/* The MIT License Copyright (c) 2013-2018 Genome Research Ltd. Author: Petr Danecek Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ #include #include #include #include #include #include #include #include #include #include #include "bcftools.h" #include "peakfit.h" typedef struct { int nvals; // all values, including RR,AA peaks double *xvals; // pointer to args_t.xvals double *yvals; int copy_number; // heuristics to skip futile CN1 fits when no het peak is detected int irr, ira, iaa; // chop off RR and AA peaks char *chr; } dist_t; typedef struct { int ndist, nbins, ra_rr_scaling, smooth; double *xvals; dist_t *dist; char **argv, *output_dir; double fit_th, peak_symmetry, cn_penalty, min_peak_size, min_fraction; int argc, plot, verbose, regions_is_file, targets_is_file, include_aa, force_cn; char *dat_fname, *fname, *regions_list, *targets_list, *sample; FILE *dat_fp; } args_t; FILE *open_file(char **fname, const char *mode, const char *fmt, ...); static void init_dist(args_t *args, dist_t *dist, int verbose) { // isolate RR and AA peaks and rescale so that they are comparable to hets int i, irr, iaa, n = dist->nvals; // smooth the distribution, this is just to find the peaks double *tmp = (double*) malloc(sizeof(double)*n); int win = args->smooth ? fabs(args->smooth)*2 + 1 : 7; // must be an odd number int hwin = win/2; double avg = tmp[0] = dist->yvals[0]; for (i=1; iyvals[2*i-1]; tmp[i] = avg/(2*i+1); } avg = 0; for (i=0; iyvals[i]; if ( i>=win-1 ) { tmp[i-hwin] = avg/win; avg -= dist->yvals[i-win+1]; } } for (i=n-hwin; iyvals[i-hwin]; hwin--; tmp[i] = avg/(2*hwin+1); avg -= dist->yvals[i-hwin]; } // find the extremes; first a simple approach: find a gap for (irr=0,i=0; i=n/2; i--) if ( tmp[i] < tmp[iaa] ) iaa = i; irr += win*0.5; iaa += win*0.5; if ( iaa>=n ) iaa = n-1; if ( irr>=iaa ) error("FIXME: oops, dist normalization failed for %s: %d vs %d\n", dist->chr,irr,iaa); // we may need to be smarter if ( args->smooth>0 ) for (i=0; iyvals[i] = tmp[i]; free(tmp); // clean the data: the AA peak is occasionally not centered at 1.0 but is closer to the center, chop off int imax_aa = iaa; for (i=iaa; iyvals[imax_aa] < dist->yvals[i] ) imax_aa = i; dist->nvals = imax_aa+1; if ( iaa>=dist->nvals ) iaa = dist->nvals-1; // find the maximum and scale the peaks (first draft: no attempt to join the segments smootly) double max_rr = 0, max_aa = 0, max_ra = 0, srr = 0, saa = 0, sra = 0; for (i=0; iyvals[i]; if ( max_rr < dist->yvals[i] ) max_rr = dist->yvals[i]; } for (i=irr; i<=iaa; i++) { sra += dist->yvals[i]; if ( max_ra < dist->yvals[i] ) max_ra = dist->yvals[i]; } for (i=iaa+1; iyvals[i]; if ( max_aa < dist->yvals[i] ) max_aa = dist->yvals[i]; } // Does the het peak exist at all? Usually the numbers are as follows: // 1: cn=0 ra/rr=0.205730 aa/ra=0.674922 nra=7066 // 20: cn=0 ra/rr=0.258019 aa/ra=0.548929 nra=2381 // X: cn=0 ra/rr=0.005976 aa/ra=44.116667 nra=60 // Y: cn=0 ra/rr=0.008316 aa/ra=7.250000 nra=12 // MT: cn=0 ra/rr=0.013699 aa/ra=0.666667 nra=3 if ( !args->ra_rr_scaling ) max_ra = max_aa = max_rr; if ( !sra || (sra/srr<0.1 && saa/sra>1.0) ) // too few hets, CN1 { max_ra = max_aa; dist->copy_number = 1; } else if ( sra/srr<0.1 || saa/sra>1.0 ) { max_ra = max_aa; dist->copy_number = -1; // unknown copy number } if ( max_rr ) for (i=0; iyvals[i] /= max_rr; if ( max_ra ) for (i=irr; i<=iaa; i++) dist->yvals[i] /= max_ra; if ( max_aa ) for (i=iaa+1; iyvals[i] /= max_aa; dist->irr = irr; dist->iaa = iaa; dist->ira = n*0.5; if ( verbose ) fprintf(stderr,"%s:\t irr,ira,iaa=%.2f,%.2f,%.2f \t cn=%2d \t ra/rr=%f \t aa/ra=%f \t nra=%d\n", dist->chr, dist->xvals[irr],dist->xvals[dist->ira],dist->xvals[iaa], dist->copy_number,sra/srr,saa/sra, (int)sra); } static void init_data(args_t *args) { bcf_srs_t *files = bcf_sr_init(); if ( args->regions_list ) { if ( bcf_sr_set_regions(files, args->regions_list, args->regions_is_file)<0 ) error("Failed to read the regions: %s\n", args->regions_list); } if ( args->targets_list ) { if ( bcf_sr_set_targets(files, args->targets_list, args->targets_is_file, 0)<0 ) error("Failed to read the targets: %s\n", args->targets_list); } if ( !bcf_sr_add_reader(files, args->fname) ) error("Failed to open %s: %s\n", args->fname,bcf_sr_strerror(files->errnum)); bcf_hdr_t *hdr = files->readers[0].header; if ( !args->sample ) { if ( bcf_hdr_nsamples(hdr)>1 ) error("Missing the option -s, --sample\n"); args->sample = hdr->samples[0]; } else if ( bcf_hdr_id2int(hdr,BCF_DT_SAMPLE,args->sample)<0 ) error("No such sample: %s\n", args->sample); int ret = bcf_hdr_set_samples(hdr, args->sample, 0); if ( ret<0 ) error("Error setting the sample: %s\n", args->sample); if ( !bcf_hdr_idinfo_exists(hdr,BCF_HL_FMT,bcf_hdr_id2int(hdr,BCF_DT_ID,"BAF")) ) error("The tag FORMAT/BAF is not present in the VCF: %s\n", args->fname); int i; args->xvals = (double*) calloc(args->nbins,sizeof(double)); for (i=0; inbins; i++) args->xvals[i] = 1.0*i/(args->nbins-1); // collect BAF distributions for all chromosomes int idist = -1, nbaf = 0, nprocessed = 0, ntotal = 0, prev_chr = -1; float *baf = NULL; while ( bcf_sr_next_line(files) ) { ntotal++; bcf1_t *line = bcf_sr_get_line(files,0); if ( bcf_get_format_float(hdr,line,"BAF",&baf,&nbaf) != 1 ) continue; if ( bcf_float_is_missing(baf[0]) ) continue; nprocessed++; if ( prev_chr==-1 || prev_chr!=line->rid ) { // new chromosome idist = args->ndist++; args->dist = (dist_t*) realloc(args->dist, sizeof(dist_t)*args->ndist); memset(&args->dist[idist],0,sizeof(dist_t)); args->dist[idist].chr = strdup(bcf_seqname(hdr,line)); args->dist[idist].yvals = (double*) calloc(args->nbins,sizeof(double)); args->dist[idist].xvals = args->xvals; args->dist[idist].nvals = args->nbins; prev_chr = line->rid; } int bin = baf[0]*(args->nbins-1); args->dist[idist].yvals[bin]++; // the distribution } free(baf); bcf_sr_destroy(files); for (idist=0; idistndist; idist++) { #if 0 int j; for (j=0; jnbins; j++) { double x = args->dist[idist].xvals[j]; args->dist[idist].yvals[j] = exp(-(x-0.5)*(x-0.5)/1e-3); } #endif init_dist(args, &args->dist[idist],args->verbose); } args->dat_fp = open_file(&args->dat_fname,"w","%s/dist.dat", args->output_dir); fprintf(args->dat_fp, "# This file was produced by: bcftools polysomy(%s+htslib-%s), the command line was:\n", bcftools_version(),hts_version()); fprintf(args->dat_fp, "# \t bcftools %s ", args->argv[0]); for (i=1; iargc; i++) fprintf(args->dat_fp, " %s",args->argv[i]); fprintf(args->dat_fp,"\n#\n"); fprintf(args->dat_fp,"# DIST\t[2]Chrom\t[3]BAF\t[4]Normalized Count\n"); fprintf(args->dat_fp,"# FIT\t[2]Goodness of Fit\t[3]iFrom\t[4]iTo\t[5]The Fitted Function\n"); fprintf(args->dat_fp,"# CN\t[2]Chrom\t[3]Estimated Copy Number\t[4]Absolute fit deviation\n"); char *fname = NULL; FILE *fp = open_file(&fname,"w","%s/dist.py", args->output_dir); //-------- matplotlib script -------------- fprintf(fp, "#!/usr/bin/env python\n" "#\n" "import matplotlib as mpl\n" "mpl.use('Agg')\n" "import matplotlib.pyplot as plt\n" "import csv,sys,argparse\n" "from math import exp\n" "\n" "outdir = '%s'\n" "\n" "def read_dat(dat,fit,cn):\n" " csv.register_dialect('tab', delimiter='\t', quoting=csv.QUOTE_NONE)\n" " with open(outdir+'/dist.dat', 'r') as f:\n" " reader = csv.reader(f, 'tab')\n" " for row in reader:\n" " if row[0][0]=='#': continue\n" " type = row[0]\n" " chr = row[1]\n" " if type=='DIST':\n" " if chr not in dat: dat[chr] = []\n" " dat[chr].append(row)\n" " elif type=='FIT':\n" " if chr not in fit: fit[chr] = []\n" " fit[chr].append(row)\n" " elif type=='CN':\n" " cn[chr] = row[2]\n" "\n" "def plot_dist(dat,fit,chr):\n" " fig, ax = plt.subplots(1, 1, figsize=(7,5))\n" " ax.plot([x[2] for x in dat[chr]],[x[3] for x in dat[chr]],'k-',label='Distribution')\n" " if chr in fit:\n" " for i in range(len(fit[chr])):\n" " pfit = fit[chr][i]\n" " exec('def xfit(x): return '+pfit[5])\n" " istart = int(pfit[3])\n" " iend = int(pfit[4])+1\n" " vals = dat[chr][istart:iend]\n" " args = {}\n" " if i==0: args = {'label':'Target to Fit'}\n" " ax.plot([x[2] for x in vals],[x[3] for x in vals],'r-',**args)\n" " if i==0: args = {'label':'Best Fit'}\n" " ax.plot([x[2] for x in vals],[xfit(float(x[2])) for x in vals],'g-',**args)\n" " ax.set_title('BAF distribution, chr'+chr)\n" " ax.set_xlabel('BAF')\n" " ax.set_ylabel('Frequency')\n" " ax.legend(loc='best',prop={'size':7},frameon=False)\n" " plt.savefig(outdir+'/dist.chr'+chr+'.png')\n" " plt.close()\n" "\n" "def plot_copy_number(cn):\n" " fig, ax = plt.subplots(1, 1, figsize=(7,5))\n" " xlabels = sorted(cn.keys())\n" " xvals = range(len(xlabels))\n" " yvals = [float(cn[x]) for x in xlabels]\n" " ax.plot(xvals,yvals,'o',color='red')\n" " for i in range(len(xvals)):\n" " if yvals[i]==-1: ax.annotate('?', xy=(xvals[i],0.5),va='center',ha='center',color='red',fontweight='bold')\n" " ax.tick_params(axis='both', which='major', labelsize=9)\n" " ax.set_xticks(xvals)\n" " ax.set_xticklabels(xlabels,rotation=45)\n" " ax.set_xlim(-1,len(xlabels))\n" " ax.set_ylim(0,5.0)\n" " ax.set_yticks([1.0,2.0,3.0,4.0])\n" " ax.set_xlabel('Chromosome')\n" " ax.set_ylabel('Copy Number')\n" " plt.savefig(outdir+'/copy-number.png')\n" " plt.close()\n" "\n" "class myParser(argparse.ArgumentParser):\n" " def error(self, message):\n" " self.print_help()\n" " sys.stderr.write('error: %%s\\n' %% message)\n" " sys.exit(2)\n" "\n" "def main():\n" " parser = myParser()\n" " parser.add_argument('-a', '--all', action='store_true', help='Create all plots')\n" " parser.add_argument('-c', '--copy-number', action='store_true', help='Create copy-number plot')\n" " parser.add_argument('-d', '--distrib', metavar='CHR', help='Plot BAF distribution of a single chromosome')\n" " args = parser.parse_args()\n" " dat = {}; fit = {}; cn = {}\n" " read_dat(dat,fit,cn)\n" " if args.distrib!=None:\n" " plot_dist(dat,fit,args.distrib)\n" " if args.all:\n" " for chr in dat: plot_dist(dat,fit,chr)\n" " plot_copy_number(cn)\n" " elif args.copy_number:\n" " plot_copy_number(cn)\n" " else:\n" " for chr in dat: plot_dist(dat,fit,chr)\n" "\n" "if __name__ == '__main__':\n" " main()\n", args->output_dir); //--------------------------------------- chmod(fname, S_IWUSR|S_IRUSR|S_IRGRP|S_IROTH|S_IXUSR|S_IXGRP|S_IXOTH); free(fname); fclose(fp); } static void destroy_data(args_t *args) { int i; for (i=0; indist; i++) { free(args->dist[i].chr); free(args->dist[i].yvals); } free(args->dist); free(args->xvals); free(args->dat_fname); fclose(args->dat_fp); } static void save_dist(args_t *args, dist_t *dist) { int i; for (i=0; inbins; i++) fprintf(args->dat_fp,"DIST\t%s\t%f\t%f\n",dist->chr,dist->xvals[i],dist->yvals[i]); } static void fit_curves(args_t *args) { peakfit_t *pkf = peakfit_init(); peakfit_verbose(pkf,args->verbose); int i, nmc = 50; for (i=0; indist; i++) { dist_t *dist = &args->dist[i]; save_dist(args, &args->dist[i]); if ( dist->copy_number!=0 ) { fprintf(args->dat_fp,"CN\t%s\t%.2f\n", dist->chr,(float)dist->copy_number); continue; } if ( args->verbose ) fprintf(stderr,"%s:\n", dist->chr); int nrr_aa = dist->iaa - dist->irr + 1; int nrr_ra = dist->ira - dist->irr + 1; int naa_max = dist->nvals - dist->iaa; double xrr = dist->xvals[dist->irr], *xrr_vals = &dist->xvals[dist->irr], *yrr_vals = &dist->yvals[dist->irr]; double xaa = dist->xvals[dist->iaa], *xaa_vals = &dist->xvals[dist->iaa], *yaa_vals = &dist->yvals[dist->iaa]; double xra = dist->xvals[dist->ira]; double xmax = dist->xvals[dist->nvals-1]; // CN2 double cn2aa_fit = 0, cn2ra_fit, cn2_fit; char *cn2aa_func = 0, *cn2ra_func; double cn2aa_params[3] = {1,1,1} ,cn2ra_params[3]; if ( args->include_aa ) { peakfit_reset(pkf); peakfit_add_exp(pkf, 1.0,1.0,0.2, 5); peakfit_set_mc(pkf, 0.01,0.3,2,nmc); peakfit_set_mc(pkf, 0.05,1.0,0,nmc); cn2aa_fit = peakfit_run(pkf, naa_max, xaa_vals, yaa_vals); cn2aa_func = strdup(peakfit_sprint_func(pkf)); peakfit_get_params(pkf,0,cn2aa_params,3); } peakfit_reset(pkf); peakfit_add_bounded_gaussian(pkf, 1.0,0.5,0.03, 0.45,0.55, 7); peakfit_set_mc(pkf, 0.01,0.3,2,nmc); peakfit_set_mc(pkf, 0.05,1.0,0,nmc); cn2ra_fit = peakfit_run(pkf, nrr_aa,xrr_vals,yrr_vals); cn2ra_func = strdup(peakfit_sprint_func(pkf)); cn2_fit = cn2ra_fit + cn2aa_fit; peakfit_get_params(pkf,0,cn2ra_params,3); // CN3: fit two peaks, then enforce the symmetry and fit again double cn3rra_params[5], cn3raa_params[5], *cn3aa_params = cn2aa_params; double cn3aa_fit = cn2aa_fit, cn3ra_fit; char *cn3aa_func = cn2aa_func, *cn3ra_func; double min_dx3 = 0.5 - 1./(args->min_fraction+2); peakfit_reset(pkf); peakfit_add_bounded_gaussian(pkf, 1.0,1/3.,0.03, xrr,xra-min_dx3, 7); peakfit_set_mc(pkf, xrr,xra-min_dx3, 1,nmc); peakfit_add_bounded_gaussian(pkf, 1.0,2/3.,0.03, xra+min_dx3,xaa, 7); peakfit_set_mc(pkf, xra+min_dx3,xaa, 1,nmc); peakfit_run(pkf, nrr_aa, xrr_vals, yrr_vals); // force symmetry around x=0.5 peakfit_get_params(pkf,0,cn3rra_params,5); peakfit_get_params(pkf,1,cn3raa_params,5); double cn3_dx = (0.5-cn3rra_params[1] + cn3raa_params[1]-0.5)*0.5; if ( cn3_dx > 0.5/3 ) cn3_dx = 0.5/3; // CN3 peaks should not be separated by more than 1/3 peakfit_reset(pkf); peakfit_add_gaussian(pkf, cn3rra_params[0],0.5-cn3_dx,cn3rra_params[2], 5); peakfit_add_gaussian(pkf, cn3raa_params[0],0.5+cn3_dx,cn3raa_params[2], 5); cn3ra_fit = peakfit_run(pkf, nrr_aa, xrr_vals, yrr_vals); cn3ra_func = strdup(peakfit_sprint_func(pkf)); // compare peak sizes peakfit_get_params(pkf,0,cn3rra_params,3); peakfit_get_params(pkf,1,cn3raa_params,3); double cn3rra_size = cn3rra_params[0]*cn3rra_params[0]; double cn3raa_size = cn3raa_params[0]*cn3raa_params[0]; double cn3_dy = cn3rra_size > cn3raa_size ? cn3raa_size/cn3rra_size : cn3rra_size/cn3raa_size; double cn3_frac = (1 - 2*cn3rra_params[1]) / cn3rra_params[1]; double cn3_fit = cn3ra_fit + cn3aa_fit; // A very reasonable heuristics: check if the peak's width converged, exclude far too broad or far too narrow peaks if ( cn3rra_params[2]>0.3 || cn3raa_params[2]>0.3 ) cn3_fit = HUGE_VAL; if ( cn3rra_params[2]<1e-2 || cn3raa_params[2]<1e-2 ) cn3_fit = HUGE_VAL; // CN4 (contaminations) // - first fit only the [0,0.5] part of the data, then enforce the symmetry and fit again // - min_frac=1 (resp. 0.5) is interpreted as 50:50% (rep. 75:25%) contamination double cn4AAaa_params[3] = {1,1,1} ,cn4AAra_params[3] = {1,1,1}, cn4RAra_params[3], cn4RArr_params[5], cn4RAaa_params[5]; double cn4aa_fit = 0, cn4ra_fit; char *cn4aa_func = 0, *cn4ra_func; double min_dx4 = 0.25*args->min_fraction; if ( args->include_aa ) { peakfit_reset(pkf); peakfit_add_exp(pkf, 0.5,1.0,0.2, 5); peakfit_set_mc(pkf, 0.01,0.3,2,nmc); peakfit_add_bounded_gaussian(pkf, 0.4,(xaa+xmax)*0.5,2e-2, xaa,xmax, 7); peakfit_set_mc(pkf, xaa,xmax, 1,nmc); cn4aa_fit = peakfit_run(pkf, naa_max, xaa_vals,yaa_vals); cn4aa_func = strdup(peakfit_sprint_func(pkf)); peakfit_get_params(pkf,0,cn4AAaa_params,3); peakfit_get_params(pkf,1,cn4AAra_params,5); } peakfit_reset(pkf); // first fit only the [0,0.5] part of the data peakfit_add_gaussian(pkf, 1.0,0.5,0.03, 5); peakfit_add_bounded_gaussian(pkf, 0.6,0.3,0.03, xrr,xra-min_dx4, 7); peakfit_set_mc(pkf, xrr,xra-min_dx4,2,nmc); peakfit_run(pkf, nrr_ra , xrr_vals, yrr_vals); // now forcet symmetry around x=0.5 peakfit_get_params(pkf,0,cn4RAra_params,3); peakfit_get_params(pkf,1,cn4RArr_params,5); double cn4_dx = 0.5-cn4RArr_params[1]; if ( cn4_dx > 0.25 ) cn4_dx = 0.25; // CN4 peaks should not be separated by more than 0.5 peakfit_reset(pkf); peakfit_add_gaussian(pkf, cn4RAra_params[0],0.5,cn4RAra_params[2], 5); peakfit_add_gaussian(pkf, cn4RArr_params[0],0.5-cn4_dx,cn4RArr_params[2], 5); peakfit_add_gaussian(pkf, cn4RArr_params[0],0.5+cn4_dx,cn4RArr_params[2], 5); peakfit_set_mc(pkf, 0.1,cn4RAra_params[0],0,nmc); peakfit_set_mc(pkf, 0.01,0.1,2,nmc); cn4ra_fit = peakfit_run(pkf, nrr_aa , xrr_vals, yrr_vals); cn4ra_func = strdup(peakfit_sprint_func(pkf)); peakfit_get_params(pkf,0,cn4RAra_params,3); peakfit_get_params(pkf,1,cn4RArr_params,3); peakfit_get_params(pkf,2,cn4RAaa_params,3); double cn4RAra_size = cn4RAra_params[0]==0 ? HUGE_VAL : cn4RAra_params[0]*cn4RAra_params[0]; double cn4RArr_size = cn4RArr_params[0]*cn4RArr_params[0]; double cn4RAaa_size = cn4RAaa_params[0]*cn4RAaa_params[0]; double cn4RArr_dy = cn4RArr_size < cn4RAra_size ? cn4RArr_size/cn4RAra_size : cn4RAra_size/cn4RArr_size; double cn4RAaa_dy = cn4RAaa_size < cn4RAra_size ? cn4RAaa_size/cn4RAra_size : cn4RAra_size/cn4RAaa_size; double cn4_dy = cn4RArr_dy < cn4RAaa_dy ? cn4RArr_dy/cn4RAaa_dy : cn4RAaa_dy/cn4RArr_dy; double cn4_ymin = cn4RArr_size < cn4RAaa_size ? cn4RArr_size/cn4RAra_size : cn4RAaa_size/cn4RAra_size; cn4_dx = (cn4RAaa_params[1]-0.5) - (0.5-cn4RArr_params[1]); double cn4_frac = cn4RAaa_params[1] - cn4RArr_params[1]; double cn4_fit = cn4ra_fit + cn4aa_fit; // A very reasonable heuristics: check if the peak's width converged, exclude far too broad or far too narrow peaks if ( cn4RAra_params[2]>0.3 || cn4RArr_params[2]>0.3 || cn4RAaa_params[2]>0.3 ) cn4_fit = HUGE_VAL; if ( cn4RAra_params[2]<1e-2 || cn4RArr_params[2]<1e-2 || cn4RAaa_params[2]<1e-2 ) cn4_fit = HUGE_VAL; // Choose the best match char cn2_fail = '*', cn3_fail = '*', cn4_fail = '*'; if ( cn2_fit > args->fit_th ) cn2_fail = 'f'; if ( cn3_fit > args->fit_th ) cn3_fail = 'f'; else if ( cn3_dy < args->peak_symmetry ) cn3_fail = 'y'; // size difference is too big if ( cn4_fit > args->fit_th ) cn4_fail = 'f'; else if ( cn4_ymin < args->min_peak_size ) cn4_fail = 'y'; // side peak is too small else if ( cn4_dy < args->peak_symmetry ) cn4_fail = 'Y'; // size difference is too big else if ( cn4_dx > 0.1 ) cn4_fail = 'x'; // side peaks placed assymetrically double cn = -1, fit = cn2_fit; if ( cn2_fail == '*' ) { cn = 2; fit = cn2_fit; } if ( cn3_fail == '*' ) { // Use cn_penalty as a tiebreaker. If set to 0.3, cn3_fit must be 30% smaller than cn2_fit. if ( cn<0 || cn3_fit < (1-args->cn_penalty) * fit ) { cn = 2 + cn3_frac; fit = cn3_fit; if ( cn2_fail=='*' ) cn2_fail = 'p'; } else cn3_fail = 'p'; } if ( cn4_fail == '*' ) { if ( cn<0 || cn4_fit < (1-args->cn_penalty) * fit ) { cn = 3 + cn4_frac; fit = cn4_fit; if ( cn2_fail=='*' ) cn2_fail = 'p'; if ( cn3_fail=='*' ) cn3_fail = 'p'; } else cn4_fail = 'p'; } if ( args->verbose ) { fprintf(stderr,"\tcn2 %c fit=%e\n", cn2_fail, cn2_fit); fprintf(stderr,"\t .. %e\n", cn2ra_fit); fprintf(stderr,"\t RA: %f %f %f\n", cn2ra_params[0],cn2ra_params[1],cn2ra_params[2]); fprintf(stderr,"\t .. %e\n", cn2aa_fit); fprintf(stderr,"\t AA: %f %f %f\n", cn2aa_params[0],cn2aa_params[1],cn2aa_params[2]); fprintf(stderr,"\t func:\n"); fprintf(stderr,"\t %s\n", cn2ra_func); fprintf(stderr,"\t %s\n", cn2aa_func); fprintf(stderr,"\n"); fprintf(stderr,"\tcn3 %c fit=%e frac=%f symmetry=%f\n", cn3_fail, cn3_fit, cn3_frac, cn3_dy); fprintf(stderr,"\t .. %e\n", cn3ra_fit); fprintf(stderr,"\t RRA: %f %f %f\n", cn3rra_params[0],cn3rra_params[1],cn3rra_params[2]); fprintf(stderr,"\t RAA: %f %f %f\n", cn3raa_params[0],cn3raa_params[1],cn3raa_params[2]); fprintf(stderr,"\t .. %e\n", cn3aa_fit); fprintf(stderr,"\t AAA: %f %f %f\n", cn3aa_params[0],cn3aa_params[1],cn3aa_params[2]); fprintf(stderr,"\t func:\n"); fprintf(stderr,"\t %s\n", cn3ra_func); fprintf(stderr,"\t %s\n", cn3aa_func); fprintf(stderr,"\n"); fprintf(stderr,"\tcn4 %c fit=%e frac=%f symmetry=%f ymin=%f\n", cn4_fail, cn4_fit, cn4_frac, cn4_dy, cn4_ymin); fprintf(stderr,"\t .. %e\n", cn4ra_fit); fprintf(stderr,"\t RArr: %f %f %f\n", cn4RArr_params[0],cn4RArr_params[1],cn4RArr_params[2]); fprintf(stderr,"\t RAra: %f %f %f\n", cn4RAra_params[0],cn4RAra_params[1],cn4RAra_params[2]); fprintf(stderr,"\t RAaa: %f %f %f\n", cn4RAaa_params[0],cn4RAaa_params[1],cn4RAaa_params[2]); fprintf(stderr,"\t .. %e\n", cn4aa_fit); fprintf(stderr,"\t AAaa: %f %f %f\n", cn4AAaa_params[0],cn4AAaa_params[1],cn4AAaa_params[2]); fprintf(stderr,"\t func:\n"); fprintf(stderr,"\t %s\n", cn4ra_func); fprintf(stderr,"\t %s\n", cn4aa_func); fprintf(stderr,"\n"); } if ( args->force_cn==2 || cn2_fail == '*' ) { fprintf(args->dat_fp,"FIT\t%s\t%e\t%d\t%d\t%s\n", dist->chr,cn2ra_fit,dist->irr,dist->iaa,cn2ra_func); if ( cn2aa_func ) fprintf(args->dat_fp,"FIT\t%s\t%e\t%d\t%d\t%s\n", dist->chr,cn2aa_fit,dist->iaa,dist->nvals-1,cn2aa_func); } if ( args->force_cn==3 || cn3_fail == '*' ) { fprintf(args->dat_fp,"FIT\t%s\t%e\t%d\t%d\t%s\n", dist->chr,cn3ra_fit,dist->irr,dist->iaa,cn3ra_func); if ( cn3aa_func ) fprintf(args->dat_fp,"FIT\t%s\t%e\t%d\t%d\t%s\n", dist->chr,cn3aa_fit,dist->iaa,dist->nvals-1,cn3aa_func); } if ( args->force_cn==4 || cn4_fail == '*' ) { fprintf(args->dat_fp,"FIT\t%s\t%e\t%d\t%d\t%s\n", dist->chr,cn4ra_fit,dist->irr,dist->iaa,cn4ra_func); if ( cn4aa_func ) fprintf(args->dat_fp,"FIT\t%s\t%e\t%d\t%d\t%s\n", dist->chr,cn4aa_fit,dist->iaa,dist->nvals-1,cn4aa_func); } fprintf(args->dat_fp,"CN\t%s\t%.2f\t%f\n", dist->chr, cn, fit); free(cn2aa_func); free(cn2ra_func); free(cn3ra_func); free(cn4ra_func); free(cn4aa_func); } peakfit_destroy(pkf); } static void usage(args_t *args) { fprintf(stderr, "\n"); fprintf(stderr, "About: Detect number of chromosomal copies from Illumina's B-allele frequency (BAF)\n"); fprintf(stderr, "Usage: bcftools polysomy [OPTIONS] \n"); fprintf(stderr, "\n"); fprintf(stderr, "General options:\n"); fprintf(stderr, " -o, --output-dir \n"); fprintf(stderr, " -r, --regions restrict to comma-separated list of regions\n"); fprintf(stderr, " -R, --regions-file restrict to regions listed in a file\n"); fprintf(stderr, " -s, --sample sample to analyze\n"); fprintf(stderr, " -t, --targets similar to -r but streams rather than index-jumps\n"); fprintf(stderr, " -T, --targets-file similar to -R but streams rather than index-jumps\n"); fprintf(stderr, " -v, --verbose \n"); fprintf(stderr, "\n"); fprintf(stderr, "Algorithm options:\n"); fprintf(stderr, " -b, --peak-size minimum peak size (0-1, larger is stricter) [0.1]\n"); fprintf(stderr, " -c, --cn-penalty penalty for increasing CN (0-1, larger is stricter) [0.7]\n"); fprintf(stderr, " -f, --fit-th goodness of fit threshold (>0, smaller is stricter) [3.3]\n"); fprintf(stderr, " -i, --include-aa include the AA peak in CN2 and CN3 evaluation\n"); fprintf(stderr, " -m, --min-fraction minimum distinguishable fraction of aberrant cells [0.1]\n"); fprintf(stderr, " -p, --peak-symmetry peak symmetry threshold (0-1, larger is stricter) [0.5]\n"); fprintf(stderr, "\n"); exit(1); } int main_polysomy(int argc, char *argv[]) { args_t *args = (args_t*) calloc(1,sizeof(args_t)); args->argc = argc; args->argv = argv; args->nbins = 150; args->fit_th = 3.3; args->cn_penalty = 0.7; args->peak_symmetry = 0.5; args->min_peak_size = 0.1; args->ra_rr_scaling = 1; args->min_fraction = 0.1; args->smooth = -3; static struct option loptions[] = { {"ra-rr-scaling",0,0,1}, // hidden option {"force-cn",1,0,2}, // hidden option {"smooth",1,0,'S'}, // hidden option {"nbins",1,0,'n'}, // hidden option {"include-aa",0,0,'i'}, {"peak-size",1,0,'b'}, {"min-fraction",1,0,'m'}, {"verbose",0,0,'v'}, {"fit-th",1,0,'f'}, {"cn-penalty",1,0,'c'}, {"peak-symmetry",1,0,'p'}, {"output-dir",1,0,'o'}, {"sample",1,0,'s'}, {"targets",1,0,'t'}, {"targets-file",1,0,'T'}, {"regions",1,0,'r'}, {"regions-file",1,0,'R'}, {0,0,0,0} }; char *tmp; int c; while ((c = getopt_long(argc, argv, "h?o:vt:T:r:R:s:f:p:c:im:b:n:S:",loptions,NULL)) >= 0) { switch (c) { case 1 : args->ra_rr_scaling = 0; break; case 2 : args->force_cn = atoi(optarg); break; case 'n': args->nbins = atoi(optarg); break; case 'S': args->smooth = atoi(optarg); break; case 'i': args->include_aa = 1; break; case 'b': args->min_peak_size = strtod(optarg,&tmp); if ( *tmp ) error("Could not parse: -b %s\n", optarg); if ( args->min_peak_size<0 || args->min_peak_size>1 ) error("Range error: -b %s\n", optarg); break; case 'm': args->min_fraction = strtod(optarg,&tmp); if ( *tmp ) error("Could not parse: -n %s\n", optarg); if ( args->min_fraction<0 || args->min_fraction>1 ) error("Range error: -n %s\n", optarg); break; case 'f': args->fit_th = strtod(optarg,&tmp); if ( *tmp ) error("Could not parse: -f %s\n", optarg); break; case 'p': args->peak_symmetry = strtod(optarg,&tmp); if ( *tmp ) error("Could not parse: -p %s\n", optarg); break; case 'c': args->cn_penalty = strtod(optarg,&tmp); if ( *tmp ) error("Could not parse: -c %s\n", optarg); break; case 's': args->sample = optarg; break; case 't': args->targets_list = optarg; break; case 'T': args->targets_list = optarg; args->targets_is_file = 1; break; case 'r': args->regions_list = optarg; break; case 'R': args->regions_list = optarg; args->regions_is_file = 1; break; case 'o': args->output_dir = optarg; break; case 'v': args->verbose++; break; default: usage(args); break; } } if ( optind>=argc ) { if ( !isatty(fileno((FILE *)stdin)) ) args->fname = "-"; } else args->fname = argv[optind]; if ( !args->fname ) usage(args); if ( !args->output_dir ) error("Missing the -o option\n"); init_data(args); fit_curves(args); destroy_data(args); free(args); return 0; }