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 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 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 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))
