geinf/vcf_utils.py

#!/usr/bin/env python3

"""
FOREST Thomas (thomas.forest@college-de-france.fr)

Caution : At the moment for gzipped files only.

ARGS
--------

standalone usage : vcf_to_sfs.py VCF.gz nb_indiv

"""
import gzip
import sys
import numpy as np
from frst import customgraphics
import json 
import time
import datetime
import gc
import pandas as pd


def parse_vcf(vcf_file, phased=False, stop_at=None, chr_starts_with="*"):
    start = time.time()
    with gzip.open(vcf_file, "rb") as inputgz:
            byte_line = inputgz.readline()
            genotypes = []
            noGenotype = []
            pos = 0
            pluriall_counts = 0
            entries = {}
            chrom = {}
            nb_site = 0
            print("Parsing VCF {} ... Please wait...".format(vcf_file))
            #print("Parsing VCF", vcf_file, "... Please wait...")
            while byte_line:
                #print(line)
                # decode gzipped binary lines
                line = byte_line.decode('utf-8').strip()
                nb_site += 1
                #  # every snp line, not comment or header
                if not line.startswith("##") and not line.startswith("#"):
                    FIELDS = line.split("\t")
                    CHROM = FIELDS[0]
                    POS = int(FIELDS[1])
                    if stop_at:
                        if POS > stop_at:
                            break
                    # REF is col 4 of VCF
                    REF = FIELDS[3].split(",")
                    # ALT is col 5 of VCF
                    ALT = FIELDS[4].split(",")
                    FORMAT = line.split("\t")[8:9]
                    SAMPLES = line.split("\t")[9:]
                    QUALITY = float(FIELDS[5])
                    INFO = FIELDS[7]
                    INFOS = {}
                    for info in INFO.split(";"):
                        try :
                            INFOS[info.split('=')[0]] = info.split('=')[1]
                        except:
                            INFOS[info] = info
                    GENOTYPE = []
                    LIKELIHOOD = []
                    # SKIP the SNP if :
                    # 1 : missing
                    # 2 : deletion among REF
                    # 3 : deletion among ALT
                    if "./.:." in line \
                       or len(ALT[0]) > 1 \
                       or len(REF[0]) > 1:
                        # sites that are not kept
                        # if at least one missing data, remember it
                        noGenotype.append(POS)
                        byte_line = inputgz.readline()
                        continue
                    elif len(ALT) > 1:
                        # pluriall sites
                        # remember that the gestion of PL is very diff with pluriall
                        pluriall_counts += 1
                        noGenotype.append(POS)
                        byte_line = inputgz.readline()
                        continue
                    else:
                        # for unphased and with only two fields : GT:PL
                        for sample in SAMPLES:
                            if not phased:
                                # for UNPHASED data
                                sample_genotype = [int(a) for a in sample.split(':')[0].split('/') if a != '.']
                            else:
                                # for PHASED
                                sample_genotype = [int(a) for a in sample.split(':')[0].split('|') if a != '.']
                            # list of PL : [prob of 0/0, prob of 0/1, prob of 1/1]
                            sample_likelihood =  sample.split(':')[1].split(',')
                            sample_likelihood = [pow(10, -int(sample_likelihood[0])/10),
                                                 pow(10, -int(sample_likelihood[1])/10), pow(10, -int(sample_likelihood[2])/10)]
                            GENOTYPE += sample_genotype
                            LIKELIHOOD.append(sample_likelihood)
                        # from log phred score to probability of error, E
                        #LIKELIHOOD = pow(10, -int(LIKELIHOOD[0])/10)
                        #print(LIKELIHOOD)
                        entries = {
                            'POS':POS,
                            'CHR':CHROM,
                            'FIELDS':FIELDS,
                            'REF':REF,
                            'ALT':ALT,
                            'FORMAT':FORMAT,
                            'INFOS':INFOS,
                            'SAMPLES':SAMPLES,
                            'QUALITY':QUALITY,
                            'GENOTYPE':GENOTYPE,
                            'LIKELIHOOD':LIKELIHOOD
                        }
                        if CHROM.startswith(chr_starts_with):
                        # keep if chr name starts with filter
                        # default : *, every chr is kept
                            if CHROM not in chrom:
                                 chrom[CHROM] = {}
                            chrom[CHROM][POS] = entries
                byte_line = inputgz.readline()
    end = time.time()
    print("Parsed", nb_site, "sites in", str(datetime.timedelta(seconds=end - start)))

    return chrom

def build_polymorph_coverage_matrix(entries, noGenotype, diploid=True, na_omit = False, normalize = False, xlim=None):
    """ Take infos out of parsing to build a coverage matrix of probability 
    of error, E, at each position
    """
    # last pos without any missing data ./.:.
    last_pos = list(entries.keys())[-1]
    # last genotyped SNP with missing data, usually bigger than last_pos
    last_genotyped = noGenotype[-1]
    mat = []
    if diploid:
        # k=N if diploids, k = 2N if haploids 
        k = int(len(entries[last_pos]['GENOTYPE'])/2)
    else:
        k = len(entries[last_pos]['GENOTYPE'])
    for _ in range(k):
        mat.append([])
    #display_max = last_pos
    if xlim:
        display_max = xlim
    else:
        if na_omit:
            display_max = last_pos
        else:
            display_max = last_genotyped
    for pos in range(display_max):
        if pos in noGenotype or pos not in entries.keys():
            if na_omit:
                continue
            else:
                for k in range(len(mat)):
                    # if missing, prob=1, worst case
                    mat[k].append(1)
        else:
            for k in range(len(mat)):
                best_prob = min(entries[pos]['LIKELIHOOD'][k])
                #print(ind, best_prob)
                mat[k].append(best_prob)
                    
    mat = np.array(mat)
    if normalize:
        row_sums = mat.sum(axis=1)
        mat = mat / row_sums[:, np.newaxis]
    return mat

def genotyping_continuity_plot(vcf_entries,
                                                            verbose=False,
                                                            step = 1):
    last_pos = int(sorted(list(vcf_entries.keys()))[-1])
    x = 0
    y = 1
    coords = [[], []]
    print(last_pos, "sites to scan")
    for k, pos in enumerate(range(0, last_pos, step)):
        if verbose:
            progress = round(k/int(last_pos))*100
            if progress % 10 == 0:
                print(progress, "%")
        # if pos is genotyped
        if k in vcf_entries:
            y+=1*step
        x+=1*step
        coords[0].append(x)
        coords[1].append(y)
    return coords

def compute_coverage(vcf_entries, verbose=False):
    last_pos = list(vcf_entries.keys())[-1]
    x = 0
    y = 1
    coords = [[], []]
    print(last_pos, "sites to scan")
    for entry in vcf_entries.values():
        y = int(entry['INFOS']['DP'])
        x = entry['POS']
        coords[0].append(x)
        coords[1].append(y)
    return coords

def free(obj):
    """ Free the object and call the garbage collector explicitely
    """
    del obj
    gc.collect()

if __name__ == "__main__":
    # check args
    if len(sys.argv) !=2:
        print("Need 1 arg")
        exit(0)
    # main
    vcf_file = sys.argv[1]

    # # without missing data
    # entries, noGenotype = parse_vcf(vcf_file, stop_at = 20000)
    # mat = build_polymorph_coverage_matrix(entries, noGenotype, diploid=True, na_omit=True, normalize=False, xlim = None)
    # customgraphics.plot_matrix(mat, color_scale_type="autumn", cbarlabel = "prob. of error, E", title="Prob. of error (E) of genotyping, at each position, lower the better")

    # # with missing data
    # entries, noGenotype = parse_vcf(vcf_file, stop_at = 500)
    # mat = build_polymorph_coverage_matrix(entries, noGenotype, diploid=True, na_omit=False, normalize=False, xlim = None)
    # customgraphics.plot_matrix(mat, color_scale_type="autumn", cbarlabel = "prob. of error, E", title="Prob. of error (E) of genotyping, at each position, lower the better")