-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcmd_cram_demuxlet.cpp
1074 lines (933 loc) · 47.3 KB
/
cmd_cram_demuxlet.cpp
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
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#include "cramore.h"
#include "bcf_filtered_reader.h"
#include "sam_filtered_reader.h"
#include "sc_drop_seq.h"
int32_t cmdCramDemuxlet(int32_t argc, char **argv) {
SAMFilteredReader sr;
BCFFilteredReader vr;
std::string field("GP");
double genoErrorOffset = 0.10;
double genoErrorCoeffR2 = 0.00;
std::string r2Info("R2");
std::string outPrefix;
std::string plpPrefix;
std::string tagGroup("CB");
std::string tagUMI("UB");
int32_t capBQ = 40;
int32_t minBQ = 13;
int32_t minTD = 0;
sr.filt.exclude_flag = 0x0f04;
sr.filt.minMQ = 20;
std::vector<std::string> smIDs;
std::vector<double> gridAlpha;
//std::vector<double> gridASE;
vr.verbose = 10000;
sr.verbose = 1000000;
vr.vfilt.minMAC = 1;
vr.vfilt.minCallRate = 0.5;
vr.vfilt.maxAlleles = 2;
//bool writePair = false;
//bool fullPair = true;
double doublet_prior = 0.5;
std::string groupList;
int32_t minTotalReads = 0;
int32_t minUniqReads = 0;
int32_t minCoveredSNPs = 0;
paramList pl;
BEGIN_LONG_PARAMS(longParameters)
LONG_PARAM_GROUP("Options for input SAM/BAM/CRAM", NULL)
LONG_STRING_PARAM("sam", &sr.sam_file_name,
"Input SAM/BAM/CRAM file. Must be sorted by coordinates and indexed")
LONG_STRING_PARAM("tag-group", &tagGroup,
"Tag representing readgroup or cell barcodes, in the case to partition the BAM file into multiple groups. For 10x genomics, use CB")
LONG_STRING_PARAM("tag-UMI", &tagUMI, "Tag representing UMIs. For 10x genomiucs, use UB")
LONG_PARAM_GROUP("Options for input Pileup format", NULL)
LONG_STRING_PARAM("plp", &plpPrefix, "Input pileup format")
LONG_PARAM_GROUP("Options for input VCF/BCF", NULL)
LONG_STRING_PARAM("vcf", &vr.bcf_file_name,
"Input VCF/BCF file, containing the individual genotypes (GT), posterior probability (GP), or genotype likelihood (PL)")
LONG_STRING_PARAM("field", &field,
"FORMAT field to extract the genotype, likelihood, or posterior from")
LONG_DOUBLE_PARAM("geno-error-offset", &genoErrorOffset,
"Offset of genotype error rate. [error] = [offset] + [1-offset]*[coeff]*[1-r2]")
LONG_DOUBLE_PARAM("geno-error-coeff", &genoErrorCoeffR2,
"Slope of genotype error rate. [error] = [offset] + [1-offset]*[coeff]*[1-r2]")
LONG_STRING_PARAM("r2-info", &r2Info,
"INFO field name representing R2 value. Used for representing imputation quality")
LONG_INT_PARAM("min-mac", &vr.vfilt.minMAC, "Minimum minor allele frequency")
LONG_DOUBLE_PARAM("min-callrate", &vr.vfilt.minCallRate, "Minimum call rate")
LONG_MULTI_STRING_PARAM("sm", &smIDs, "List of sample IDs to compare to (default: use all)")
LONG_STRING_PARAM("sm-list", &vr.sample_id_list,
"File containing the list of sample IDs to compare")
LONG_PARAM_GROUP("Output Options", NULL)
LONG_STRING_PARAM("out", &outPrefix, "Output file prefix")
LONG_MULTI_DOUBLE_PARAM("alpha", &gridAlpha,
"Grid of alpha to search for (default is 0.1, 0.2, 0.3, 0.4, 0.5)")
LONG_DOUBLE_PARAM("doublet-prior", &doublet_prior, "Prior of doublet")
LONG_INT_PARAM("sam-verbose", &sr.verbose, "Verbose message frequency for SAM/BAM/CRAM")
LONG_INT_PARAM("vcf-verbose", &vr.verbose, "Verbose message frequency for VCF/BCF")
LONG_PARAM_GROUP("Read filtering Options", NULL)
LONG_INT_PARAM("cap-BQ", &capBQ, "Maximum base quality (higher BQ will be capped)")
LONG_INT_PARAM("min-BQ", &minBQ, "Minimum base quality to consider (lower BQ will be skipped)")
LONG_INT_PARAM("min-MQ", &sr.filt.minMQ,
"Minimum mapping quality to consider (lower MQ will be ignored)")
LONG_INT_PARAM("min-TD", &minTD, "Minimum distance to the tail (lower will be ignored)")
LONG_INT_PARAM("excl-flag", &sr.filt.exclude_flag, "SAM/BAM FLAGs to be excluded")
LONG_PARAM_GROUP("Cell/droplet filtering options", NULL)
LONG_STRING_PARAM("group-list", &groupList,
"List of tag readgroup/cell barcode to consider in this run. All other barcodes will be ignored. This is useful for parallelized run")
LONG_INT_PARAM("min-total", &minTotalReads,
"Minimum number of total reads for a droplet/cell to be considered")
LONG_INT_PARAM("min-uniq", &minUniqReads,
"Minimum number of unique reads (determined by UMI/SNP pair) for a droplet/cell to be considered")
LONG_INT_PARAM("min-snp", &minCoveredSNPs,
"Minimum number of SNPs with coverage for a droplet/cell to be considered")
END_LONG_PARAMS();
pl.Add(new longParams("Available Options", longParameters));
pl.Read(argc, argv);
pl.Status();
if (gridAlpha.empty()) {
gridAlpha.push_back(0);
//gridAlpha.push_back(0.25);
gridAlpha.push_back(0.5);
}
std::set<std::string> bcdSet;
sc_dropseq_lib_t scl;
int32_t nAlpha = (int32_t) gridAlpha.size();
for (int32_t i = 0; i < (int32_t) smIDs.size(); ++i) {
vr.add_specified_sample(smIDs[i].c_str());
}
vr.unlimited_buffer = true;
vr.vfilt.maxAlleles = 2;
vr.init_params();
int32_t nv = vr.get_nsamples();
double *gps = NULL;
if (!plpPrefix.empty()) { // read from pileup
//int nrd = 0;
if (!sr.sam_file_name.empty()) {
error("with --plp option, neither --sam option cannot be used");
}
scl.load_from_plp(plpPrefix.c_str(), &vr, field.c_str(), genoErrorOffset, genoErrorCoeffR2, r2Info.c_str());
} else { // read BAM directly
if (!groupList.empty()) {
tsv_reader tsv_group_list(groupList.c_str());
while (tsv_group_list.read_line() > 0) {
bcdSet.insert(tsv_group_list.str_field_at(0));
}
notice("Finished loading %u droplet/cell barcodes to consider", bcdSet.size());
}
sr.set_buffer_size(1);
//sr.unlimited_buffer = true;
sr.init_params();
int32_t n_warning_no_gtag = 0;
int32_t n_warning_no_utag = 0;
//if ( outPrefix.empty() )
// error("[E:%s:%d %s] --out parameter is missing",__FILE__,__LINE__,__PRETTY_FUNCTION__);
char gtag[2] = {0, 0};
char utag[2] = {0, 0};
if (tagGroup.empty()) { // do nothing
} else if (tagGroup.size() == 2) {
gtag[0] = tagGroup.at(0);
gtag[1] = tagGroup.at(1);
} else {
error("[E:%s:%d %s] Cannot recognize group tag %s. It is suppose to be a length 2 string", __FILE__,
__LINE__, __FUNCTION__, tagGroup.c_str());
}
if (tagUMI.empty()) { // do nothing
} else if (tagUMI.size() == 2) {
utag[0] = tagUMI.at(0);
utag[1] = tagUMI.at(1);
} else {
error("[E:%s:%d %s] Cannot recognize UMI tag %s. It is suppose to be a length 2 string", __FILE__, __LINE__,
__FUNCTION__, tagUMI.c_str());
}
// scan VCF and CRAM simultaneously
// read a variant first
std::vector<int32_t> snpids;
//std::vector<int32_t> cellids;
// makr sure that the variant exists
if (!vr.read())
error("[E:%s Cannot read any single variant from %s]", __PRETTY_FUNCTION__, vr.bcf_file_name.c_str());
// make sure that the genotype field is parseable
if (!vr.parse_posteriors(vr.cdr.hdr, vr.cursor(), field.c_str()))
error("[E:%s] Cannot parse posterior probability at %s:%d", __PRETTY_FUNCTION__,
bcf_hdr_id2name(vr.cdr.hdr, vr.cursor()->rid), vr.cursor()->pos + 1);
// check if the chromosome names are in the same order between BCF and SAM
std::map<int32_t, int32_t> rid2tids;
std::map<int32_t, int32_t> tid2rids;
int32_t ntids = bam_hdr_get_n_targets(sr.hdr);
int32_t prevrid = -1;
for (int32_t i = 0; i < ntids; ++i) {
const char *chrom = bam_get_chromi(sr.hdr, i);
int32_t rid = bcf_hdr_name2id(vr.cdr.hdr, chrom);
if (rid >= 0) {
if (prevrid >= rid) {
const char *prevchrom = bcf_hdr_id2name(vr.cdr.hdr, prevrid);
error("[E:%s] Your VCF/BCF files and SAM/BAM/CRAM files have different ordering of chromosomes. SAM/BAM/CRAM file has %s before %s, but VCF/BCF file has %s after %s",
__PRETTY_FUNCTION__, prevchrom, chrom, prevchrom, chrom);
}
rid2tids[rid] = i;
tid2rids[i] = rid;
prevrid = rid;
}
}
if (rid2tids.empty() || tid2rids.empty() || (rid2tids.size() != tid2rids.size())) {
error("[E:%s] Your VCF/BCF files and SAM/BAM/CRAM files does not have any matching chromosomes, or some chromosome names are duplicated");
}
//nv = vr.get_nsamples();
gps = new double[nv * 3];
for (int32_t i = 0; i < nv * 3; ++i)
gps[i] = vr.get_posterior_at(i);
int32_t snpid = scl.add_snp(vr.cursor()->rid, vr.cursor()->pos + 1, vr.cursor()->d.allele[0][0],
vr.cursor()->d.allele[1][0], vr.get_af(1), gps);
snpids.push_back(snpid);
int32_t ibeg = 0;
char base, qual;
int32_t rpos;
kstring_t readseq = {0, 0, 0};
kstring_t readqual = {0, 0, 0};
int32_t nReadsMultiSNPs = 0, nReadsSkipBCD = 0, nReadsPass = 0, nReadsRedundant = 0, nReadsN = 0, nReadsLQ = 0, nReadsTMP = 0;
float *r2flts = NULL; // for extracting R2 fields..
int32_t n_r2flts = 0;
while (sr.read()) { // read SAM file
int32_t endpos = bam_endpos(sr.cursor());
int32_t tid2rid = bcf_hdr_name2id(vr.cdr.hdr, bam_get_chrom(sr.hdr, sr.cursor()));
if (tid2rid < 0) { // no matching BCF entry in the chromosome, skip;
continue;
}
int32_t n_cleared = vr.clear_buffer_before(bcf_hdr_id2name(vr.cdr.hdr, vr.cursor()->rid),
sr.cursor()->core.pos);
//for(int32_t i=ibeg; i < ibeg+n_cleared; ++i) {
// v_umis.clear();
//}
ibeg += n_cleared;
// add new snps
while ((!vr.eof) &&
((vr.cursor()->rid < tid2rid) || ((vr.cursor()->rid == tid2rid) && (vr.cursor()->pos < endpos)))) {
if (vr.read()) {
if (!vr.parse_posteriors(vr.cdr.hdr, vr.cursor(), field.c_str(), 0))
error("[E:%s] Cannot parse posterior probability at %s:%d", __PRETTY_FUNCTION__,
bcf_hdr_id2name(vr.cdr.hdr, vr.cursor()->rid), vr.cursor()->pos + 1);
gps = new double[nv * 3];
double avgGPs[3] = {1e-10, 1e-10, 1e-10}; // represents empirical GP averages across samples
for (int32_t i = 0; i < nv * 3; ++i) {
avgGPs[i % 3] += (gps[i] = vr.get_posterior_at(i));
}
// get average GPs to account for genoErrors
double sumGP = avgGPs[0] + avgGPs[1] + avgGPs[2];
avgGPs[0] /= sumGP;
avgGPs[1] /= sumGP;
avgGPs[2] /= sumGP;
// account for genotype errors as [Offset] + [1-Offset]*[1-R2]*[Coeff]
double err = genoErrorOffset;
if (genoErrorCoeffR2 > 0) { // look for R2 INFO field
if ((bcf_get_info_float(vr.cdr.hdr, vr.cursor(), r2Info.c_str(), &r2flts, &n_r2flts) < 0) ||
(n_r2flts != 1)) {
error("Cannot extract %s (1 float value) from INFO field at %s:%d. Cannot use --geno-error-coeff",
r2Info.c_str(), bcf_hdr_id2name(vr.cdr.hdr, vr.cursor()->rid), vr.cursor()->pos + 1);
}
err += (1 - genoErrorOffset) * (1 - r2flts[0]) * genoErrorCoeffR2;
}
if (err > 0.999) err = 0.999;
if (err < 0) err = 0;
if (err > 0) { // if error is greater than zero, adjust it
for (int32_t i = 0; i < nv * 3; ++i) {
gps[i] = (1 - err) * gps[i] + err * avgGPs[i % 3];
}
}
snpid = scl.add_snp(vr.cursor()->rid, vr.cursor()->pos + 1, vr.cursor()->d.allele[0][0],
vr.cursor()->d.allele[1][0], vr.get_af(1), gps);
snpids.push_back(snpid);
} else {
//error("Cannot read new SNP");
}
}
// get barcode
int32_t ibcd = 0;
if (tagGroup.empty()) {
ibcd = scl.add_cell(".");
} else {
uint8_t *bcd = (*gtag) ? (uint8_t *) bam_aux_get(sr.cursor(), gtag) : NULL;
const char *sbcd = ".";
if ((bcd != NULL) && (*bcd == 'Z')) {
sbcd = bam_aux2Z(bcd);
} else {
if (n_warning_no_gtag < 10) {
notice("WARNING: Cannot find Droplet/Cell tag %s from %d-th read %s at %s:%d-%d. Treating all of them as a single group",
tagUMI.c_str(), sr.n_read, bam_get_qname(sr.cursor()),
bam_get_chrom(sr.hdr, sr.cursor()), sr.cursor()->core.pos, bam_endpos(sr.cursor()));
} else if (n_warning_no_gtag == 10) {
notice("WARNING: Suppressing 10+ missing Droplet/Cell tag warnings...");
}
++n_warning_no_gtag;
}
if (bcdSet.empty() || (bcdSet.find(sbcd) != bcdSet.end())) {
ibcd = scl.add_cell(sbcd);
} else {
++nReadsSkipBCD;
continue;
}
}
++nReadsTMP;
// get UMI
std::string sumi(".");
if (tagUMI.empty()) {
catprintf(sumi, "%x", rand()); // give a random UMI
} else {
uint8_t *umi = (*utag) ? (uint8_t *) bam_aux_get(sr.cursor(), utag) : NULL;
if ((umi != NULL) && (*umi == 'Z')) {
sumi = bam_aux2Z(umi);
} else {
if (n_warning_no_utag < 10) {
notice("WARNING: Cannot find UMI tag %s from %d-th read %s at %s:%d-%d. Treating all of them as a single UMI",
tagUMI.c_str(), sr.n_read, bam_get_qname(sr.cursor()),
bam_get_chrom(sr.hdr, sr.cursor()), sr.cursor()->core.pos, bam_endpos(sr.cursor()));
} else if (n_warning_no_utag == 10) {
notice("WARNING: Suppressing 10+ UMI warnings...");
}
++n_warning_no_utag;
//error("[E:%s] Cannot find UMI tag %d %d %x %s %s %x", __PRETTY_FUNCTION__, sr.nbuf, sr.ridx, sr.cursor(), bcd, utag, umi);
}
}
++scl.cell_totl_reads[ibcd];
// genotype all reads together
int32_t nv_pass = 0;
int32_t nv_redundant = 0;
int32_t nv_valid = 0;
int32_t allele, bq;
//if ( rand() % 10000 == 0 )
//notice("Reading between %s:%d-%d at %s:%d to %d i=beg=%d, nbuf=%d, vidx=%d, size=%u prevpos=%d", bam_get_chrom(sr.hdr, sr.cursor()), sr.cursor()->core.pos+1, bam_endpos(sr.cursor()), bcf_hdr_id2name(vr.cdr.hdr, scl.snps[ibeg].rid), scl.snps[ibeg].pos, scl.snps[ibeg+vr.nbuf-1].pos, ibeg, vr.nbuf, vr.vidx, vr.vbufs.size(), scl.snps[ibeg-1].pos);
for (int32_t i = ibeg; i < ibeg + vr.nbuf; ++i) {
bam1_t *b = sr.cursor();
bam_get_base_and_qual_and_read_and_qual(b, (uint32_t) scl.snps[i].pos - 1, base, qual, rpos, &readseq,
&readqual);
if (rpos == BAM_READ_INDEX_NA) {
//if ( rand() % 1000 == 0 )
//notice("Cannot find any informative read between %s:%d-%d at %s:%d", bam_get_chrom(sr.hdr, b), b->core.pos+1, bam_endpos(b), bcf_hdr_id2name(vr.cdr.hdr, scl.snps[i].rid), scl.snps[i].pos);
continue;
}
if (base == 'N') continue;
++nv_valid;
if (qual - 33 < minBQ) { continue; }
if (rpos < minTD - 1) { continue; }
if (rpos + minTD > b->core.l_qseq) { continue; }
allele = (base == scl.snps[i].ref) ? 0 : ((base == scl.snps[i].alt) ? 1 : 2);
bq = qual - 33 > capBQ ? capBQ : qual - 33;
if (scl.add_read(snpids[i], ibcd, sumi.c_str(), allele, bq))
++nv_pass;
else
++nv_redundant;
}
if (nv_pass > 1) ++nReadsMultiSNPs;
if (nv_pass > 0) ++nReadsPass;
else if (nv_redundant > 0) ++nReadsRedundant;
else if (nv_valid > 0) ++nReadsLQ;
else ++nReadsN;
//if ( nv_valid > 0 ) ++scl.cell_totl_reads[ibcd];
}
free(r2flts);
if (n_warning_no_utag > 10)
notice("WARNING: Suppressed a total of %d UMI warnings...", n_warning_no_utag);
if (n_warning_no_gtag > 10)
notice("WARNING: Suppressed a total of %d droplet/cell barcode warnings...", n_warning_no_gtag);
notice("Finished reading %d markers from the VCF file", (int32_t) snpids.size());
//notice("Finished processing %d reads across %d variants across %d barcodes", nReadsPass, (int32_t)v_poss.size(), (int32_t)bcMap.size(), (int32_t)bcMap.size());
notice("Total number input reads : %d", sr.n_read);
notice("Total number of read-QC-passed reads : %d ",
sr.n_read - sr.n_skip); //, nReadsN + nReadsUnique + nReadsLQ + nReadsPass);
notice("Total number of skipped reads with ignored barcodes : %d", nReadsSkipBCD);
notice("Total number of non-skipped reads with considered barcodes : %d", nReadsTMP);
notice("Total number of gapped/noninformative reads : %d", nReadsN);
notice("Total number of base-QC-failed reads : %d", nReadsLQ);
notice("Total number of redundant reads : %d", nReadsRedundant);
notice("Total number of pass-filtered reads : %d", nReadsPass);
notice("Total number of pass-filtered reads overlapping with multiple SNPs : %d", nReadsMultiSNPs);
//notice("Finished processing %d reads across %d variants across %d barcodes, filtering %d (%.2lf%%) reads, including %d (%.2lf%%) gapped reads, %d (%.2lf%%) low quality reads, and %d (%.2lf%%) redundant/qcfail reads from the BAM file %s", nReadsPass, (int32_t)v_poss.size(), (int32_t)bcMap.size(), nReadsLQ + nReadsUnique + nReadsN, 100.0 * (nReadsLQ + nReadsUnique + nReadsN) / (nReads, nReadsN, 100.0 * nReadsN / (nReadsPass + nReadsLQ + nReadsUnique + nReadsN), nReadsLQ, 100.0 * nReadsLQ / nR, nReadsRedundant, 100.0 * nReadsRedundant / nReadsAll, inSam.c_str());
sr.close();
//vr.close();
notice("Starting to prune out cells with too few reads...");
int32_t nRemoved = 0;
if (minTotalReads + minUniqReads + minCoveredSNPs < 0) {
for (int32_t i = 0; i < scl.nbcs; ++i) {
if ((scl.cell_totl_reads[i] < minTotalReads) || (scl.cell_uniq_reads[i] < minUniqReads) ||
((int32_t) scl.cell_umis[i].size() < minCoveredSNPs)) {
for (std::map<int32_t, sc_snp_droplet_t *>::iterator it = scl.cell_umis[i].begin();
it != scl.cell_umis[i].end(); ++it) {
delete it->second;
scl.snp_umis[it->first].erase(i);
}
scl.cell_umis[i].clear();
++nRemoved;
}
}
}
notice("Finishing pruning out %d cells with too few reads...", nRemoved);
if ((int32_t) snpids.size() != scl.nsnps)
error("[E:%s snpids.size() = %u != scl.nsnps = %d", __PRETTY_FUNCTION__, snpids.size(), scl.nsnps);
}
// Calculate average genotype probability for each SNP
double *gp0s = (double *) calloc(scl.nsnps * 3, sizeof(double));
for (int32_t i = 0; i < scl.nsnps; ++i) {
if (scl.snps[i].gps != NULL) {
for (int32_t j = 0; j < nv; ++j) {
gp0s[i * 3] += scl.snps[i].gps[3 * j];
gp0s[i * 3 + 1] += scl.snps[i].gps[3 * j + 1];
gp0s[i * 3 + 2] += scl.snps[i].gps[3 * j + 2];
}
gp0s[i * 3] /= nv;
gp0s[i * 3 + 1] /= nv;
gp0s[i * 3 + 2] /= nv;
}
}
// start evaluating genotype concordances
// calculate for (nBcd) x (nInds) to find the best matching genotypes first
notice("Starting to identify best matching individual IDs");
//htsFile* wsingle = hts_open((outPrefix+".single").c_str(),"w");
//if ( wsingle == NULL )
// error("[E:%s:%d %s] Cannot create %s.single file",__FILE__,__LINE__,__FUNCTION__,outPrefix.c_str());
/*
std::vector<double> llks(scl.nbcs * nv, 0);
std::vector<double> llk0s(scl.nbcs, 0);
double tmp;
for(int32_t i=0; i < scl.nsnps; ++i) {
if ( ( vr.verbose > 0 ) && ( (i+1) % vr.verbose == 0 ) )
notice("Processing %d markers...",i+1);
std::map<int32_t,sc_snp_droplet_t*>& cells = scl.snp_umis[i];
if ( cells.empty() ) continue;
std::map<int32_t,sc_snp_droplet_t*>::iterator it;
//std::vector<double> GLs(scl.nbcs * 4, 0);
double GLs[3];
// store llks[icell * nv + k] as matching likelihood for single individual
for(it = cells.begin(); it != cells.end(); ++it) {
GLs[0] = GLs[1] = GLs[2] = 1.0;
for(sc_snp_droplet_it_t it2=it->second->begin(); it2 != it->second->end(); ++it2) {
uint8_t al = ( it2->second >> 24 ) & 0x00ff;
uint8_t bq = ( it2->second >> 16 ) & 0x00ff;
//if ( rand() % 1000 == 0 ) notice("bq = %d, al = %d", bq, al);
//uint32_t ibcd = it->first;
if ( al == 2 ) continue;
GLs[0] *= ((al==0) ? phredConv.phred2Mat[bq] : phredConv.phred2Err[bq]/3.0);
GLs[1] *= (0.5 - phredConv.phred2Err[bq]/3.0);
GLs[2] *= ((al==1) ? phredConv.phred2Mat[bq] : phredConv.phred2Err[bq]/3.0);
tmp = GLs[0] + GLs[1] + GLs[2];
GLs[0] /= tmp;
GLs[1] /= tmp;
GLs[2] /= tmp;
}
GLs[0] += 1e-6;
GLs[1] += 1e-6;
GLs[2] += 1e-6;
tmp = GLs[0] + GLs[1] + GLs[2];
GLs[0] /= tmp;
GLs[1] /= tmp;
GLs[2] /= tmp;
gps = scl.snps[i].gps;
if ( gps != NULL ) {
for(int32_t k=0; k < nv; ++k) {
llks[it->first * nv + k] += log(GLs[0]*gps[k*3] + GLs[1]*gps[k*3+1] + GLs[2]*gps[k*3+2]);
//if ( rand() % 1000 == 0 ) notice("%lg %lg %lg",gps[k*3],gps[k*3+1],gps[k*3+2]);
}
llk0s[it->first] += log( GLs[0] * gp0s[i*3] + GLs[1] * gp0s[i*3+1] + GLs[2] * gp0s[i*3+2] );
}
}
}
*/
// find the two best matching individuals
/*
std::vector<int32_t> iBest(scl.nbcs,0);
std::vector<int32_t> iNext(scl.nbcs,0);
std::vector<double> llkBest(scl.nbcs,0);
notice("Identifying best-matching individual..");
//hprintf(wsingle, "BARCODE\tSM_ID\tRD.TOTL\tRD.PASS\tRD.UNIQ\tN.SNP\tLLK1\tLLK0\tPOSTPRB\n");
int32_t i=0;
for(std::map<std::string,int32_t>::iterator it = scl.bc_map.begin();
it != scl.bc_map.end(); ++it) {
int32_t imax = -1;
int32_t inext = -1;
double maxLLK = -1e300;
double nextLLK = -1e300;
double sumLLK = -1e300;
if ( ( scl.cell_totl_reads[it->second] < minTotalReads ) || ( scl.cell_uniq_reads[it->second] < minUniqReads) || ( (int32_t)scl.cell_umis[it->second].size() < minCoveredSNPs ) ) continue;
for(int32_t j=0; j < nv; ++j) {
double curLLK = llks[it->second * nv + j];
if ( sumLLK > curLLK ) {
sumLLK = sumLLK + log(1.0 + exp(curLLK - sumLLK));
}
else {
sumLLK = curLLK + log(1.0 + exp(sumLLK - curLLK));
}
if ( curLLK > maxLLK ) {
inext = imax;
nextLLK = maxLLK;
imax = j;
maxLLK = curLLK;
}
else if ( curLLK > nextLLK ) {
nextLLK = curLLK;
inext = j;
}
}
for(int32_t j=0; j < nv; ++j) {
double curLLK = llks[it->second * nv + j];
hprintf(wsingle,"%s\t%s\t%d\t%d\t%d\t%d\t%.5lf\t%.5lf\t%.3lg\n",
it->first.c_str(),
vr.get_sample_id_at(j),
scl.cell_totl_reads[it->second],
scl.cell_pass_reads[it->second],
scl.cell_uniq_reads[it->second],
(int32_t)scl.cell_umis[it->second].size(),
curLLK,
llk0s[it->second],
exp(curLLK-sumLLK)
);
}
iBest[it->second] = imax;
iNext[it->second] = inext;
llkBest[it->second] = maxLLK;
++i;
if ( i % 1000 == 0 )
notice("Processing %d droplets...", i);
}
notice("Finished processing %d droplets total", i);
//hts_close(wsingle);
*/
//htsFile* wsing2 = hts_open((outPrefix+".sing2").c_str(),"w");
//htsFile* wpair = (writePair ? hts_open((outPrefix+".pair").c_str(),"w") : NULL);
htsFile *wbest = hts_open((outPrefix + ".best").c_str(), "w");
//hprintf(wsing2, "BARCODE\tSM_ID\tRD.TOTL\tRD.PASS\tRD.UNIQ\tN.SNP\tLLK1\tLLK0\tPOSTPRB\n");
//if ( ( writePair && wpair == NULL ) || ( wsingle == NULL ) )
// error("[E:%s:%d %s] Cannot create %s.single, %s.pair files",__FILE__,__LINE__,__FUNCTION__,outPrefix.c_str(), outPrefix.c_str());
// start finding the next-best matching individual
// here we iterate each cell separately.
// pre-calculate nsnp*nv*nv*9, nv*1*9, 1*nv*9, 1*9
//double* gpAB = new double[scl.nsnps * nv * nv * 9];
std::vector<double *> gpA0(scl.nsnps, NULL);
std::vector<double *> gp00(scl.nsnps, NULL);
//double* gpA0 = new double*//new double[scl.nsnps * nv * 9];
//double* gp00 = //new double[scl.nsnps * 9];
//double** gpAB0 = new (double*)[ scl.nsnps ];
//double *gpAB = NULL, *gpA0 = NULL, *gp00 = NULL;
int32_t i, j, k, l, m, n;
for (i = 0; i < scl.nsnps; ++i) {
//gpAB0[i] = new double[nv * nv * 9 + nv * 9 + 9];
gps = scl.snps[i].gps;
if (gps != NULL) {
gpA0[i] = new double[nv * 9];
gp00[i] = new double[9];
for (j = 0; j < nv; ++j) {
for (l = 0; l < 3; ++l) {
for (m = 0; m < 3; ++m) {
//gpAB[i*nv*nv*9 + j*nv*9 + k*9 + l*3 + m] = gps[j*3+l] * gps[k*3+m];
//gpA0[i*nv*9 + j*9 + l*3 + m] = gps[j*3+l] * gp0s[i*3+m];
//gp00[i*9 + l*3 + m] = gp0s[i*3+l] * gp0s[i*3+m];
gpA0[i][j * 9 + l * 3 + m] = gps[j * 3 + l] * gp0s[i * 3 + m];
gp00[i][l * 3 + m] = gp0s[i * 3 + l] * gp0s[i * 3 + m];
}
}
}
}
}
// iterate each barcode
int32_t n1 = nv;
double *llksAB = new double[n1 * nv * nAlpha]; // pairwise doublet/singlet likelihood
double *llksA0 = new double[nv * nAlpha]; // half-specified doublet likelihood
double *llks00 = new double[nAlpha]; // unspecified doublet likelihood
//double* postAB = new double[n1 * nv * nAlpha];
//if ( writePair )
// hprintf(wpair,"BARCODE\tSM1.ID\tSM2.ID\tLLK12\tPOSTPRB\n");
//hprintf(wbest,"BARCODE\tRD.TOTL\tRD.PASS\tRD.UNIQ\tN.SNP\tBEST\tSNG.1ST\tSNG.LLK1\tSNG.2ND\tSNG.LLK2\tSNG.LLK0\tDBL.1ST\tDBL.2ND\tALPHA\tLLK12\tLLK1\tLLK2\tLLK10\tLLK20\tLLK00\tPRB.DBL\tPRB.SNG1\n");
hprintf(wbest,
"BARCODE\tNUM.SNPS\tNUM.READS\tDROPLET.TYPE\tBEST.GUESS\tBEST.LLK\tNEXT.GUESS\tNEXT.LLK\tDIFF.LLK.BEST.NEXT\tBEST.POSTERIOR\tSNG.POSTERIOR\tSNG.BEST.GUESS\tSNG.BEST.LLK\tSNG.NEXT.GUESS\tSNG.NEXT.LLK\tSNG.ONLY.POSTERIOR\tDBL.BEST.GUESS\tDBL.BEST.LLK\tDIFF.LLK.SNG.DBL\n");
//SINGLE.BEST.ID\tSINGLE.NEXT.ID\t
//SM1.ID\tSM2.ID\tALPHA\tRD.TOTL\tRD.PASS\tRD.UNIQ\tN.SNP\tLLK12\tLLK1\tLLK0\tLLK10\tLLK00\tPOSTPRB\n");
int ncells = 0;
// iterate across all possible droplets
for (std::map<std::string, int32_t>::iterator it0 = scl.bc_map.begin(); it0 != scl.bc_map.end(); ++it0, ++ncells) {
if (ncells % 100 == 0)
notice("Demultiplexing %d droplets..", ncells);
i = it0->second;
if ((scl.cell_totl_reads[i] < minTotalReads) || (scl.cell_uniq_reads[i] < minUniqReads) ||
((int32_t) scl.cell_umis[i].size() < minCoveredSNPs))
continue;
memset(llksAB, 0, sizeof(double) * n1 * nv * nAlpha); // pairwise doublet/singlet likelihood
memset(llksA0, 0, sizeof(double) * nv * nAlpha); // half-unspecified double likelihood
memset(llks00, 0, sizeof(double) * nAlpha); // fully-unspecified double likelihood
// currently, compare with everyone
int32_t jbeg = 0; //fullPair ? 0 : iBest[i];
int32_t jend = nv; //fullPair ? nv : iBest[i]+1;
// iterate across all possible snps
std::map<int32_t, sc_snp_droplet_t *> &snps = scl.cell_umis[i];
if (snps.empty()) continue;
std::map<int32_t, sc_snp_droplet_t *>::iterator it;
std::vector<double> pGs(nAlpha * 9, 1.0);
for (it = snps.begin(); it != snps.end(); ++it) {
std::fill(pGs.begin(), pGs.end(), 1.0);
// calculate genotype likelihoods for the SNP
for (sc_snp_droplet_it_t it2 = it->second->begin(); it2 != it->second->end(); ++it2) {
uint8_t al = (it2->second >> 24) & 0x00ff;
uint8_t bq = (it2->second >> 16) & 0x00ff;
if (al == 2) continue;
double pR = (al == 0) ? phredConv.phred2Mat[bq] : phredConv.phred2Err[bq] / 3.0;
double pA = (al == 1) ? phredConv.phred2Mat[bq] : phredConv.phred2Err[bq] / 3.0;
double maxpG = 0;
for (int32_t k = 0; k < nAlpha; ++k) {
for (int32_t l = 0; l < 3; ++l) { // 1-Alpha
for (int32_t m = 0; m < 3; ++m) { // Alpha
double p = 0.5 * l + (m - l) * 0.5 *
gridAlpha[k]; // %A (0, 0.5a, 1.0a, 0.5-0.5a, 0.5, 0.5+0.5a, 1.0-a, 1.0-0.5a, 1.0)
double &pG = pGs[k * 9 + l * 3 + m];
// l m p pR pA
// 0 0 0 1-e e/3 1-e
// 0 1 a/2 (1-e)(1-a/2) + e/3*a/2
// 0 2 a
// 1 0 0.5-a/2
// 1 1 0.5 1-e e/3 0.5-e/3
// 1 2 0.5+a/2
// 2 0 1-a
// 2 1 1-a/2
// 2 2 1 1-e e/3 e/3
pG *= (pR * (1.0 - p) + pA * p);
if (maxpG < pG)
maxpG = pG;
}
}
}
for (int32_t k = 0; k < nAlpha; ++k) {
// normalize
for (int32_t l = 0; l < 3; ++l) { // 1-Alpha
for (int32_t m = 0; m < 3; ++m) { // Alpha
pGs[k * 9 + l * 3 + m] /= maxpG;
}
}
}
}
// now pG[k*9 + l*3 + m] = Pr(Data|g1=l,g2=m,alpha=gridAlpha[k])
// add marginal probability in genotype likelihood
double maxpG = 0;
for (int32_t k = 0; k < nAlpha; ++k) {
// normalize
for (int32_t l = 0; l < 3; ++l) { // 1-Alpha
for (int32_t m = 0; m < 3; ++m) { // Alpha
double &pG = pGs[k * 9 + l * 3 + m];
pG += 1e-10;
if (maxpG < pG)
maxpG = pG;
}
}
}
// normalize the likelihoods
for (int32_t k = 0; k < nAlpha; ++k) {
// normalize
for (int32_t l = 0; l < 3; ++l) { // 1-Alpha
for (int32_t m = 0; m < 3; ++m) { // Alpha
pGs[k * 9 + l * 3 + m] /= maxpG;
}
}
}
// calculate the sum of posterior probabilities
int32_t isnp = it->first;
std::vector<double> sumPs(nAlpha, 0);
double p;
int32_t j, k, l, m, n;
if (scl.snps[isnp].gps != NULL) { // skip if the marker does not have genotypes
for (j = jbeg; j < jend; ++j) { // j is the intended sample
// pairwise LLK
for (k = 0; k < nv; ++k) { // k is the contaminating sample
std::fill(sumPs.begin(), sumPs.end(), 0); // for computing llksAB
for (l = 0; l < 3; ++l) {
for (m = 0; m < 3; ++m) {
p = scl.snps[isnp].gps[j * 3 + l] * scl.snps[isnp].gps[k * 3 + m];
for (n = 0; n < nAlpha; ++n)
sumPs[n] += (p * pGs[n * 9 + l * 3 + m]); // sumP is the per-SNP likelihood
}
}
for (n = 0; n < nAlpha; ++n)
llksAB[(j - jbeg) * nv * nAlpha + k * nAlpha + n] += log(sumPs[n]);
}
// A0 LLK
std::fill(sumPs.begin(), sumPs.end(), 0); // for computing llksA0
for (l = 0; l < 3; ++l) {
for (m = 0; m < 3; ++m) {
//p = gpA0[isnp*nv*9 + j*9 + l*3 + m];
p = gpA0[isnp][j * 9 + l * 3 + m];
for (n = 0; n < nAlpha; ++n)
sumPs[n] += (p * pGs[n * 9 + l * 3 + m]);
}
}
for (n = 0; n < nAlpha; ++n)
llksA0[(j - jbeg) * nAlpha + n] += log(sumPs[n]);
}
std::fill(sumPs.begin(), sumPs.end(), 0); // for computing llks00
for (l = 0; l < 3; ++l) {
for (m = 0; m < 3; ++m) {
//p = gp00[isnp*9 + l*3 + m];
p = gp00[isnp][l * 3 + m];
for (n = 0; n < nAlpha; ++n)
sumPs[n] += (p * pGs[n * 9 + l * 3 + m]);
}
}
for (n = 0; n < nAlpha; ++n)
llks00[n] += log(sumPs[n]);
}
} // finished calculating genotype likelihoods
// normalize by max likelihood
//double maxLLK = -1e300;
//for(j=jbeg; j < jend; ++j) {
// for(k=0; k < nv; ++k) {
//for(n=0; n < nAlpha; ++n) {
// if ( maxLLK < llksAB[(j-jbeg)*nv*nAlpha+k*nAlpha+n] )
// maxLLK = llksAB[(j-jbeg)*nv*nAlpha+k*nAlpha+n];
// }
// }
//}
int32_t sBest = -1, sNext = -1, dBest1 = -1, dBest2 = -1, dNext1 = -1, dNext2 = -1, dblBestAlpha = -1, dblNextAlpha = -1;
double sngBestLLK = -1e300, sngNextLLK = -1e300;
double dblBestLLK = -1e300, dblNextLLK = -1e300;
double sumLLK = -1e-300, sngLLK = -1e-300;
double bestPP = -1e300, sngPP = -1e300, sngOnlyPP = -1e300;
double log_single_prior = log((1.0 - doublet_prior) / nv);
double log_doublet_prior1 = log(doublet_prior / nv / (nv - 1.) / (nAlpha - 1.));
double log_doublet_prior2 = log(doublet_prior / nv / (nv - 1.) / (nAlpha - 1.) * 2);
//double log_doublet_prior = log(doublet_prior/nSamples/(nSamples-1)/(nAlpha-1)*2);
// llksAB[(j-jbeg)*nv*nAlpha + ANY*nAlpha + 0] -- singlet likelihood
// llksAB[(j-jbed)*nv*nAlpha + k*nAlpha + n] -- doublet likelihood
// calculate posterior probability of singlets and doublets
//double sumSingle = 0, sumDouble = 0;
for (j = jbeg; j < jend; ++j) {
//sumSingle += (exp(llksAB[(j-jbeg)*nv*nAlpha] - maxLLK)* (1.-doublet_prior) / (jend-jbeg));
sumLLK = logAdd(sumLLK, llksAB[(j - jbeg) * nv * nAlpha] + log_single_prior);
sngLLK = logAdd(sngLLK, llksAB[(j - jbeg) * nv * nAlpha] + log_single_prior);
for (k = 0; k < nv; ++k) {
if (j == k) continue; // singlets
for (n = 1; n < nAlpha; ++n) {
if (gridAlpha[n] == 0.5) {
if (k > j) continue;
sumLLK = logAdd(sumLLK, llksAB[(j - jbeg) * nv * nAlpha + k * nAlpha + n] + log_doublet_prior2);
} else
sumLLK = logAdd(sumLLK, llksAB[(j - jbeg) * nv * nAlpha + k * nAlpha + n] + log_doublet_prior1);
//sumDouble += ( exp(llksAB[(j-jbeg)*nv*nAlpha+k*nAlpha+n] - maxLLK)* doublet_prior / (jend-jbeg) / (nv-1) / (nAlpha-1) / (gridAlpha[n] == 0.5 ? 2.0 : 1.0));
}
}
}
//int32_t iSing1 = -1, iSing2 = -1;
//double maxSing1 = -1e300, maxSing2 = -1e300;
// scan for best-matching singlets
for (j = jbeg; j < jend; ++j) {
if (sngBestLLK < llksAB[(j - jbeg) * nv * nAlpha]) {
sngNextLLK = sngBestLLK;
sNext = sBest;
sBest = j;
sngBestLLK = llksAB[(j - jbeg) * nv * nAlpha];
} else if (sngNextLLK < llksAB[(j - jbeg) * nv * nAlpha]) {
sNext = j;
sngNextLLK = llksAB[(j - jbeg) * nv * nAlpha];
}
/* hprintf(wsing2,"%s\t%s\t%d\t%d\t%d\t%d\t%.4lf\t%.4lf\t%.3lg\n",
it0->first.c_str(),
vr.get_sample_id_at(j),
scl.cell_totl_reads[i],
scl.cell_pass_reads[i],
scl.cell_uniq_reads[i],
(int32_t)scl.cell_umis[i].size(),
llksAB[(j-jbeg)*nv*nAlpha],
llks00[0],
exp(llksAB[(j-jbeg)*nv*nAlpha]-maxLLK) * (1.-doublet_prior) / (jend-jbeg) / sumSingle); */
}
/*
if ( writePair ) {
for(j=jbeg; j < jend; ++j) {
hprintf(wpair,"%s\t%s\t%s\t%.3lf\t%.5lf\t%.5lg\n",
it0->first.c_str(),
vr.get_sample_id_at(j),
vr.get_sample_id_at(j),
gridAlpha[0],
llksAB[(j-jbeg)*nv*nAlpha],
exp(llksAB[(j-jbeg)*nv*nAlpha]-maxLLK)*(1.-doublet_prior)/(jend-jbeg)/(sumSingle+sumDouble));
for(k=0; k < nv; ++k) {
for(n=0; n < nAlpha; ++n) {
if ( ( n > 0 ) && ( j != k ) ) {
if ( ( j > k ) && ( gridAlpha[n] == 0.5 ) ) continue;
hprintf(wpair,"%s\t%s\t%s\t%.3lf\t%.5lf\t%.5lg\n",
it0->first.c_str(),
vr.get_sample_id_at(j),
vr.get_sample_id_at(k),
gridAlpha[n],
llksAB[(j-jbeg)*nv*nAlpha+k*nAlpha+n],
exp(llksAB[(j-jbeg)*nv*nAlpha+k*nAlpha+n]-maxLLK)*doublet_prior/(jend-jbeg)/(nv-1)/(nAlpha-1)/(sumSingle+sumDouble));
}
}
}
}
}
*/
//int jBest = -1, kBest = -1, alphaBest = -1;
//double maxAB = -1e300;
for (j = jbeg; j < jend; ++j) {
for (k = 0; k < nv; ++k) {
if (j == k) continue;
for (n = 1; n < nAlpha; ++n) {
if (dblBestLLK < llksAB[(j - jbeg) * nv * nAlpha + k * nAlpha + n]) {
dNext1 = dBest1;
dNext2 = dBest2;
dblNextAlpha = dblBestAlpha;
dblNextLLK = dblBestLLK;
dBest1 = j;
dBest2 = k;
dblBestAlpha = n;
dblBestLLK = llksAB[(j - jbeg) * nv * nAlpha + k * nAlpha + n];
} else if (dblNextLLK < llksAB[(j - jbeg) * nv * nAlpha + k * nAlpha + n]) {
dNext1 = j;
dNext2 = k;
dblNextAlpha = n;
dblNextLLK = llksAB[(j - jbeg) * nv * nAlpha + k * nAlpha + n];
}
}
}
}
//double singLLK1 = llksAB[(iSing1-jbeg)*nv*nAlpha];
//double singLLK2 = llksAB[(iSing2-jbeg)*nv*nAlpha];
//double singLLK1 = llksAB[(iBest[i]-jbeg)*nv*nAlpha];
//double singLLK2 = llksAB[(iNext[i]-jbeg)*nv*nAlpha];
//double singLLK0 = llks00[0];
//double pairLLK12 = llksAB[(jBest-jbeg)*nv*nAlpha+kBest*nAlpha+alphaBest];
//double pairLLK1 = llksAB[(jBest-jbeg)*nv*nAlpha];
//double pairLLK2 = llksAB[(kBest-jbeg)*nv*nAlpha];
//double pairLLK10 = llksAB[(jBest-jbeg)*nv*nAlpha+alphaBest];
//double pairLLK20 = llksAB[(kBest-jbeg)*nv*nAlpha+alphaBest];
//double pairLLK00 = llks00[alphaBest];
//double postDoublet = sumDouble/(sumSingle+sumDouble);
//double postSinglet = exp(singLLK1 - maxLLK) * (1.-doublet_prior) / (jend-jbeg) / sumSingle;
std::string bestType, nextType;
int32_t jBest = -1, kBest = -1, jNext = -1, kNext = -1, alphaBest = -1, alphaNext = -1;
double bestLLK = -1e300, nextLLK = -1e300;
if (dblBestLLK > sngBestLLK + 2) { // bestcall is doublet
bestType = "DBL";
bestPP = exp(
dblBestLLK + ((gridAlpha[dblBestAlpha] == 0.5) ? log_doublet_prior2 : log_doublet_prior1) - sumLLK);
jBest = dBest1;
kBest = dBest2;
bestLLK = dblBestLLK;
alphaBest = dblBestAlpha;
if (dblNextLLK > sngBestLLK + 2) {
nextType = "DBL";
jNext = dNext1;
kNext = dNext2;
nextLLK = dblNextLLK;
alphaNext = dblNextAlpha;
} else {
nextType = "SNG";
jNext = kNext = sBest;
nextLLK = sngBestLLK;
alphaNext = 0;
}
} else if (sngBestLLK > sngNextLLK + 2) {
bestType = "SNG";
bestPP = sngBestLLK + log_single_prior - sumLLK;
jBest = kBest = sBest;
bestLLK = sngBestLLK;
alphaBest = 0;
if (dblBestLLK > sngNextLLK + 2) {
nextType = "DBL";
jNext = dBest1;
kNext = dBest2;
nextLLK = dblBestLLK;
alphaNext = dblBestAlpha;
} else {
nextType = "SNG";
jNext = kNext = sNext;
nextLLK = sngNextLLK;
alphaNext = 0;
}
} else {
bestType = "AMB";
bestPP = sngBestLLK + log_single_prior - sumLLK;
jBest = kBest = sBest;
bestLLK = sngBestLLK;
alphaBest = 0;
if (dblBestLLK > sngNextLLK + 2) {
nextType = "DBL";
jNext = dBest1;
kNext = dBest2;
nextLLK = dblBestLLK;
alphaNext = dblBestAlpha;
} else {
nextType = "SNG";
jNext = kNext = sNext;
nextLLK = sngNextLLK;
alphaNext = 0;
}
}
sngPP = exp(sngLLK - sumLLK);
sngOnlyPP = exp(sngBestLLK + log_single_prior - sngLLK);
//BARCODE NUM.SNPS NUM.READS DROPLET.TYPE BEST.GUESS BEST.LLK NEXT.GUESS NEXT.LLK DIFF.LLK.BEST.NEXT BEST.POSTERIOR SNG.POSTERIOR SNG.BEST.GUESS SNG.BEST.LLK SNG.NEXT.GUESS SNG.NEXT.LLK SNG.ONLY.POSTERIOR DBL.BEST.GUESS DBL.BEST.LLK DIFF.LLK.SNG.DBL
hprintf(wbest,
"%s\t%u\t%d\t%s\t%s,%s\t%.2lf\t%s,%s\t%.2lf\t%.2lg\t%.2lg\t%.2lg\t%s\t%.2lf\t%s\t%.2lf\t%.5lf\t%s,%s\t%.2lf\t%.2lf\n",