def main(args):
np.random.seed(args.beta_num)
sim = SumstatSimulation(args.sim_name)
arch = Architecture(sim.architecture)
d = Dataset(sim.dataset)
# sample the beta
beta = arch.draw_effect_sizes(sim.dataset, sim.h2g)[:, 0]
# compute noiseless phenotypes slice by slice
Y = np.zeros(d.N)
t0 = time()
for s in d.slices():
# X will be N x M
print(int(time() - t0), ": getting genotypes from file. SNPs", s)
X = d.get_standardized_genotypes(s)
print("computing phenotypes. SNPs", s)
Y += X.dot(beta[s[0] : s[1]])
del X
# normalize the Y and the beta to the desired heritability
normalization = np.std(Y) / np.sqrt(sim.h2g)
if normalization == 0:
normalization = 1 # just in case we have some 0s...
Y /= normalization
beta /= normalization
# write the betas and the noiseless phenotypes
pickle.dump(beta, sim.beta_file(args.beta_num, "wb"), 2)
pickle.dump(Y, sim.noiseless_Y_file(args.beta_num, "wb"), 2)
def preprocess(self):
if self.params.baseline and not self.baseline_preprocessing_in_progress(
):
print('baseline model not found. creating...')
self.declare_baseline_preprocessing_in_progress()
self.create_baseline_model()
print('submitting ld score jobs for annotation of interest')
gs = GenomicSubset(self.params.region)
# create the annotation file
for chrnum in self.refpanel.chromosomes():
d = Dataset(self.params.refpanel, chrnum=chrnum)
ss = SnpSubset(d, gs.restricted_to_chrom_bedtool(chrnum))
SnpSubset.print_subsets(self.annotation_filename(chrnum), [ss],
[self.params.region],
add_other=True)
# create the ldscores file
for chrnum in self.refpanel.chromosomes():
d = Dataset(self.params.refpanel, chrnum=chrnum)
ldscores_command = [
'python', '-u', paths.foreign + 'ldsc/ldsc.py', '--l2',
'--ld-wind-cm',
str(self.params.ld_window / 1000.), '--bfile',
d.genotypes_bedfile.filename, '--annot',
self.annotation_filename(chrnum), '--out',
self.annotation_l2_filestem(chrnum)
]
print(' '.join(ldscores_command))
outfilepath = self.annotation_l2_filestem(chrnum) + '.bsub_out'
bsub.submit(ldscores_command,
outfilepath,
jobname=self.preprocessing_foldername() + ',chr=' +
str(chrnum))
def main(args):
np.random.seed(args.beta_num)
sim = SumstatSimulation(args.sim_name)
arch = Architecture(sim.architecture)
d = Dataset(sim.dataset)
# sample the beta
beta = arch.draw_effect_sizes(sim.dataset, sim.h2g)[:, 0]
# compute noiseless phenotypes slice by slice
Y = np.zeros(d.N)
t0 = time()
for s in d.slices():
# X will be N x M
print(int(time() - t0), ': getting genotypes from file. SNPs', s)
X = d.get_standardized_genotypes(s)
print('computing phenotypes. SNPs', s)
Y += X.dot(beta[s[0]:s[1]])
del X
# normalize the Y and the beta to the desired heritability
normalization = np.std(Y) / np.sqrt(sim.h2g)
if normalization == 0: normalization = 1 # just in case we have some 0s...
Y /= normalization
beta /= normalization
# write the betas and the noiseless phenotypes
pickle.dump(beta, sim.beta_file(args.beta_num, 'wb'), 2)
pickle.dump(Y, sim.noiseless_Y_file(args.beta_num, 'wb'), 2)
def create_baseline_model(self):
gss = [GenomicSubset(region) for region in LDSC.baseline_model_regions]
# create the annotation file
for chrnum in self.refpanel.chromosomes():
print('creating baseline annot file for chr', chrnum)
d = Dataset(self.params.refpanel, chrnum=chrnum)
sss = [
SnpSubset(d, gs.restricted_to_chrom_bedtool(chrnum))
for gs in gss
]
SnpSubset.print_subsets(self.baseline_filename(chrnum), sss,
LDSC.baseline_model_regions)
# create the ldscores file
for chrnum in self.refpanel.chromosomes():
d = Dataset(self.params.refpanel, chrnum=chrnum)
ldscores_command = [
'python', '-u', paths.foreign + 'ldsc/ldsc.py', '--l2',
'--ld-wind-cm',
str(self.params.ld_window / 1000.), '--bfile',
d.genotypes_bedfile.filename, '--annot',
self.baseline_filename(chrnum), '--out',
self.baseline_l2_filestem(chrnum)
]
print(' '.join(ldscores_command))
outfilepath = self.baseline_l2_filestem(chrnum) + '.bsub_out'
bsub.submit(ldscores_command,
outfilepath,
jobname='baseline,chr=' + str(chrnum))
def main(args):
np.random.seed(args.beta_num + args.sample_num * 10000)
sim = SumstatSimulation(args.sim_name)
d = Dataset(sim.dataset)
pretty.print_namespace(sim)
print()
# read in noiseless phenotypes
Y = pickle.load(sim.noiseless_Y_file(args.beta_num))
# choose individuals and create ensemble of Ys
indices = np.random.choice(Y.shape[0], size=(sim.sample_size, ))
Y = Y[indices]
# compute how much noise to add
sigma2e = 1 - sim.h2g
print('adding noise. sigma2e =', sigma2e)
Y += np.sqrt(sigma2e) * np.random.randn(*Y.shape)
if sim.condition_on_covariates:
print('projecting covariates out of Y')
Y = d.project_out_covariates(Y, covariates=d.covariates[indices])
alphahat = np.zeros(d.M)
t0 = time()
def compute_sumstats_for_slice(s):
# X will be N x M
print(int(time() - t0), ': getting genotypes from file. SNPs', s)
X = d.get_standardized_genotypes(s)[indices]
if sim.condition_on_covariates:
print(int(time() - t0), ': projecting out covariates')
X = d.project_out_covariates(X, covariates=d.covariates[indices])
print(int(time() - t0), ': computing sumstats. SNPs', s)
alphahat[s[0]:s[1]] = X.T.dot(Y) / sim.sample_size
del X
map(compute_sumstats_for_slice, d.slices())
# write output
def write_output():
pickle.dump(indices,
sim.individuals_file(args.beta_num, args.sample_num, 'wb'),
2)
pickle.dump(Y, sim.noisy_Y_file(args.beta_num, args.sample_num, 'wb'),
2)
pickle.dump(alphahat,
sim.sumstats_file(args.beta_num, args.sample_num, 'wb'), 2)
write_output()
def main(args):
refpanel = Dataset(args.refpanel + '.' + str(args.chrnum))
annot_filename = '{}.{}.annot.gz'.format(args.annot_stem, args.chrnum)
result_filename = '{}.maf1p.{}.annot.gz'.format(args.annot_stem,
args.chrnum)
print('reading annot')
annot = pd.read_csv(annot_filename, compression='gzip', sep='\t', header=0)
name = annot.columns[-1]
names = annot.columns.values
print('reading frq')
refpanel_frq = pd.read_csv(refpanel.genotypes_bedfile.filename + '.frq',
delim_whitespace=True,
header=0)
refpanel_frq = refpanel_frq[['SNP', 'MAF']]
print('merging')
annot = annot.merge(refpanel_frq, how='left', on=['SNP'])
print('before filtering, |A| =', np.sum(annot[name]))
annot.ix[annot['MAF'] > 0.01, name] = 0
print('after filtering, |A| =', np.sum(annot[name]))
# write output
print('writing output to', result_filename)
with gzip.open(result_filename, 'wt') as f:
annot[names].to_csv(f, index=False, sep='\t')
def main(args):
np.random.seed(args.beta_num + args.sample_num * 10000)
sim = SumstatSimulation(args.sim_name)
d = Dataset(sim.dataset)
pretty.print_namespace(sim); print()
# read in noiseless phenotypes
Y = pickle.load(sim.noiseless_Y_file(args.beta_num))
# choose individuals and create ensemble of Ys
indices = np.random.choice(Y.shape[0], size=(sim.sample_size,))
Y = Y[indices]
# compute how much noise to add
sigma2e = 1 - sim.h2g
print('adding noise. sigma2e =', sigma2e)
Y += np.sqrt(sigma2e) * np.random.randn(*Y.shape)
if sim.condition_on_covariates:
print('projecting covariates out of Y')
Y = d.project_out_covariates(Y, covariates=d.covariates[indices])
alphahat = np.zeros(d.M)
t0 = time()
def compute_sumstats_for_slice(s):
# X will be N x M
print(int(time() - t0), ': getting genotypes from file. SNPs', s)
X = d.get_standardized_genotypes(s)[indices]
if sim.condition_on_covariates:
print(int(time() - t0), ': projecting out covariates')
X = d.project_out_covariates(X, covariates=d.covariates[indices])
print(int(time() - t0), ': computing sumstats. SNPs', s)
alphahat[s[0]:s[1]] = X.T.dot(Y) / sim.sample_size
del X
map(compute_sumstats_for_slice, d.slices())
# write output
def write_output():
pickle.dump(indices, sim.individuals_file(
args.beta_num, args.sample_num, 'wb'), 2)
pickle.dump(Y, sim.noisy_Y_file(
args.beta_num, args.sample_num, 'wb'), 2)
pickle.dump(alphahat, sim.sumstats_file(
args.beta_num, args.sample_num, 'wb'), 2)
write_output()
def main(args):
result_filename = '{}.{}.annot.gz'.format(args.annot_stem, args.chrnum)
name = args.annot_stem.split('/')[-1]
print('reading refpanel bim')
refpanel = Dataset(args.refpanel + '.' + str(args.chrnum))
refpanel_bim = pd.read_csv(refpanel.genotypes_bedfile.filename + '.bim',
sep='\t',
names=['CHR', 'SNP', 'cM', 'BP', 'A1', 'A2'])
print('\tthere are', len(refpanel_bim), 'SNPs')
print('reading frq')
refpanel_frq = pd.read_csv(refpanel.genotypes_bedfile.filename + '.frq',
delim_whitespace=True,
header=0)
refpanel_frq = refpanel_frq[['SNP', 'MAF']]
print('merging')
annot = refpanel_bim.merge(refpanel_frq, how='left', on=['SNP'])
annot[name] = 0
print('before filtering, |A| =', np.sum(annot[name]))
to_include = np.flatnonzero((annot['MAF'] <= 0.01).values)
# sizes = [
# 27285,
# 26794,
# 23253,
# 18865,
# 20449,
# 17280,
# 15776,
# 13198,
# 14079,
# 15070,
# 14247,
# 12971,
# 9039,
# 11219,
# 8688,
# 11506,
# 10187,
# 7603,
# 7804,
# 6765,
# 3747,
# 4021
# ]
# to_include = to_include[np.random.choice(len(to_include), replace=False,
# size=sizes[args.chrnum-1])]
annot.ix[to_include, name] = 1
print('after filtering, |A| =', np.sum(annot[name]))
annot.rename(columns={'cM': 'CM'}, inplace=True)
names = ['CHR', 'BP', 'SNP', 'CM', name]
# write output
print('writing output')
with gzip.open(result_filename, 'wt') as f:
annot[names].to_csv(f, index=False, sep='\t')
def submit(args):
my_args = ['main', '--chrom', '$LSB_JOBINDEX']
d = Dataset('UK10Khg19.22')
outfilepath = d.auxfiles_path + '../' + \
'%I/.preprocess.out'
bsub.submit(['python', '-u', paths.code + 'real/preprocess.py'] + my_args,
outfilepath,
jobname='preprocess[1-22]',
memory_GB=16)
def get_refpanel(args):
print('reading refpanel and refpanel bim')
refpanel = Dataset(args.refpanel + '.' + str(args.chrnum))
refpanel_bim = pd.read_csv(refpanel.genotypes_bedfile.filename + '.bim',
sep='\t',
names=['CHR', 'SNP', 'cM', 'BP', 'A1', 'A2'])
refpanel_bim['refpanelINDEX'] = np.arange(len(refpanel_bim))
print('\trefpanel contains', len(refpanel_bim), 'SNPs')
return refpanel, refpanel_bim
def main(args):
d = Dataset(args.refpanel + '.' + str(args.chrnum))
annot_filename = '{}.{}.annot.gz'.format(args.annot_stem, args.chrnum)
cannot_filename = '{}.{}.cannot.gz'.format(args.annot_stem, args.chrnum)
cannot_norm_filename = '{}.{}.cannot.norm'.format(args.annot_stem, args.chrnum)
annot = pd.read_csv(annot_filename, compression='gzip', sep='\t', header=0)
name = annot.columns[-1]
v = annot.ix[:,name].values
#TODO: use ld blocks, possibly just those that have non-trivial intersection with the
# nonzero entries of v
print('computing Xv')
Xv = np.zeros(d.N)
for s in d.slices():
print(s)
X = d.get_standardized_genotypes(s)
Xv += X.dot(v[s[0]:s[1]])
print('computing XTXv')
XTXv = np.zeros(d.M)
for s in d.slices():
print(s)
X = d.get_standardized_genotypes(s)
XTXv[s[0]:s[1]] = X.T.dot(Xv)
print('computing V^TRv')
Rv = XTXv / d.N
vTRv = v.dot(Rv)
# write output
print('writing output')
annot[name+'.CONV'] = Rv
with gzip.open(cannot_filename, 'wt') as f:
annot.to_csv(f, index=False, sep='\t')
with open(cannot_norm_filename, 'w') as f:
f.write(str(vTRv))
def create_annot(args):
path = '/'.join(args.bedfile.split('/')[:-1]) + '/'
filename = args.bedfile.split('/')[-1]
if filename[-4:] == '.bed':
name = filename[:-4]
else:
name = filename
gs = GenomicSubset(name, path=path)
for chrnum in range(1, 23)[::-1]:
print('creating annot file for chr', chrnum)
d = Dataset(args.refpanel + '.' + str(chrnum))
sss = [SnpSubset(d, gs.restricted_to_chrom_bedtool(chrnum))]
SnpSubset.print_subsets('{}{}.{}.annot.gz'.format(path, name, chrnum),
sss, [name])
def run(self, beta_num, sim):
print('loading data set and region info')
d = Dataset(sim.dataset)
gs = GenomicSubset(self.params.region)
ss = SnpSubset(d, bedtool=gs.bedtool)
print('loading ld score info')
ref_ldscores, w_ld, M_annot = self.ld_score_info()
N = np.ones((d.M, 1)) * d.N
print(('ref_ldscores shape:{}\nw_ld shape:{}\nN shape:{}\n' + \
'M_annot shape:{}').format(
ref_ldscores.shape,
w_ld.shape,
N.shape,
M_annot.shape))
overlaps = self.overlap_vector()
print('num snps overlapping with each category:', overlaps)
results = []
variances = []
for alphahat in sim.sumstats_files(beta_num):
alphahat = d.N * alphahat**2
if self.params.constrain_intercept:
hsqhat = ldsc.ldscore.regressions.Hsq(alphahat.reshape(
(d.M, 1)),
ref_ldscores,
w_ld,
N,
M_annot,
intercept=1)
else:
hsqhat = ldsc.ldscore.regressions.Hsq(
alphahat.reshape((d.M, 1)), ref_ldscores, w_ld, N, M_annot)
results.append(hsqhat.coef.dot(overlaps))
variances.append(overlaps.dot(hsqhat.coef_cov).dot(overlaps))
print('intercept:', hsqhat.intercept)
print(len(results), results[-1], variances[-1])
return np.concatenate([np.array([results]).T,
np.array([variances]).T],
axis=1)
def main(args):
d = Dataset(args.refpanel + '.' + str(args.chrnum))
annot_filename = '{}.{}.annot.gz'.format(args.annot_stem, args.chrnum)
cannot_filename = '{}.{}.cannot.gz'.format(args.annot_stem, args.chrnum)
cannot_norm_filename = '{}.{}.cannot.norm'.format(args.annot_stem,
args.chrnum)
annot = pd.read_csv(annot_filename, compression='gzip', sep='\t', header=0)
name = annot.columns[-1]
v = annot.ix[:, name].values
#TODO: use ld blocks, possibly just those that have non-trivial intersection with the
# nonzero entries of v
print('computing Xv')
Xv = np.zeros(d.N)
for s in d.slices():
print(s)
X = d.get_standardized_genotypes(s)
Xv += X.dot(v[s[0]:s[1]])
print('computing XTXv')
XTXv = np.zeros(d.M)
for s in d.slices():
print(s)
X = d.get_standardized_genotypes(s)
XTXv[s[0]:s[1]] = X.T.dot(Xv)
print('computing V^TRv')
Rv = XTXv / d.N
vTRv = v.dot(Rv)
# write output
print('writing output')
annot[name + '.CONV'] = Rv
with gzip.open(cannot_filename, 'wt') as f:
annot.to_csv(f, index=False, sep='\t')
with open(cannot_norm_filename, 'w') as f:
f.write(str(vTRv))
self.add_ridge(Lambda)
self.covcsr /= (1 + Lambda)
if __name__ == '__main__':
from primitives import Dataset, GenomicSubset, SnpSubset
import copy
from time import time
import argparse
np.random.seed(0)
parser = argparse.ArgumentParser()
parser.add_argument('--M', type=int, required=True, help='the number of SNPs to use')
parser.add_argument('-check_dense', action='store_true', default=False)
args = parser.parse_args()
d = Dataset('GERA', forced_M=args.M)
indivs = d.random_indivs(200)
t0 = time()
R = LdMatrix(d, indivs, 200)
R.add_ridge(0.05)
print('computing R took', time() - t0)
print('shape of R is:', R.covcsr.shape)
# tiny = GenomicSubset('tiny')
# tiny_irs = SnpSubset(d, bedtool=tiny.bedtool).irs
tiny_irs = IntRangeSet('300:350')
RA = LdMatrix(d, indivs, 200, snpset_irs=tiny_irs, output=False)
b = np.random.randn(d.M)
# check inverse computation
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--refpanel', type=str, required=True)
parser.add_argument('--ldblocks',
type=str,
required=False,
default='pickrell_ldblocks.hg19.eur.bed')
parser.add_argument('--region', type=str, required=True)
parser.add_argument('--sumstats_path', type=str, required=True)
args = parser.parse_args()
print('loading reference panel')
refpanel = Dataset(args.refpanel)
print('loading region')
A = GenomicSubset(args.region)
print('loading ld blocks')
blocks = BedTool(paths.reference + args.ldblocks)
print('finding ld blocks that overlap with A')
relevant_blocks = blocks.intersect(A.bedtool, wa=True).saveas()
print('found', len(relevant_blocks), 'blocks that overlap with A')
print('reading refpanel bim')
refpanel_bim = pd.read_csv(refpanel.genotypes_bedfile.filename + '.bim',
sep='\t',
names=['CHR', 'SNP', 'cM', 'BP', 'A1', 'A2'])
if __name__ == '__main__':
from primitives import Dataset, GenomicSubset, SnpSubset
import copy
from time import time
import argparse
np.random.seed(0)
parser = argparse.ArgumentParser()
parser.add_argument('--M',
type=int,
required=True,
help='the number of SNPs to use')
parser.add_argument('-check_dense', action='store_true', default=False)
args = parser.parse_args()
d = Dataset('GERA', forced_M=args.M)
indivs = d.random_indivs(200)
t0 = time()
R = LdMatrix(d, indivs, 200)
R.add_ridge(0.05)
print('computing R took', time() - t0)
print('shape of R is:', R.covcsr.shape)
# tiny = GenomicSubset('tiny')
# tiny_irs = SnpSubset(d, bedtool=tiny.bedtool).irs
tiny_irs = IntRangeSet('300:350')
RA = LdMatrix(d, indivs, 200, snpset_irs=tiny_irs, output=False)
b = np.random.randn(d.M)
# check inverse computation
def refpanel(self):
if self.__refpanel is None:
self.__refpanel = Dataset(self.params.refpanel)
return self.__refpanel
print(time.time()-t0, 'variance is {} + {} + {} = {}'.format(variance1, variance2, variance3, variance))
print('zscore:', point_estimate / np.sqrt(variance))
return point_estimate, variance, R, RA
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--refpanel', type=str, required=True)
parser.add_argument('--ldblocks', type=str, required=False,
default='pickrell_ldblocks.hg19.eur.bed')
parser.add_argument('--region', type=str, required=True)
parser.add_argument('--sumstats_path', type=str, required=True)
args = parser.parse_args()
print('loading reference panel')
refpanel = Dataset(args.refpanel)
print('loading region')
A = GenomicSubset(args.region)
print('loading ld blocks')
blocks = BedTool(paths.reference + args.ldblocks)
print('finding ld blocks that overlap with A')
relevant_blocks = blocks.intersect(A.bedtool, wa=True).saveas()
print('found', len(relevant_blocks), 'blocks that overlap with A')
print('reading refpanel bim')
refpanel_bim = pd.read_csv(refpanel.genotypes_bedfile.filename + '.bim',
sep='\t',
names=['CHR', 'SNP', 'cM', 'BP', 'A1', 'A2'])
