def gen_human_results(self, pheno_vector, key, temp_uuid):
file_base = os.path.join(webqtlConfig.PYLMM_PATH, self.dataset.group.name)
print("file_base:", file_base)
plink_input = input.plink(file_base, type='b')
input_file_name = os.path.join(webqtlConfig.SNP_PATH, self.dataset.group.name + ".snps.gz")
pheno_vector = pheno_vector.reshape((len(pheno_vector), 1))
covariate_matrix = np.ones((pheno_vector.shape[0],1))
kinship_matrix = np.fromfile(open(file_base + '.kin','r'),sep=" ")
kinship_matrix.resize((len(plink_input.indivs),len(plink_input.indivs)))
print("Before creating params")
params = dict(pheno_vector = pheno_vector.tolist(),
covariate_matrix = covariate_matrix.tolist(),
input_file_name = input_file_name,
kinship_matrix = kinship_matrix.tolist(),
refit = False,
temp_uuid = temp_uuid,
# meta data
timestamp = datetime.datetime.now().isoformat(),
)
print("After creating params")
json_params = json.dumps(params)
Redis.set(key, json_params)
Redis.expire(key, 60*60)
print("Before creating the command")
command = 'python /home/zas1024/gene/wqflask/wqflask/my_pylmm/pyLMM/lmm.py --key {} --species {}'.format(key,
"human")
print("command is:", command)
os.system(command)
json_results = Redis.blpop("pylmm:results:" + temp_uuid, 45*60)
results = json.loads(json_results[1])
t_stats = results['t_stats']
p_values = results['p_values']
#p_values, t_stats = lmm.run_human(key)
#p_values, t_stats = lmm.run_human(
# pheno_vector,
# covariate_matrix,
# input_file_name,
# kinship_matrix,
# loading_progress=tempdata
# )
return p_values, t_stats
def create_snp_iterator_file(group):
plink_file_base = os.path.join(webqtlConfig.PYLMM_PATH, group)
plink_input = input.plink(plink_file_base, type='b')
data = dict(plink_input = list(plink_input),
numSNPs = plink_input.numSNPs)
#input_dict = {}
#
#input_dict['plink_input'] = list(plink_input)
#input_dict['numSNPs'] = plink_input.numSNPs
#
snp_file_base = os.path.join(webqtlConfig.SNP_PATH, group + ".snps.gz")
with gzip.open(snp_file_base, "wb") as fh:
pickle.dump(data, fh, pickle.HIGHEST_PROTOCOL)
def gen_human_results(self, pheno_vector, tempdata):
file_base = os.path.join(webqtlConfig.PYLMM_PATH, self.dataset.group.name)
plink_input = input.plink(file_base, type='b')
input_file_name = os.path.join(webqtlConfig.SNP_PATH, self.dataset.group.name + ".snps.gz")
pheno_vector = pheno_vector.reshape((len(pheno_vector), 1))
covariate_matrix = np.ones((pheno_vector.shape[0],1))
kinship_matrix = np.fromfile(open(file_base + '.kin','r'),sep=" ")
kinship_matrix.resize((len(plink_input.indivs),len(plink_input.indivs)))
p_values, t_stats = lmm.run_human(
pheno_vector,
covariate_matrix,
input_file_name,
kinship_matrix,
loading_progress=tempdata
)
return p_values, t_stats
