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cmd_cram_demuxlet.cpp
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cmd_cram_demuxlet.cpp
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#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 = 20;
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 minUMIs = 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-umi", &minUMIs, "Minimum number of UMIs 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.minRead = minTotalReads;
scl.minUMI = minUMIs;
scl.minSNP = minCoveredSNPs;
scl.capBQ = capBQ;
scl.minBQ = minBQ;
if ( !groupList.empty() ) {
scl.load_valid_barcodes(groupList.c_str());
}
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 + minUMIs + minCoveredSNPs < 0 ) {
for(int32_t i=0; i < scl.nbcs; ++i) {
if ( ( scl.cell_totl_reads[i] < minTotalReads ) || ( scl.cell_uniq_reads[i] < minUMIs) || ( (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, "INT_ID\tBARCODE\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] < minUMIs) || ( (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);
hprintf(wbest, "%d\t%s\t%u\t%d\t%s\t%s,%s,%.2lf\t%.2lf\t%s,%s,%.2lf\t%.2lf\t%.2lf\t%.2lg\t%.2lg\t%s\t%.2lf\t%s\t%.2lf\t%.5lf\t%s,%s,%.2lf\t%.2lf\t%.2lf\n",
ncells,
it0->first.c_str(),
scl.cell_umis[i].size(),
scl.cell_uniq_reads[i],
bestType.c_str(),
vr.get_sample_id_at(jBest), vr.get_sample_id_at(kBest), gridAlpha[alphaBest],
bestLLK,