def homo_sapiens_Gutenkunst(path, seed, chrmStr, sample_size=20):
chrom = homo_sapiens.genome.chromosomes[chrmStr]
model = homo_sapiens.GutenkunstThreePopOutOfAfrica()
# model.debug()
# Currently sampling 20 individuals from a single popn.
samples = [msprime.Sample(population=0, time=0)] * sample_size
ts = msprime.simulate(
samples=samples,
recombination_map=chrom.recombination_map(),
mutation_rate=chrom.default_mutation_rate,
random_seed=seed,
**model.asdict())
ts.dump(path)
def run_human_gutenkunst_three_pop_ooa(args):
if args.num_yri_samples + args.num_ceu_samples + args.num_chb_samples < 2:
exit(
"Must specify at least 2 samples from YRI, CEU or CHB populations")
chromosome = homo_sapiens.chromosome_factory(
args.chromosome,
genetic_map=args.genetic_map,
length_multiplier=args.length_multiplier)
logger.info(
f"Running GutenkunstThreePopOutOfAfrica with YRI={args.num_yri_samples}"
f" CEU={args.num_ceu_samples} CHB={args.num_chb_samples}")
samples = ([msprime.Sample(population=0, time=0)] * args.num_yri_samples +
[msprime.Sample(population=1, time=0)] * args.num_ceu_samples +
[msprime.Sample(population=2, time=0)] * args.num_chb_samples)
model = homo_sapiens.GutenkunstThreePopOutOfAfrica()
ts = msprime.simulate(samples=samples,
recombination_map=chromosome.recombination_map,
mutation_rate=chromosome.mutation_rate,
**model.asdict())
write_output(ts, args)
write_citations(chromosome, model, args)
def msprime_to_dadi_simulation_OutOfAfrica(path, seed, chrom, sample_size=20):
'''
Generate however many different SFS with msprime and convert+save them into SFS for dadi to use.
'''
#For testing
# print(path, seed, chrom, sample_size)
chrom = homo_sapiens.genome.chromosomes[chrom]
model = homo_sapiens.GutenkunstThreePopOutOfAfrica()
samples_pops_joint = [
msprime.Sample(population=0, time=0)
] * sample_size + [msprime.Sample(population=1, time=0)] * sample_size
ts_pops_joint = msprime.simulate(
samples=samples_pops_joint,
recombination_map=chrom.recombination_map(),
mutation_rate=chrom.default_mutation_rate,
random_seed=seed,
**model.asdict())
haps_pops_joint = np.array(ts_pops_joint.genotype_matrix())
#Break up the haplotypes into seperate populations based on sample_size
haps_pop0_joint = haps_pops_joint[:, :sample_size]
haps_pop1_joint = haps_pops_joint[:, sample_size:]
genotypes_pop0_joint = allel.HaplotypeArray(haps_pop0_joint).to_genotypes(
ploidy=2)
allele_counts_pop0_joint = genotypes_pop0_joint.count_alleles()
genotypes_pop1_joint = allel.HaplotypeArray(haps_pop1_joint).to_genotypes(
ploidy=2)
allele_counts_pop1_joint = genotypes_pop1_joint.count_alleles()
sfs_joint = allel.joint_sfs(allele_counts_pop0_joint[:, 1],
allele_counts_pop1_joint[:, 1])
sfs_joint = dadi.Spectrum(sfs_joint)
sfs_joint.to_file(path)
import stdpopsim import msprime from stdpopsim import homo_sapiens from stdpopsim import drosophila_melanogaster import numpy as np model = homo_sapiens.GutenkunstThreePopOutOfAfrica() #model = drosophila_melanogaster.LiStephanTwoPopulation() dd = msprime.DemographyDebugger(**model.asdict()) dd.print_history() #t = 600000 # divergence time for drosophila t = 140e3 / 25 #divergence time for humans p = dd.coalescence_rate_trajectory(steps=[t], num_samples=[0, 2, 0]) print(p[1]) N = -t / (2 * np.log(p[1])) print(N)
def test_debug_runs(self):
model = homo_sapiens.GutenkunstThreePopOutOfAfrica()
output = io.StringIO()
model.debug(output)
s = output.getvalue()
self.assertGreater(len(s), 0)
def test_simulation_runs(self):
model = homo_sapiens.GutenkunstThreePopOutOfAfrica()
ts = msprime.simulate(
samples=[msprime.Sample(pop, 0) for pop in range(3)],
**model.asdict())
self.assertEqual(ts.num_populations, 3)
def main():
parser = argparse.ArgumentParser()
num_cores = multiprocessing.cpu_count()
parser.add_argument(
"-c",
"--cpus",
help="number of CPUs. Must be integer value between 1 \
and " + str(num_cores),
nargs='?',
type=int,
choices=range(1, num_cores + 1),
metavar='INT',
default=num_cores)
parser.add_argument(
"-v", "--verbose", help="Enable verbose logging", action="store_true")
parser.add_argument(
"-m",
"--mutation_rate",
help="mutation rate in simulation",
nargs='?',
type=float,
metavar='FLOAT',
default=1.15e-8)
parser.add_argument(
"-r",
"--recombination_rate",
help="recombination rate in simulation",
nargs='?',
type=float,
metavar='FLOAT',
default=1e-8)
parser.add_argument(
"-f",
"--mnm_frac",
help="fraction of expected MNMs",
nargs='?',
type=float,
metavar='FLOAT',
default=0.015)
parser.add_argument(
"-d",
"--mnm_dist",
help="maximum distance between simulated MNMs",
nargs='?',
type=int,
metavar='INT',
default=100)
parser.add_argument(
"-n",
"--mnm_num",
help="number of mutations to include in each simulated MNM",
nargs='?',
type=int,
metavar='INT',
default=2)
parser.add_argument(
"-l",
"--length",
help="length of each simulated haplotype",
nargs='?',
type=int,
metavar='INT',
default=50000)
parser.add_argument(
"-N",
"--num_samples",
help="number of samples per replicate",
nargs='?',
type=int,
metavar='INT',
default=100)
parser.add_argument(
"-R",
"--replicates",
help="number of replicates",
nargs='?',
type=int,
metavar='INT',
default=1000)
parser.add_argument(
"-i",
"--replicate_ID",
help="unique identifier when running simulation for specific replicate",
nargs='?',
type=int,
metavar='INT',
default=0)
parser.add_argument(
"-s",
"--seed",
help="set seed",
nargs='?',
type=int,
metavar='INT',
default=30)
parser.add_argument(
"-D",
"--demographic_model",
help="demographic model to simulate under",
nargs='?',
type=str,
metavar='STR',
default="GutenkunstThreePop")
parser.add_argument(
"-M",
"--method",
help="archaic ancestry inference method",
nargs='?',
type=str,
metavar='method',
default="archie")
parser.add_argument(
"-F",
"--force",
help="force rewrite of output files, even if they already exist",
action="store_true")
args = parser.parse_args()
warnings.filterwarnings("ignore", category=RuntimeWarning)
if args.verbose:
loglev = 'DEBUG'
else:
loglev = 'INFO'
warnings.filterwarnings("ignore", category=UserWarning)
util.util_log.setLevel(loglev)
log = util.get_logger("archanc", level=loglev)
log.debug("Running with the following options:")
for arg in vars(args):
log.debug("%s : %s", arg, getattr(args, arg))
# coalescent simulation parameters
sample_size = args.num_samples
length = args.length
mu = args.mutation_rate
rr = args.recombination_rate
replicates = args.replicates
if replicates == 1:
seed = args.replicate_ID
else:
seed = args.seed
proj_dir = os.getcwd()
msprime_dir = proj_dir + "/output/msprime/"
out_dir = msprime_dir + args.demographic_model + "/"
archie_src_dir = proj_dir + "/src/ArchIE/"
archie_out_dir = proj_dir + "/output/ArchIE/"
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if args.demographic_model == "GutenkunstThreePop":
demo_model = homo_sapiens.GutenkunstThreePopOutOfAfrica()
pop_samples = [msprime.Sample(0, 0)] * sample_size + \
[msprime.Sample(1, 0)] * sample_size + \
[msprime.Sample(2, 0)] * sample_size
# model_dict = {"GutenkunstThreePop": demo_model_ts}
elif args.demographic_model == "TennessenTwoPop":
demo_model = homo_sapiens.TennessenTwoPopOutOfAfrica()
pop_samples = [msprime.Sample(0, 0)] * sample_size + \
[msprime.Sample(1, 0)] * sample_size
elif args.demographic_model == "TennessenTwoPopNoAncientMig":
demo_model = homo_sapiens.TennessenTwoPopOutOfAfricaNoAncientMig()
pop_samples = [msprime.Sample(0, 0)] * sample_size + \
[msprime.Sample(1, 0)] * sample_size
elif args.demographic_model == "TennessenTwoPopNoMig":
demo_model = homo_sapiens.TennessenTwoPopOutOfAfricaNoMig()
pop_samples = [msprime.Sample(0, 0)] * sample_size + \
[msprime.Sample(1, 0)] * sample_size
elif args.demographic_model == "RagsdaleArchaic":
demo_model = homo_sapiens.RagsdaleArchaic()
pop_samples = [msprime.Sample(0, 0)] * sample_size + \
[msprime.Sample(1, 0)] * sample_size
demo_model_ts = msprime.simulate(
# first 100 samples from AFR, next 100 from EUR
samples=pop_samples,
length=length,
mutation_rate=mu,
recombination_rate=rr,
random_seed=seed,
num_replicates=replicates,
**demo_model.asdict())
model_dict = {"model": demo_model_ts}
#-------------------------------------------------------
# define other models here and add to model_dict below
#-------------------------------------------------------
# e.g., modify demographic parameters to include archaic branches
# GutenkunstThreePopArchaic_model = homo_sapiens.GutenkunstThreePopArchaic()
# GutenkunstThreePopArchaic_ts = msprime.simulate(
# # first 100 samples from AFR, next 100 from EUR
# samples=[msprime.Sample(0, 0)]*sample_size + [msprime.Sample(1, 0)]*sample_size,
# length=length,
# mutation_rate=mu,
# recombination_rate=rr,
# random_seed=seed,
# num_replicates=replicates,
# **GutenkunstThreePopArchaic_model.asdict())
# process simulated data and write files for use with different methods
for model_label, model in model_dict.items():
ts_list = {}
for j, ts in enumerate(model):
if args.replicate_ID == 0:
prefix_nomnm = out_dir + args.demographic_model + "_rep" + str(
j + 1)
else:
prefix_nomnm = out_dir + args.demographic_model + "_rep" + str(
args.replicate_ID)
prefix_mnm = prefix_nomnm + "_mnm" + str(
args.mnm_dist) + "-" + str(args.mnm_frac)
if args.method == "sprime":
suffix = ".vcf"
elif args.method == "archie":
suffix = ".snp"
if args.force or not os.path.isfile(
prefix_nomnm + suffix) or not os.path.isfile(prefix_mnm +
suffix):
log.info(
"Output files %s*%s are missing or the --force flag is enabled"
% (prefix_nomnm, suffix))
ts_list[j] = list(ts.variants())
else:
log.debug(
"Output files %s*%s already exist and will not be overwritten"
% (prefix_nomnm, suffix))
# parallelize if running multiple replicates
if args.cpus > 1 and replicates > 1:
Parallel(n_jobs=args.cpus) \
(delayed(util.process_ts)(ts, args.demographic_model, j+1, args.mnm_frac, args.mnm_dist, args.mnm_num, args.method, out_dir) \
for j, ts in ts_list.items())
# run as single instance if returning only a single replicate
# (for use with the cluster)
elif replicates == 1:
for j, ts in ts_list.items():
util.process_ts(ts, args.demographic_model, args.replicate_ID,
args.mnm_frac, args.mnm_dist, args.mnm_num,
args.method, out_dir)