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simulate_counts.py
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simulate_counts.py
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from random import choice, sample, random
from numpy.random import beta, negative_binomial, binomial
import numpy as np
import sys
import argparse
def write_options(f, options):
f.write("# prefix = %s\n" % options.prefix)
f.write("# num_tests = %d\n" % options.num_tests)
f.write("# num_inds = %d\n" % options.num_inds)
f.write("# min_hets = %d\n" % options.min_hets)
f.write("# maf = %g\n" % options.maf)
f.write("# mean_counts = %g\n" % options.mean_counts)
f.write("# mean_counts_distr = %s\n" % options.mean_counts_distr)
f.write("# as_counts = %g\n" % options.as_counts)
f.write("# gene_disp = %g\n" % options.gene_disp)
f.write("# gene_disp_distr = %s\n" % options.gene_disp_distr)
f.write("# ind_disp = %s\n" % ",".join(["%g" % x for x in options.ind_disp]))
f.write("# as_disp = %g\n" % options.as_disp)
f.write("# effect_size = %g\n" % options.effect_size)
f.write("# additivity = %g\n" % options.additivity)
f.write("# het_error_rate = %g\n" % options.het_error_rate)
f.write("# read_error_rate = %g\n" % options.read_error_rate)
f.write("# true_positives = %g\n" % options.true_positives)
f.write("# sim_hom_as = %s\n" % options.sim_hom_as)
def write_header(f):
f.write("CHROM "
"TEST.SNP.POS "
"TEST.SNP.ID "
"TEST.SNP.REF.ALLELE "
"TEST.SNP.ALT.ALLELE "
"TEST.SNP.GENOTYPE "
"TEST.SNP.HAPLOTYPE "
"REGION.START "
"REGION.END "
"REGION.SNP.POS "
"REGION.SNP.HET.PROB "
"REGION.SNP.LINKAGE.PROB "
"REGION.SNP.REF.HAP.COUNT "
"REGION.SNP.ALT.HAP.COUNT "
"REGION.SNP.OTHER.HAP.COUNT "
"REGION.READ.COUNT "
"GENOMEWIDE.READ.COUNT\n")
def parse_options():
parser = argparse.ArgumentParser(description="simulate counts for "
"combined haplotype test")
parser.add_argument("--prefix", default=None, required=True,
help="prefix for output files")
dflt_num_tests = 10000
parser.add_argument("--num_tests", default=dflt_num_tests, type=int,
help="number of regions to simulate "
"(default=%d)" % dflt_num_tests)
dflt_num_inds = 10
parser.add_argument("--num_inds", default=dflt_num_inds, type=int,
help="number of individuals to simulate "
"(default=%d)" % dflt_num_inds)
dflt_min_hets = 2
parser.add_argument("--min_hets", default=dflt_min_hets, type=int,
help="minimum number of heterozygous individuals "
"per test SNP (default=%d)" % dflt_min_hets)
dflt_maf = 0.2
parser.add_argument("--maf", default=dflt_maf, type=float,
help="minor allele frequency of test SNP "
"(default=%.2f)" % dflt_maf)
dflt_mean_counts = 200.0
parser.add_argument("--mean_counts", default=dflt_mean_counts, type=float,
help="mean number of read counts per region "
"(default=%.2f)" % dflt_mean_counts)
dflt_mean_counts_distr = "POINT"
parser.add_argument("--mean_counts_distr", default=dflt_mean_counts_distr,
help="distribution for mean number of "
"read counts per region (default=%s). "
" If EXPONENTIAL is specified, the value of "
"mean_counts is used as the scale parameter "
"(mean) of the distribution" % dflt_mean_counts_distr,
choices=("POINT", "EXPONENTIAL"))
dflt_as_counts = 20.0
parser.add_argument("--as_counts", default=dflt_as_counts, type=float,
help="expected number of allele-specific read counts "
"per regions (default=%.2f)" % dflt_as_counts)
dflt_gene_disp = 0.01
parser.add_argument("--gene_disp", default=dflt_gene_disp, type=float,
help="per-gene overdispersion parameter for "
"beta-negative binomial (default=%.2f)" %
dflt_gene_disp)
dflt_gene_disp_distr = "POINT"
parser.add_argument("--gene_disp_distr", default=dflt_gene_disp_distr,
help="distribution for sampling per-gene "
"overdispersion from (default=%s). If EXPONENTIAL "
"is specified, the value of gene_disp is used as "
"the scale parameter (mean) of the distribution"
% dflt_gene_disp, choices=("POINT", "EXPONENTIAL"))
dflt_ind_disp = "100.0"
parser.add_argument("--ind_disp", default=dflt_ind_disp,
help="per individual overdispersion parameter(s) for "
"beta-negative binomial. Can either provide a single value "
"that is used for all individuals or a comma-delimited list "
"of values (one per individual) (default=%s)." % dflt_ind_disp)
dflt_as_disp = 0.2
parser.add_argument("--as_disp", default=dflt_as_disp, type=float,
help="per individual allele-specific overdispersion "
"parameter for beta-binomial (default=%.2f)" % dflt_as_disp)
dflt_effect_size = 0.2
parser.add_argument("--effect_size", default=dflt_effect_size, type=float,
help="effect size of true positives (default=%.2f)" %
dflt_effect_size)
dflt_additivity = 1.0
parser.add_argument("--additivity", default=dflt_additivity, type=float,
help="additivity of alleles (default=%.2f)" %
dflt_additivity)
dflt_het_error_rate = 0.01
parser.add_argument("--het_error_rate", default=dflt_het_error_rate,
type=float, help="rate of incorrect heterozygous genotype calls"
"(default=%.2f)" % dflt_het_error_rate)
dflt_read_error_rate = 0.01
parser.add_argument("--read_error_rate", default=dflt_read_error_rate,
type=float, help="rate of incorrect alleles in reads"
"(default=%.2f)" % dflt_read_error_rate)
dflt_true_positives = 0.05
parser.add_argument("--true_positives", default=dflt_true_positives,
type=float, help="fraction of test SNPs that are "
"true positives (default=%.2f)" % dflt_true_positives)
dflt_sim_hom_as = False
parser.add_argument("--sim_hom_as", action="store_true", dest="sim_hom_as",
help="simulate allele specific counts "
"at homozygous test SNPs (default=False)", default=False)
options = parser.parse_args()
# split apart individual dispersion value string
vals = [float(x) for x in options.ind_disp.split(",")]
if len(vals) == 1:
options.ind_disp = [vals[0]] * options.num_inds
elif len(vals) != options.num_inds:
raise ValueError("number of ind_disp values should be "
"equal to 1 or to num_ind (%d)\n" % options.num_inds)
else:
options.ind_disp = vals
return options
def main():
options = parse_options()
out_files = []
sys.stderr.write("creating output files:\n")
file_list = open("%s_file_list.txt" % options.prefix, "w")
for i in range(options.num_inds):
out_filename = "%s_%d.txt" % (options.prefix, i+1)
sys.stderr.write(" %s\n" % out_filename)
out_files.append(open(out_filename, "w"))
# write_options(out_files[i], options)
write_header(out_files[i])
file_list.write(out_filename + "\n")
file_list.close()
ASseq_Y_file = open("%s_Y.txt" % options.prefix, "w")
ASseq_Y1_file = open("%s_Y1.txt" % options.prefix, "w")
ASseq_Y2_file = open("%s_Y2.txt" % options.prefix, "w")
ASseq_Z_file = open("%s_Z.txt" % options.prefix, "w")
test = 1
while test <= options.num_tests:
if random() > options.true_positives:
# simulate a site with no effect, this is not a positive
effect = 0
alt_expr = 1
AS_frac = 0.5
elif random() < 0.5:
# simulate a site with effect and beta > alpha
effect = 0
alt_expr = 1.0 + options.effect_size
AS_frac = 1.0 / (2.0 + options.additivity * options.effect_size)
else:
# simulate a site with effect and alpha > beta
effect = 1
alt_expr = 1 / (1 + options.effect_size)
AS_frac = (1 + options.additivity * options.effect_size) / \
(2.0 + options.additivity * options.effect_size)
snps = []
counts = []
num_hets = 0
if options.mean_counts_distr == "POINT":
mean_counts = options.mean_counts
elif options.mean_counts_distr == "EXPONENTIAL":
mean_counts = np.random.exponential(options.mean_counts)
else:
raise ValueError("unknown distribution %s\n" %
options.mean_counts_distr)
if options.gene_disp_distr == "POINT":
gene_disp = options.gene_disp
elif options.gene_disp_distr == "EXPONENTIAL":
gene_disp = np.random.exponential(options.gene_disp)
sys.stderr.write("gene_disp: %.2f\n" % gene_disp)
else:
sys.stderr.write("unknown distribution: %s\n" % gene_disp)
for ind in range(options.num_inds):
# Simulate the individual's haps=[0,0]
# prob of each minor allele is MAF (minor allele freq)
is_het = False
n_minor = int(random() < options.maf) + int(random() < options.maf)
if n_minor == 0:
# no minor alleles
haps = [0,0]
elif n_minor == 1:
# heterozygous
haps = [0,1]
num_hets += 1
is_het = True
else:
# two minor alleles
haps = [1,1]
# Expected number of reads based on genotypes
ind_mean_counts = mean_counts * ((2 - n_minor) + (n_minor * alt_expr))
#sys.stderr.write("n_minor: %d alt_expr: %g mean_counts: %g " %
# (n_minor, alt_expr, ind_mean_counts))
sim_count = simulate_BNB(ind_mean_counts, gene_disp, options.ind_disp[ind])
if is_het:
if random() < options.het_error_rate:
# simulate a homozygous site that was miscalled
# as a heterozygote
if haps[0] == 0:
ref, alt = simulate_BB(options.as_counts,
options.read_error_rate, options.as_disp)
else:
ref, alt = simulate_BB(options.as_counts, 1-options.read_error_rate,
options.as_disp)
else:
ref, alt = simulate_BB(options.as_counts, AS_frac,
options.as_disp)
else:
if options.sim_hom_as:
# simulate allele-specific counts even when test SNP
# is homozygous
ref, alt = simulate_BB(options.as_counts, 0.5, AS_disp)
else:
ref, alt = 0, 0
snps.append(TestSNP(effect, test, haps, sim_count,
ref, alt, 1.0 - options.het_error_rate))
counts.append(sim_count)
mean_counts = np.mean(counts)
Y=[]
Y1=[]
Y2=[]
Z=[]
if num_hets >= options.min_hets:
for snp_indx in range(len(snps)):
snps[snp_indx].set_total_counts(mean_counts)
out_files[snp_indx].write(snps[snp_indx].print_snp())
out_files[snp_indx].flush()
Y.append(snps[snp_indx].count)
Y1.append(snps[snp_indx].ref_count)
Y2.append(snps[snp_indx].alt_count)
if(snps[snp_indx].haps[0]==0 and snps[snp_indx].haps[1]==0):
Z.append(0)
elif(snps[snp_indx].haps[0]==0 and snps[snp_indx].haps[1]==1):
Z.append(1)
elif(snps[snp_indx].haps[0]==1 and snps[snp_indx].haps[1]==0):
Z.append(1)
elif(snps[snp_indx].haps[0]==1 and snps[snp_indx].haps[1]==1):
Z.append(4)
ASseq_Y_file.write("\t".join(str(y) for y in Y)+"\n")
ASseq_Y1_file.write("\t".join(str(y1) for y1 in Y1)+"\n")
ASseq_Y2_file.write("\t".join(str(y2) for y2 in Y2)+"\n")
ASseq_Z_file.write("\t".join(str(z) for z in Z)+"\n")
test+=1
class TestSNP:
def __init__(self,effect,test_num,haps,count,as_ref,as_alt,hetp):
self.chrm = effect
self.pos = test_num
self.ref_allele = "A"
self.alt_allele = "T"
self.count = count
self.ref_count = as_ref
self.alt_count = as_alt
self.haps = haps
if haps[0] != haps[1]:
self.hetp=hetp
else:
self.hetp = 0
self.genotype = sum(haps)
self.tot_count = 0
def set_total_counts(self, tot_count):
self.tot_count = tot_count
def print_snp(self):
return("%i %i %i %s %s %i %i|%i %i %i %i %f %i %i %i %i %i %i\n" %
(self.chrm, self.pos, self.pos, self.ref_allele, self.alt_allele,
self.genotype, self.haps[0], self.haps[1], self.pos, self.pos+1,
self.pos, self.hetp, 1, self.ref_count, self.alt_count, 0,
self.count, self.tot_count))
def simulate_BNB(mean, sigma, n):
# sys.stderr.write("%g %g %g\n" % (mean, sigma, n))
mean_p = np.float64(n) / (n+mean)
sigma = (1 / sigma)**2
a = mean_p * (sigma)+1
b = (1 - mean_p)*sigma
p = beta(a, b)
#sys.stderr.write("%f %f\n"%(n,p))
counts = negative_binomial(n, p)
return counts
def simulate_BB(tot, mean_p, sigma):
a = mean_p * (1/sigma**2 - 1)
b = (1-mean_p) * (1/sigma**2 - 1)
p = beta(a,b)
counts = binomial(tot,p)
#sys.stderr.write("%f %f %i\n"%(mean_p,p,counts))
return counts, (tot-counts)
main()