") { initscore = trial.calc_combo(nrows, ncols, co_matrix); }
+ else { m->mothurOut("[ERROR]: No metric selected!\n"); m->control_pressed = true; return 1; }
+
+ m->mothurOut("Initial c score: " + toString(initscore)); m->mothurOutEndLine();
+
+ double previous;
+ double current;
+ double randnum;
+ int count;
+
+ //burn-in for sim9
+ if(matrix == "sim9") {
+ for(int i=0;i<10000;i++) trial.swap_checkerboards (co_matrix, ncols, nrows);
+ }
+
+ //populate null matrix from probability matrix, do this a lot.
+ for(int k=0;k<runs;k++){
+ nullmatrix.clear();
+ //zero-fill the null matrix
+ nullmatrix.assign(nrows, vector<int>(ncols, 0));
+
+ if(matrix == "sim1" || matrix == "sim6" || matrix == "sim8" || matrix == "sim7") {
+ count = 0;
+ while(count < n) {
+ if (m->control_pressed) { return 0; }
+ nextnum2:
+ previous = 0.0;
+ randnum = rand() / double(RAND_MAX);
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ current = probabilityMatrix[ncols * i + j];
+ if(randnum <= current && randnum > previous) {
+ nullmatrix[i][j] = 1;
+ count++;
+ if (count > n) break;
+ else
+ goto nextnum2;
+ }
+ previous = current;
+ }
+ }
+ }
+ }
+
+ else if (matrix == "sim2") {
+ for(int i=0;i<nrows;i++) {
+ random_shuffle( co_matrix[i].begin(), co_matrix[i].end() );
+ }
+ //do this for the scoring since those all have nullmatrix as a parameter
+ //nullmatrix gets cleared at the begining of each run
+ nullmatrix = co_matrix;
+ }
+
+ else if(matrix == "sim4") {
+ for(int i=0;i<nrows;i++) {
+ count = 0;
+ while(count < rowtotal[i]) {
+ previous = 0.0;
+ if (m->control_pressed) { return 0; }
+ randnum = rand() / double(RAND_MAX);
+ for(int j=0;j<ncols;j++) {
+ current = probabilityMatrix[ncols * i + j];
+ if(randnum <= current && randnum > previous && nullmatrix[i][j] != 1) {
+ nullmatrix[i][j] = 1;
+ count++;
+ previous = 0.0;
+ break;
+ }
+ previous = current;
+ }
+ }
+ }
+ }
+
+ else if(matrix == "sim3" || matrix == "sim5") {
+ //columns
+ for(int j=0;j<ncols;j++) {
+ count = 0;
+ while(count < columntotal[j]) {
+ if (m->control_pressed) { return 0; }
+ randnum = rand() / double(RAND_MAX);
+ for(int i=0;i<nrows;i++) {
+ current = probabilityMatrix[ncols * i + j];
+ if(randnum <= current && randnum > previous && nullmatrix[i][j] != 1) {
+ nullmatrix[i][j] = 1;
+ count++;
+ previous = 0.0;
+ break;
+ }
+ previous = current;
+ }
+ }
+ }
+ }
+
+ //swap_checkerboards takes the original matrix and swaps checkerboards
+ else if(matrix == "sim9") {
+ trial.swap_checkerboards (co_matrix, ncols, nrows);
+ nullmatrix = co_matrix;
+ }
+ else {
+ m->mothurOut("[ERROR]: No null model selected!\n\n"); m->control_pressed = true;
+ return 1;
}
- //
- //
- trial.update_row_col_totals(co_matrix, rowtotal, columntotal);
- if (metric == "cscore") {
- stats.push_back(trial.calc_c_score(co_matrix, rowtotal));
- }else if (metric == "checker") {
- stats.push_back(trial.calc_checker(co_matrix, rowtotal));
- }else if (metric == "vratio") {
- stats.push_back(trial.calc_vratio(rowtotal, columntotal));
- }else if (metric == "combo") {
- stats.push_back(trial.calc_combo(co_matrix));
- }else {
- m->mothurOut("[ERROR]: No metric selected!\n");
- m->control_pressed = true;
+ //run metric on null matrix and add score to the stats vector
+ if (metric == "cscore"){
+ stats.push_back(trial.calc_c_score(nullmatrix, rowtotal, ncols, nrows));
+ }
+ else if (metric == "checker") {
+ stats.push_back(trial.calc_checker(nullmatrix, rowtotal, ncols, nrows));
+ }
+ else if (metric == "vratio") {
+ stats.push_back(trial.calc_vratio(nrows, ncols, rowtotal, columntotal));
+ }
+ else if (metric == "combo") {
+ stats.push_back(trial.calc_combo(nrows, ncols, nullmatrix));
+ }
+ else {
+ m->mothurOut("[ERROR]: No metric selected!\n\n"); m->control_pressed = true;
return 1;
}
}
-
+
+
+
double total = 0.0;
- for (int i=0; i<stats.size();i++) { total+=stats[i]; }
+ for (int i=0; i<stats.size();i++) { total+=stats[i]; }
- double nullMean = double (total/(double)stats.size());
+ double nullMean = double (total/(double)stats.size());
m->mothurOutEndLine(); m->mothurOut("average metric score: " + toString(nullMean)); m->mothurOutEndLine();
+ //calc_p_value is not a statistical p-value, it's just the average that are either > or < the initscore.
+ //All it does is show what is expected in a competitively structured community
+ //zscore is output so p-value can be looked up in a ztable
double pvalue = 0.0;
- if (metric == "cscore" || metric == "checker") { pvalue = trial.calc_pvalue_greaterthan (stats, initscore); }
- else{ pvalue = trial.calc_pvalue_lessthan (stats, initscore); }
+ if (metric == "cscore" || metric == "checker") { pvalue = trial.calc_pvalue_greaterthan (stats, initscore); }
+ else{ pvalue = trial.calc_pvalue_lessthan (stats, initscore); }
+
+ double sd = trial.getSD(runs, stats, nullMean);
+
+ double zscore = trial.get_zscore(sd, nullMean, initscore);
- m->mothurOut("pvalue: " + toString(pvalue)); m->mothurOutEndLine();
- out << metric << '\t' << thisLookUp[0]->getLabel() << '\t' << nullMean << '\t' << pvalue << endl;
+ m->mothurOut("zscore: " + toString(zscore)); m->mothurOutEndLine();
+ m->mothurOut("standard deviation: " + toString(sd)); m->mothurOutEndLine();
+ m->mothurOut("non-parametric p-value: " + toString(pvalue)); m->mothurOutEndLine();
+ out << metric << '\t' << thisLookUp[0]->getLabel() << '\t' << nullMean << '\t' << zscore << '\t' << sd << '\t' << pvalue << endl;
return 0;
- }
- catch(exception& e) {
- m->errorOut(e, "CooccurrenceCommand", "Cooccurrence");
- exit(1);
- }
+ }
+ catch(exception& e) {
+ m->errorOut(e, "CooccurrenceCommand", "Cooccurrence");
+ exit(1);
+ }
}
//**********************************************************************************************************************
projects / mothur.git / blobdiff