def main(p_dict):
ld_dict = ld.get_ld_dict_using_p_dict(p_dict, summary_dict={})
ldpred_inf_genomewide(data_file=p_dict['cf'],
out_file_prefix=p_dict['out'],
ld_radius=p_dict['ldr'],
ld_dict=ld_dict,
n=p_dict['N'],
h2=p_dict['h2'],
verbose=p_dict['debug'])
def main(p_dict):
#Check parameters
summary_dict = {}
summary_dict[0] = {
'name': 'Coordinated data filename',
'value': p_dict['cf']
}
summary_dict[0.1] = {
'name': 'SNP weights output file (prefix)',
'value': p_dict['out']
}
eff_type = get_eff_type(p_dict['cf'])
#If already BLUP betas, then skip LD calculation
if eff_type != 'BLUP':
summary_dict[0.2] = {
'name': 'LD data filename (prefix)',
'value': p_dict['ldf']
}
summary_dict[1.01] = {
'name': 'LD radius used',
'value': str(p_dict['ldr'])
}
summary_dict[1] = {'name': 'dash', 'value': 'LD information'}
t0 = time.time()
ld_dict = ld.get_ld_dict_using_p_dict(p_dict, summary_dict)
t1 = time.time()
t = (t1 - t0)
summary_dict[1.2] = {
'name': 'Running time for calculating LD information:',
'value': '%d min and %0.2f secs' % (t / 60, t % 60)
}
t0 = time.time()
summary_dict[1.9] = {'name': 'dash', 'value': 'LDpred-fast'}
ldpred_fast_genomewide(
data_file=p_dict['cf'],
out_file_prefix=p_dict['out'],
ps=p_dict['f'],
ld_radius=p_dict['ldr'],
ld_dict=ld_dict,
n=p_dict['N'],
h2=p_dict['h2'],
use_gw_h2=p_dict['use_gw_h2'],
eff_type=eff_type,
summary_dict=summary_dict,
debug=p_dict['debug'],
)
t1 = time.time()
t = (t1 - t0)
summary_dict[3] = {
'name': 'Running time for LDpred-fast:',
'value': '%d min and %0.2f secs' % (t / 60, t % 60)
}
reporting.print_summary(summary_dict, 'Summary of LDpred-fast')
def main(p_dict):
#Check parameters
summary_dict = {}
summary_dict[0] = {
'name': 'Coordinated data filename',
'value': p_dict['cf']
}
summary_dict[0.1] = {
'name': 'SNP weights output file (prefix)',
'value': p_dict['out']
}
summary_dict[0.2] = {
'name': 'LD data filename (prefix)',
'value': p_dict['ldf']
}
summary_dict[1] = {'name': 'LD radius used', 'value': str(p_dict['ldr'])}
t0 = time.time()
summary_dict[1.09] = {'name': 'dash', 'value': 'LD information'}
ld_dict = ld.get_ld_dict_using_p_dict(p_dict, summary_dict)
t1 = time.time()
t = (t1 - t0)
summary_dict[1.2] = {
'name': 'Running time for calculating LD information:',
'value': '%d min and %0.2f secs' % (t / 60, t % 60)
}
t0 = time.time()
summary_dict[1.9] = {'name': 'dash', 'value': 'LDpred Gibbs sampler'}
ldpred_genomewide(data_file=p_dict['cf'],
out_file_prefix=p_dict['out'],
ps=p_dict['f'],
ld_radius=p_dict['ldr'],
ld_dict=ld_dict,
n=p_dict['N'],
num_iter=p_dict['n_iter'],
burn_in=p_dict['n_burn_in'],
h2=p_dict['h2'],
use_gw_h2=p_dict['use_gw_h2'],
sampl_var_shrink_factor=1,
verbose=p_dict['debug'],
summary_dict=summary_dict)
t1 = time.time()
t = (t1 - t0)
summary_dict[2.2] = {
'name': 'Running time for Gibbs sampler(s):',
'value': '%d min and %0.2f secs' % (t / 60, t % 60)
}
reporting.print_summary(summary_dict, 'Summary of LDpred Gibbs')
def main(p_dict):
summary_dict = {}
summary_dict[0] = {
'name': 'Coordinated data filename',
'value': p_dict['cf']
}
summary_dict[0.1] = {
'name': 'SNP weights output file (prefix)',
'value': p_dict['out']
}
summary_dict[0.2] = {
'name': 'LD data filename (prefix)',
'value': p_dict['ldf']
}
summary_dict[1] = {'name': 'LD radius used', 'value': str(p_dict['ldr'])}
t0 = time.time()
summary_dict[1.09] = {'name': 'dash', 'value': 'LD information'}
ld_dict = ld.get_ld_dict_using_p_dict(p_dict, summary_dict={})
t1 = time.time()
t = (t1 - t0)
summary_dict[1.2] = {
'name': 'Running time for calculating LD information:',
'value': '%d min and %0.2f secs' % (t / 60, t % 60)
}
t0 = time.time()
summary_dict[1.9] = {'name': 'dash', 'value': 'LDpred infinitesimal model'}
ldpred_inf_genomewide(data_file=p_dict['cf'],
out_file_prefix=p_dict['out'],
ld_radius=p_dict['ldr'],
ld_dict=ld_dict,
n=p_dict['N'],
h2=p_dict['h2'],
use_gw_h2=p_dict['use_gw_h2'],
verbose=p_dict['debug'],
summary_dict=summary_dict)
t1 = time.time()
t = (t1 - t0)
summary_dict[2.2] = {
'name': 'Running time for LDpred-inf:',
'value': '%d min and %0.2f secs' % (t / 60, t % 60)
}
reporting.print_summary(summary_dict, 'Summary of LDpred-inf')
def test_get_chromosome_herits(self):
p_dict = make_p_dict(
'--debug',
'inf',
'--cf=%s/test_data/goldens/golden.coord.hdf5' % TEST_DIR,
'--ldr=100',
'--ldf=' + self.tmp_file_prefix,
'--N=10000',
'--out=' + self.tmp_file_prefix,
)
summary_dict = {}
ld_dict = ld.get_ld_dict_using_p_dict(p_dict, summary_dict)
coord_file = os.path.join(TEST_DIR,
'test_data/goldens/golden.coord.hdf5')
df = h5py.File(coord_file, 'r')
herit_dict = ld.get_chromosome_herits(df['cord_data'],
ld_dict['ld_scores_dict'],
n=p_dict['N'],
h2=None)
print(herit_dict)
self.assertAlmostEqual(herit_dict['chrom_1'], 0.0741219)
self.assertAlmostEqual(herit_dict['gw_h2_ld_score_est'], 0.0741219)