1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
|
/*
* mgclustercommand.cpp
* Mothur
*
* Created by westcott on 12/11/09.
* Copyright 2009 Schloss Lab. All rights reserved.
*
*/
#include "mgclustercommand.h"
//**********************************************************************************************************************
vector<string> MGClusterCommand::setParameters(){
try {
CommandParameter pblast("blast", "InputTypes", "", "", "none", "none", "none","list",false,true,true); parameters.push_back(pblast);
CommandParameter pname("name", "InputTypes", "", "", "NameCount", "none", "ColumnName","rabund-sabund",false,false,true); parameters.push_back(pname);
CommandParameter pcount("count", "InputTypes", "", "", "NameCount", "none", "none","",false,false,true); parameters.push_back(pcount);
CommandParameter plength("length", "Number", "", "5", "", "", "","",false,false); parameters.push_back(plength);
CommandParameter ppenalty("penalty", "Number", "", "0.10", "", "", "","",false,false); parameters.push_back(ppenalty);
CommandParameter pcutoff("cutoff", "Number", "", "0.70", "", "", "","",false,false,true); parameters.push_back(pcutoff);
CommandParameter pprecision("precision", "Number", "", "100", "", "", "","",false,false); parameters.push_back(pprecision);
CommandParameter pmethod("method", "Multiple", "furthest-nearest-average-opti", "opti", "", "", "","",false,false); parameters.push_back(pmethod);
CommandParameter pinitialize("initialize", "Multiple", "oneotu-singleton", "singleton", "", "", "","",false,false,true); parameters.push_back(pinitialize);
CommandParameter pmetric("metric", "Multiple", "mcc-sens-spec-tptn-fpfn-tp-tn-fp-fn-f1score-accuracy-ppv-npv-fdr", "mcc", "", "", "","",false,false,true); parameters.push_back(pmetric);
CommandParameter pmetriccutoff("delta", "Number", "", "0.0001", "", "", "","",false,false,true); parameters.push_back(pmetriccutoff);
CommandParameter piters("iters", "Number", "", "100", "", "", "","",false,false,true); parameters.push_back(piters);
CommandParameter pmin("min", "Boolean", "", "T", "", "", "","",false,false); parameters.push_back(pmin);
CommandParameter pmerge("merge", "Boolean", "", "T", "", "", "","",false,false); parameters.push_back(pmerge);
CommandParameter padjust("adjust", "String", "", "F", "", "", "","",false,false); parameters.push_back(padjust);
CommandParameter phcluster("hcluster", "Boolean", "", "F", "", "", "","",false,false); parameters.push_back(phcluster);
CommandParameter pseed("seed", "Number", "", "0", "", "", "","",false,false); parameters.push_back(pseed);
CommandParameter pinputdir("inputdir", "String", "", "", "", "", "","",false,false); parameters.push_back(pinputdir);
CommandParameter poutputdir("outputdir", "String", "", "", "", "", "","",false,false); parameters.push_back(poutputdir);
abort = false; calledHelp = false;
vector<string> tempOutNames;
outputTypes["list"] = tempOutNames;
outputTypes["rabund"] = tempOutNames;
outputTypes["sabund"] = tempOutNames;
outputTypes["steps"] = tempOutNames;
outputTypes["sensspec"] = tempOutNames;
vector<string> myArray;
for (int i = 0; i < parameters.size(); i++) { myArray.push_back(parameters[i].name); }
return myArray;
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "setParameters");
exit(1);
}
}
//**********************************************************************************************************************
string MGClusterCommand::getHelpString(){
try {
string helpString = "";
helpString += "The mgcluster command parameter options are blast, name, cutoff, precision, method, metric, initialize, iters, merge, min, length, penalty and adjust. The blast parameter is required.\n";
helpString += "The mgcluster command reads a blast and name file and clusters the sequences into OPF units similar to the OTUs.\n";
helpString += "This command outputs a .list, .rabund and .sabund file that can be used with mothur other commands to estimate richness.\n";
helpString += "The cutoff parameter is used to specify the maximum distance you would like to cluster to. The default is 0.70.\n";
helpString += "The precision parameter's default value is 100. \n";
helpString += "The acceptable mgcluster methods are furthest, nearest, average and opti. If no method is provided then opti is assumed.\n";
helpString += "The min parameter allows you to specify is you want the minimum or maximum blast score ratio used in calculating the distance. The default is true, meaning you want the minimum.\n";
helpString += "The iters parameter allow you to set the maxiters for the opticluster method. \n";
helpString += "The metric parameter allows to select the metric in the opticluster method. Options are Matthews correlation coefficient (mcc), sensitivity (sens), specificity (spec), true positives + true negatives (tptn), false positives + false negatives (fpfn), true positives (tp), true negative (tn), false positive (fp), false negative (fn), f1score (f1score), accuracy (accuracy), positive predictive value (ppv), negative predictive value (npv), false discovery rate (fdr). Default=mcc.\n";
helpString += "The initialize parameter allows to select the initial randomization for the opticluster method. Options are singleton, meaning each sequence is randomly assigned to its own OTU, or oneotu meaning all sequences are assigned to one otu. Default=singleton.\n";
helpString += "The delta parameter allows to set the stable value for the metric in the opticluster method (delta=0.0001). \n";
helpString += "The length parameter is used to specify the minimum overlap required. The default is 5.\n";
helpString += "The adjust parameter is used to handle missing distances. If you set a cutoff, adjust=f by default. If not, adjust=t by default. Adjust=f, means ignore missing distances and adjust cutoff as needed with the average neighbor method. Adjust=t, will treat missing distances as 1.0. You can also set the value the missing distances should be set to, adjust=0.5 would give missing distances a value of 0.5.\n";
helpString += "The penalty parameter is used to adjust the error rate. The default is 0.10.\n";
helpString += "The merge parameter allows you to shut off merging based on overlaps and just cluster. By default merge is true, meaning you want to merge.\n";
helpString += "The mgcluster command should be in the following format: \n";
helpString += "mgcluster(blast=yourBlastfile, name=yourNameFile, cutoff=yourCutOff).\n";
return helpString;
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "getHelpString");
exit(1);
}
}
//**********************************************************************************************************************
string MGClusterCommand::getOutputPattern(string type) {
try {
string pattern = "";
if (type == "list") { pattern = "[filename],[clustertag],list-[filename],[clustertag],[tag2],list"; }
else if (type == "rabund") { pattern = "[filename],[clustertag],rabund"; }
else if (type == "sabund") { pattern = "[filename],[clustertag],sabund"; }
else if (type == "steps") { pattern = "[filename],[clustertag],steps"; }
else if (type == "sensspec") { pattern = "[filename],[clustertag],sensspec"; }
else { m->mothurOut("[ERROR]: No definition for type " + type + " output pattern.\n"); m->setControl_pressed(true); }
return pattern;
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "getOutputPattern");
exit(1);
}
}
//**********************************************************************************************************************
MGClusterCommand::MGClusterCommand(string option) : Command() {
try {
if(option == "help") { help(); abort = true; calledHelp = true; }
else if(option == "citation") { citation(); abort = true; calledHelp = true;}
else if(option == "category") { abort = true; calledHelp = true; }
else {
OptionParser parser(option, setParameters());
map<string, string> parameters = parser.getParameters();
ValidParameters validParameter;
blastfile = validParameter.validFile(parameters, "blast");
if (blastfile == "not open") { blastfile = ""; abort = true; }
else if (blastfile == "not found") { blastfile = ""; }
if (outputdir == ""){ outputdir += util.hasPath(blastfile); }
namefile = validParameter.validFile(parameters, "name");
if (namefile == "not open") { abort = true; }
else if (namefile == "not found") { namefile = ""; }
else { current->setNameFile(namefile); }
countfile = validParameter.validFile(parameters, "count");
if (countfile == "not open") { abort = true; }
else if (countfile == "not found") { countfile = ""; }
else { current->setCountFile(countfile); }
if (countfile != "" && namefile != "") { m->mothurOut("[ERROR]: Cannot have both a name file and count file. Please use one or the other.\n"); abort = true; }
if ((blastfile == "")) { m->mothurOut("When executing a mgcluster command you must provide a blastfile.\n"); abort = true; }
//check for optional parameter and set defaults
string temp;
temp = validParameter.valid(parameters, "precision"); if (temp == "not found") { temp = "100"; }
precisionLength = temp.length();
util.mothurConvert(temp, precision);
cutoffSet = false;
temp = validParameter.valid(parameters, "cutoff");
if (temp == "not found") { temp = "0.70"; }
else { cutoffSet = true; }
util.mothurConvert(temp, cutoff);
method = validParameter.valid(parameters, "method");
if (method == "not found") { method = "opti"; }
if ((method == "furthest") || (method == "nearest") || (method == "average") || (method == "opti")) { }
else { m->mothurOut("Not a valid clustering method. Valid clustering algorithms are furthest, nearest, average or opti.\n"); abort = true; }
metric = validParameter.valid(parameters, "metric"); if (metric == "not found") { metric = "mcc"; }
if ((metric == "mcc") || (metric == "sens") || (metric == "spec") || (metric == "tptn") || (metric == "tp") || (metric == "tn") || (metric == "fp") || (metric == "fn") || (metric == "f1score") || (metric == "accuracy") || (metric == "ppv") || (metric == "npv") || (metric == "fdr") || (metric == "fpfn") ){ }
else { m->mothurOut("[ERROR]: Not a valid metric. Valid metrics are mcc, sens, spec, tp, tn, fp, fn, tptn, fpfn, f1score, accuracy, ppv, npv, fdr.\n"); abort = true; }
initialize = validParameter.valid(parameters, "initialize"); if (initialize == "not found") { initialize = "singleton"; }
if ((initialize == "singleton") || (initialize == "oneotu")){ }
else { m->mothurOut("[ERROR]: Not a valid initialization. Valid initializations are singleton and oneotu.\n"); abort = true; }
temp = validParameter.valid(parameters, "delta"); if (temp == "not found") { temp = "0.0001"; }
util.mothurConvert(temp, stableMetric);
temp = validParameter.valid(parameters, "iters"); if (temp == "not found") { temp = "100"; }
util.mothurConvert(temp, maxIters);
temp = validParameter.valid(parameters, "length"); if (temp == "not found") { temp = "5"; }
util.mothurConvert(temp, length);
temp = validParameter.valid(parameters, "penalty"); if (temp == "not found") { temp = "0.10"; }
util.mothurConvert(temp, penalty);
temp = validParameter.valid(parameters, "min"); if (temp == "not found") { temp = "true"; }
minWanted = util.isTrue(temp);
temp = validParameter.valid(parameters, "merge"); if (temp == "not found") { temp = "true"; }
merge = util.isTrue(temp);
temp = validParameter.valid(parameters, "adjust"); if (temp == "not found") { if (cutoffSet) { temp = "F"; }else { temp="T"; } }
if (util.isNumeric1(temp)) { util.mothurConvert(temp, adjust); }
else if (util.isTrue(temp)) { adjust = 1.0; }
else { adjust = -1.0; }
}
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "MGClusterCommand");
exit(1);
}
}
//**********************************************************************************************************************
int MGClusterCommand::execute(){
try {
if (abort) { if (calledHelp) { return 0; } return 2; }
fileroot = outputdir + util.getRootName(util.getSimpleName(blastfile));
tag = "";
if (method == "furthest") { tag = "fn"; }
else if (method == "nearest") { tag = "nn"; }
else if (method == "average") { tag = "an"; }
else if (method == "opti") { tag = "opti"; }
if (method == "opti") { runOptiCluster(); }
else { runMothurCluster(); }
m->mothurOut("\nOutput File Names: \n");
m->mothurOut(listFileName); m->mothurOutEndLine(); outputNames.push_back(listFileName); outputTypes["list"].push_back(listFileName);
if (countfile == "") {
m->mothurOut(rabundFileName); m->mothurOutEndLine(); outputNames.push_back(rabundFileName); outputTypes["rabund"].push_back(rabundFileName);
m->mothurOut(sabundFileName); m->mothurOutEndLine(); outputNames.push_back(sabundFileName); outputTypes["sabund"].push_back(sabundFileName);
}
m->mothurOutEndLine();
//set list file as new current listfile
string currentName = "";
itTypes = outputTypes.find("list");
if (itTypes != outputTypes.end()) {
if ((itTypes->second).size() != 0) { currentName = (itTypes->second)[0]; current->setListFile(currentName); }
}
//set rabund file as new current rabundfile
itTypes = outputTypes.find("rabund");
if (itTypes != outputTypes.end()) {
if ((itTypes->second).size() != 0) { currentName = (itTypes->second)[0]; current->setRabundFile(currentName); }
}
//set sabund file as new current sabundfile
itTypes = outputTypes.find("sabund");
if (itTypes != outputTypes.end()) {
if ((itTypes->second).size() != 0) { currentName = (itTypes->second)[0]; current->setSabundFile(currentName); }
}
return 0;
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "execute");
exit(1);
}
}
//**********************************************************************************************************************
void MGClusterCommand::printData(ListVector* mergedList, map<string, int>& counts, bool& ph){
try {
mergedList->setPrintedLabels(ph); ph = false;
if (countfile != "") {
mergedList->print(listFile, counts);
}else { mergedList->print(listFile, true); }
SAbundVector sabund = mergedList->getSAbundVector();
if (countfile == "") {
mergedList->getRAbundVector().print(rabundFile);
sabund.print(sabundFile);
}
sabund.print(cout);
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "printData");
exit(1);
}
}
//**********************************************************************************************************************
int MGClusterCommand::runOptiCluster(){
try {
if (!cutoffSet) { m->mothurOut("\nYou did not set a cutoff, using 0.03.\n"); cutoff = 0.03; }
string nameOrCount = "";
string thisNamefile = "";
map<string, int> counts;
if (countfile != "") { nameOrCount = "count"; thisNamefile = countfile; CountTable ct; ct.readTable(countfile, false, false); counts = ct.getNameMap(); }
else if (namefile != "") { nameOrCount = "name"; thisNamefile = namefile; }
string distfile = blastfile;
time_t start = time(nullptr);
OptiData* matrix; matrix = new OptiBlastMatrix(distfile, thisNamefile, nameOrCount, false, cutoff, length, penalty, minWanted);
ClusterMetric* metricCalc = nullptr;
if (metric == "mcc") { metricCalc = new MCC(); }
else if (metric == "sens") { metricCalc = new Sensitivity(); }
else if (metric == "spec") { metricCalc = new Specificity(); }
else if (metric == "tptn") { metricCalc = new TPTN(); }
else if (metric == "tp") { metricCalc = new TP(); }
else if (metric == "tn") { metricCalc = new TN(); }
else if (metric == "fp") { metricCalc = new FP(); }
else if (metric == "fn") { metricCalc = new FN(); }
else if (metric == "f1score") { metricCalc = new F1Score(); }
else if (metric == "accuracy") { metricCalc = new Accuracy(); }
else if (metric == "ppv") { metricCalc = new PPV(); }
else if (metric == "npv") { metricCalc = new NPV(); }
else if (metric == "fdr") { metricCalc = new FDR(); }
else if (metric == "fpfn") { metricCalc = new FPFN(); }
OptiCluster cluster(matrix, metricCalc, 0);
string tag = cluster.getTag();
map<string, string> variables;
variables["[filename]"] = fileroot;
variables["[clustertag]"] = tag;
sabundFileName = getOutputFileName("sabund", variables);
rabundFileName = getOutputFileName("rabund", variables);
//if (countfile != "") { variables["[tag2]"] = "unique_list"; }
listFileName = getOutputFileName("list", variables);
string outputName = getOutputFileName("steps", variables);
outputNames.push_back(outputName); outputTypes["steps"].push_back(outputName);
m->mothurOutEndLine(); m->mothurOut("Clustering " + distfile); m->mothurOutEndLine();
if (outputdir == "") { outputdir += util.hasPath(distfile); }
ofstream listFile;
util.openOutputFile(listFileName, listFile);
outputNames.push_back(listFileName); outputTypes["list"].push_back(listFileName);
ofstream outStep;
util.openOutputFile(outputName, outStep);
int iters = 0;
double listVectorMetric = 0; //worst state
double delta = 1;
cluster.initialize(listVectorMetric, true, initialize);
long long numBins = cluster.getNumBins();
m->mothurOut("\n\niter\ttime\tlabel\tnum_otus\tcutoff\ttp\ttn\tfp\tfn\tsensitivity\tspecificity\tppv\tnpv\tfdr\taccuracy\tmcc\tf1score\n");
outStep << "iter\ttime\tlabel\tnum_otus\tcutoff\ttp\ttn\tfp\tfn\tsensitivity\tspecificity\tppv\tnpv\tfdr\taccuracy\tmcc\tf1score\n";
double tp, tn, fp, fn;
vector<double> results = cluster.getStats(tp, tn, fp, fn);
m->mothurOut("0\t0\t" + toString(cutoff) + "\t" + toString(numBins) + "\t"+ toString(cutoff) + "\t" + toString(tp) + "\t" + toString(tn) + "\t" + toString(fp) + "\t" + toString(fn) + "\t");
outStep << "0\t0\t" + toString(cutoff) + "\t" + toString(numBins) + "\t" + toString(cutoff) + "\t" << tp << '\t' << tn << '\t' << fp << '\t' << fn << '\t';
for (int i = 0; i < results.size(); i++) { m->mothurOut(toString(results[i]) + "\t"); outStep << results[i] << "\t"; }
m->mothurOutEndLine();
outStep << endl;
while ((delta > stableMetric) && (iters < maxIters)) {
long start = time(nullptr);
if (m->getControl_pressed()) { break; }
double oldMetric = listVectorMetric;
cluster.update(listVectorMetric);
delta = abs(oldMetric - listVectorMetric);
iters++;
results = cluster.getStats(tp, tn, fp, fn);
numBins = cluster.getNumBins();
m->mothurOut(toString(iters) + "\t" + toString(time(nullptr) - start) + "\t" + toString(cutoff) + "\t" + toString(numBins) + "\t" + toString(cutoff) + "\t"+ toString(tp) + "\t" + toString(tn) + "\t" + toString(fp) + "\t" + toString(fn) + "\t");
outStep << (toString(iters) + "\t" + toString(time(nullptr) - start) + "\t" + toString(cutoff) + "\t" + toString(numBins) + "\t" + toString(cutoff) + "\t") << tp << '\t' << tn << '\t' << fp << '\t' << fn << '\t';
for (int i = 0; i < results.size(); i++) { m->mothurOut(toString(results[i]) + "\t"); outStep << results[i] << "\t"; }
m->mothurOutEndLine();
outStep << endl;
}
m->mothurOutEndLine(); m->mothurOutEndLine();
list = cluster.getList();
list->setLabel(toString(cutoff));
if (merge) {
vector< set<long long> > overlap = matrix->getBlastOverlap();
//assign each sequence to bins
map<string, long long> seqToBin;
for (long long i = 0; i < list->getNumBins(); i++) {
if (m->getControl_pressed()) { break; }
string bin = list->get(i);
vector<string> names; util.splitAtComma(bin, names);
for (long long j = 0; j < names.size(); j++) { seqToBin[names[j]] = i; }
}
//merge overlapping bins
long long mergedBinCount = 0;
for (long long i = 0; i < overlap.size(); i++) {
set<long long> temp = overlap[i]; overlap[i].clear();
for (set<long long>::iterator itOverlap = temp.begin(); itOverlap != temp.end(); itOverlap++) {
string firstName = matrix->getOverlapName(i);
string secondName = matrix->getOverlapName(*itOverlap);
long long binKeep = seqToBin[firstName];
long long binRemove = seqToBin[secondName];
if(binKeep != binRemove) {
//save names in old bin
string bin = list->get(binRemove);
//merge bins into name1s bin
list->set(binKeep, bin+','+list->get(binKeep));
list->set(binRemove, "");
vector<string> binNames; util.splitAtComma(bin, binNames);
//update binInfo //save name and new bin number
for (int k = 0; k < binNames.size(); k++) { seqToBin[binNames[k]] = binKeep; }
mergedBinCount++;
}
}
}
if (mergedBinCount != 0) { m->mothurOut("Merged " + toString(mergedBinCount) + " OTUs based on blast overlap.\n\n"); }
}
if(countfile != "") { list->print(listFile, counts); }
else { list->print(listFile); }
listFile.close();
variables["[filename]"] = fileroot;
variables["[clustertag]"] = tag;
string sabundFileName = getOutputFileName("sabund", variables);
string rabundFileName = getOutputFileName("rabund", variables);
if (countfile == "") {
util.openOutputFile(sabundFileName, sabundFile);
util.openOutputFile(rabundFileName, rabundFile);
outputNames.push_back(sabundFileName); outputTypes["sabund"].push_back(sabundFileName);
outputNames.push_back(rabundFileName); outputTypes["rabund"].push_back(rabundFileName);
SAbundVector sabund = list->getSAbundVector();
sabund.print(sabundFile);
sabundFile.close();
RAbundVector rabund = list->getRAbundVector();
rabund.print(rabundFile);
rabundFile.close();
}
delete list;
string sensspecFilename = fileroot+ tag + ".sensspec";
ofstream sensFile;
util.openOutputFile(sensspecFilename, sensFile);
outputNames.push_back(sensspecFilename); outputTypes["sensspec"].push_back(sensspecFilename);
sensFile << "label\tcutoff\ttp\ttn\tfp\tfn\tsensitivity\tspecificity\tppv\tnpv\tfdr\taccuracy\tmcc\tf1score\n";
results = cluster.getStats(tp, tn, fp, fn);
sensFile << cutoff << '\t' << cutoff << '\t' << tp << '\t' << tn << '\t' << fp << '\t' << fn << '\t';
for (int i = 0; i < results.size(); i++) { sensFile << results[i] << '\t'; }
sensFile << '\n';
sensFile.close();
m->mothurOut("It took " + toString(time(nullptr) - start) + " seconds to cluster.\n");
delete metricCalc; delete matrix;
return 0;
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "runOptiCluster");
exit(1);
}
}
//**********************************************************************************************************************
int MGClusterCommand::runMothurCluster(){
try {
//read names file
map<string, int> counts;
if (namefile != "") {
nameMap = new NameAssignment(namefile);
nameMap->readMap();
}else if (countfile != "") {
ct = new CountTable();
ct->readTable(countfile, false, false);
nameMap= new NameAssignment();
vector<string> tempNames = ct->getNamesOfSeqs();
for (int i = 0; i < tempNames.size(); i++) { nameMap->push_back(tempNames[i]); }
counts = ct->getNameMap();
}else{ nameMap= new NameAssignment(); }
map<string, string> variables;
variables["[filename]"] = fileroot;
variables["[clustertag]"] = tag;
sabundFileName = getOutputFileName("sabund", variables);
rabundFileName = getOutputFileName("rabund", variables);
//if (countfile != "") { variables["[tag2]"] = "unique_list"; }
listFileName = getOutputFileName("list", variables);
float previousDist = 0.00000;
float rndPreviousDist = 0.00000;
time_t start = time(nullptr);
//read blastfile - creates sparsematrices for the distances and overlaps as well as a listvector
//must remember to delete those objects here since readBlast does not
read = new ReadBlast(blastfile, cutoff, penalty, length, minWanted);
read->read(nameMap);
list = new ListVector(nameMap->getListVector());
RAbundVector* rabund = nullptr;
if(countfile != "") {
rabund = new RAbundVector();
createRabund(ct, list, rabund);
}else {
rabund = new RAbundVector(list->getRAbundVector());
}
if (m->getControl_pressed()) { outputTypes.clear(); delete nameMap; delete read; delete list; delete rabund; return 0; }
oldList = *list;
map<string, int> Seq2Bin;
map<string, int> oldSeq2Bin;
if (countfile == "") {
util.openOutputFile(sabundFileName, sabundFile);
util.openOutputFile(rabundFileName, rabundFile);
}
util.openOutputFile(listFileName, listFile);
if (m->getControl_pressed()) {
delete nameMap; delete read; delete list; delete rabund;
listFile.close(); if (countfile == "") { rabundFile.close(); sabundFile.close(); util.mothurRemove(rabundFileName); util.mothurRemove(sabundFileName); } util.mothurRemove(listFileName);
outputTypes.clear();
return 0;
}
double saveCutoff = cutoff;
bool printHeaders = true;
//get distmatrix and overlap
SparseDistanceMatrix* distMatrix = read->getDistMatrix();
overlapMatrix = read->getOverlapMatrix(); //already sorted by read
delete read;
//create cluster
if (method == "furthest") { cluster = new CompleteLinkage(rabund, list, distMatrix, cutoff, method, adjust); }
else if(method == "nearest"){ cluster = new SingleLinkage(rabund, list, distMatrix, cutoff, method, adjust); }
else if(method == "average"){ cluster = new AverageLinkage(rabund, list, distMatrix, cutoff, method, adjust); }
cluster->setMapWanted(true);
Seq2Bin = cluster->getSeqtoBin();
oldSeq2Bin = Seq2Bin;
if (m->getControl_pressed()) {
delete nameMap; delete distMatrix; delete list; delete rabund; delete cluster;
listFile.close(); if (countfile == "") { rabundFile.close(); sabundFile.close(); util.mothurRemove(rabundFileName); util.mothurRemove(sabundFileName); } util.mothurRemove(listFileName);
outputTypes.clear();
return 0;
}
//cluster using cluster classes
while (distMatrix->getSmallDist() <= cutoff && distMatrix->getNNodes() > 0){
if (m->getDebug()) { cout << "numNodes=" << distMatrix->getNNodes() << " smallDist = " << distMatrix->getSmallDist() << endl; }
cluster->update(cutoff);
if (m->getControl_pressed()) {
delete nameMap; delete distMatrix; delete list; delete rabund; delete cluster;
listFile.close(); if (countfile == "") { rabundFile.close(); sabundFile.close(); util.mothurRemove(rabundFileName); util.mothurRemove(sabundFileName); } util.mothurRemove(listFileName);
outputTypes.clear();
return 0;
}
float dist = distMatrix->getSmallDist();
float rndDist = util.ceilDist(dist, precision);
if(previousDist <= 0.0000 && !util.isEqual(dist, previousDist)){
oldList.setLabel("unique");
printData(&oldList, counts, printHeaders);
}
else if(!util.isEqual(rndDist, rndPreviousDist)){
if (merge) {
ListVector* temp = mergeOPFs(oldSeq2Bin, rndPreviousDist);
if (m->getControl_pressed()) {
delete nameMap; delete distMatrix; delete list; delete rabund; delete cluster; delete temp;
listFile.close(); if (countfile == "") { rabundFile.close(); sabundFile.close(); util.mothurRemove(rabundFileName); util.mothurRemove(sabundFileName); } util.mothurRemove(listFileName);
outputTypes.clear();
return 0;
}
temp->setLabel(toString(rndPreviousDist));
printData(temp, counts, printHeaders);
delete temp;
}else{
oldList.setLabel(toString(rndPreviousDist));
printData(&oldList, counts, printHeaders);
}
}
previousDist = dist;
rndPreviousDist = rndDist;
oldList = *list;
Seq2Bin = cluster->getSeqtoBin();
oldSeq2Bin = Seq2Bin;
}
if(previousDist <= 0.0000){
oldList.setLabel("unique");
printData(&oldList, counts, printHeaders);
}
else if(rndPreviousDist<cutoff){
if (merge) {
ListVector* temp = mergeOPFs(oldSeq2Bin, rndPreviousDist);
if (m->getControl_pressed()) {
delete nameMap; delete distMatrix; delete list; delete rabund; delete cluster; delete temp;
listFile.close(); if (countfile == "") { rabundFile.close(); sabundFile.close(); util.mothurRemove(rabundFileName); util.mothurRemove(sabundFileName); } util.mothurRemove(listFileName);
outputTypes.clear();
return 0;
}
temp->setLabel(toString(rndPreviousDist));
printData(temp, counts, printHeaders);
delete temp;
}else{
oldList.setLabel(toString(rndPreviousDist));
printData(&oldList, counts, printHeaders);
}
}
//free memory
overlapMatrix.clear();
delete distMatrix;
delete cluster;
delete list;
delete rabund;
listFile.close();
if (countfile == "") {
sabundFile.close();
rabundFile.close();
}
if (m->getControl_pressed()) {
delete nameMap;
listFile.close(); if (countfile == "") { rabundFile.close(); sabundFile.close(); util.mothurRemove(rabundFileName); util.mothurRemove(sabundFileName); } util.mothurRemove(listFileName);
outputTypes.clear();
return 0;
}
if (!util.isEqual(saveCutoff, cutoff)) {
saveCutoff = util.ceilDist(saveCutoff, precision);
m->mothurOut("changed cutoff to " + toString(cutoff)); m->mothurOutEndLine();
}
m->mothurOut("It took " + toString(time(nullptr) - start) + " seconds to cluster.\n");
return 0;
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "runMothurCluster");
exit(1);
}
}
//**********************************************************************************************************************
//this merging is just at the reporting level, after this info is printed to the file it is gone and does not effect the datastructures
//that are used to cluster by distance. this is done so that the overlapping data does not have more influenece than the distance data.
ListVector* MGClusterCommand::mergeOPFs(map<string, int> binInfo, float dist){
try {
//create new listvector so you don't overwrite the clustering
ListVector* newList = new ListVector(oldList);
bool done = false;
ifstream inOverlap;
int count = 0;
if (overlapMatrix.size() == 0) { done = true; }
while (!done) {
if (m->getControl_pressed()) { return newList; }
//get next overlap
seqDist overlapNode;
if (count < overlapMatrix.size()) { //do we have another node in the matrix
overlapNode = overlapMatrix[count];
count++;
}else { break; }
if (overlapNode.dist < dist) {
//get names of seqs that overlap
string name1 = nameMap->get(overlapNode.seq1);
string name2 = nameMap->get(overlapNode.seq2);
//use binInfo to find out if they are already in the same bin
//map<string, int>::iterator itBin1 = binInfo.find(name1);
//map<string, int>::iterator itBin2 = binInfo.find(name2);
//if(itBin1 == binInfo.end()){ cerr << "AAError: Sequence '" << name1 << "' does not have any bin info.\n"; exit(1); }
//if(itBin2 == binInfo.end()){ cerr << "ABError: Sequence '" << name2 << "' does not have any bin info.\n"; exit(1); }
//int binKeep = itBin1->second;
//int binRemove = itBin2->second;
int binKeep = binInfo[name1];
int binRemove = binInfo[name2];
//if not merge bins and update binInfo
if(binKeep != binRemove) {
//save names in old bin
string names = newList->get(binRemove);
//merge bins into name1s bin
newList->set(binKeep, newList->get(binRemove)+','+newList->get(binKeep));
newList->set(binRemove, "");
vector<string> binNames; util.splitAtComma(names, binNames);
//update binInfo //save name and new bin number
for (int i = 0; i < binNames.size(); i++) { binInfo[binNames[i]] = binKeep; }
}
}else { done = true; }
}
//return listvector
return newList;
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "mergeOPFs");
exit(1);
}
}
//**********************************************************************************************************************
void MGClusterCommand::createRabund(CountTable*& ct, ListVector*& list, RAbundVector*& rabund){
try {
//vector<string> names = ct.getNamesOfSeqs();
//for ( int i; i < ct.getNumGroups(); i++ ) { rav.push_back( ct.getNumSeqs(names[i]) ); }
//return rav;
for(int i = 0; i < list->getNumBins(); i++) {
vector<string> binNames;
string bin = list->get(i);
util.splitAtComma(bin, binNames);
int total = 0;
for (int j = 0; j < binNames.size(); j++) {
total += ct->getNumSeqs(binNames[j]);
}
rabund->push_back(total);
}
}
catch(exception& e) {
m->errorOut(e, "MGClusterCommand", "createRabund");
exit(1);
}
}
//**********************************************************************************************************************
|
