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import os
import math
import string
import random
import json
from gn2.base import webqtlConfig
from gn2.base.trait import create_trait
from gn2.base.data_set import create_dataset
from gn2.utility.redis_tools import get_redis_conn
from gn2.utility.tools import flat_files, assert_file
from gn2.utility.tools import GEMMA_WRAPPER_COMMAND
from gn2.utility.tools import TEMPDIR
from gn2.utility.tools import WEBSERVER_MODE
from gn2.utility.tools import get_setting
from gn2.wqflask.database import database_connection
from gn3.computations.gemma import generate_hash_of_string
GEMMAOPTS = "-debug"
if WEBSERVER_MODE == 'PROD':
GEMMAOPTS = "-no-check"
def generate_random_n_string(n):
return ''.join(random.choice(string.ascii_uppercase + string.digits)
for _ in range(n))
def run_gemma(this_trait, this_dataset, samples, vals, covariates, use_loco,
maf=0.01, first_run=True, output_files=None):
"""Generates p-values for each marker using GEMMA"""
if this_dataset.group.genofile is not None:
genofile_name = this_dataset.group.genofile[:-5]
else:
genofile_name = this_dataset.group.name
if first_run:
pheno_filename = gen_pheno_txt_file(this_dataset, genofile_name, vals)
if not os.path.isfile(f"{webqtlConfig.GENERATED_IMAGE_DIR}"
f"{genofile_name}_output.assoc.txt"):
open((f"{webqtlConfig.GENERATED_IMAGE_DIR}"
f"{genofile_name}_output.assoc.txt"),
"w+")
k_output_filename = (f"{this_dataset.group.name}_K_"
f"{generate_random_n_string(6)}")
gwa_output_filename = (f"{this_dataset.group.name}_GWA_"
f"{generate_random_n_string(6)}")
this_chromosomes_name = []
with database_connection(get_setting("SQL_URI")) as conn, conn.cursor() as db_cursor:
for this_chr in this_dataset.species.chromosomes.chromosomes(db_cursor):
this_chromosomes_name.append(this_dataset.species.chromosomes.chromosomes(db_cursor)[this_chr].name)
chr_list_string = ",".join(this_chromosomes_name)
if covariates != "":
covar_filename = gen_covariates_file(this_dataset, covariates, samples)
if str(use_loco).lower() == "true":
bimbam_dir = flat_files('genotype/bimbam')
geno_filepath = assert_file(
f"{bimbam_dir}/{genofile_name}_geno.txt")
pheno_filepath = f"{TEMPDIR}/gn2/{pheno_filename}.txt"
snps_filepath = assert_file(
f"{bimbam_dir}/{genofile_name}_snps.txt")
k_json_output_filepath = f"{TEMPDIR}/gn2/{k_output_filename}.json"
generate_k_command = (f"{GEMMA_WRAPPER_COMMAND} --json --loco "
f"{chr_list_string} -- {GEMMAOPTS} "
f"-g {geno_filepath} -p "
f"{pheno_filepath} -a "
f"{snps_filepath} -gk > "
f"{k_json_output_filepath}")
os.system(generate_k_command)
gemma_command = (f"{GEMMA_WRAPPER_COMMAND} --json --loco "
f"--input {k_json_output_filepath} "
f"-- {GEMMAOPTS} "
f"-g {geno_filepath} "
f"-p {pheno_filepath} ")
if covariates != "":
gemma_command += (f"-c {flat_files('mapping')}/"
f"{covar_filename}.txt "
f"-a {flat_files('genotype/bimbam')}/"
f"{genofile_name}_snps.txt "
f"-lmm 9 -maf {maf} > {TEMPDIR}/gn2/"
f"{gwa_output_filename}.json")
else:
gemma_command += (f"-a {flat_files('genotype/bimbam')}/"
f"{genofile_name}_snps.txt -lmm 9 -maf "
f"{maf} > "
f"{TEMPDIR}/gn2/{gwa_output_filename}.json")
else:
generate_k_command = (f"{GEMMA_WRAPPER_COMMAND} --json -- "
f"{GEMMAOPTS} "
f" -g {flat_files('genotype/bimbam')}/"
f"{genofile_name}_geno.txt -p "
f"{TEMPDIR}/gn2/{pheno_filename}.txt -a "
f"{flat_files('genotype/bimbam')}/"
f"{genofile_name}_snps.txt -gk > "
f"{TEMPDIR}/gn2/{k_output_filename}.json")
os.system(generate_k_command)
gemma_command = (f"{GEMMA_WRAPPER_COMMAND} --json --input "
f"{TEMPDIR}/gn2/{k_output_filename}.json -- "
f"{GEMMAOPTS} "
f"-a {flat_files('genotype/bimbam')}/"
f"{genofile_name}_snps.txt "
f"-lmm 9 -g {flat_files('genotype/bimbam')}/"
f"{genofile_name}_geno.txt -p "
f"{TEMPDIR}/gn2/{pheno_filename}.txt ")
if covariates != "":
gemma_command += (f" -c {flat_files('mapping')}/"
f"{covar_filename}.txt > "
f"{TEMPDIR}/gn2/{gwa_output_filename}.json")
else:
gemma_command += f" > {TEMPDIR}/gn2/{gwa_output_filename}.json"
os.system(gemma_command)
else:
gwa_output_filename = output_files
if use_loco == "True":
marker_obs = parse_loco_output(this_dataset, gwa_output_filename)
return marker_obs, gwa_output_filename
else:
marker_obs = parse_loco_output(
this_dataset, gwa_output_filename, use_loco)
return marker_obs, gwa_output_filename
def gen_pheno_txt_file(this_dataset, genofile_name, vals):
"""Generates phenotype file for GEMMA"""
filename = "PHENO_" + generate_hash_of_string(this_dataset.name + str(vals)).replace("/", "_")
with open(f"{TEMPDIR}/gn2/{filename}.txt", "w") as outfile:
for value in vals:
if value == "x":
outfile.write("NA\n")
else:
outfile.write(value + "\n")
return filename
def gen_covariates_file(this_dataset, covariates, samples):
covariate_list = covariates.split(",")
covariate_data_object = []
for covariate in covariate_list:
this_covariate_data = []
trait_name = covariate.split(":")[0]
dataset_name = covariate.split(":")[1]
if dataset_name == "Temp":
temp_group = trait_name.split("_")[2]
dataset_ob = create_dataset(
dataset_name="Temp", dataset_type="Temp", group_name=temp_group)
else:
dataset_ob = create_dataset(covariate.split(":")[1])
trait_ob = create_trait(dataset=dataset_ob,
name=trait_name,
cellid=None)
this_dataset.group.get_samplelist(redis_conn=get_redis_conn())
trait_samples = this_dataset.group.samplelist
trait_sample_data = trait_ob.data
for index, sample in enumerate(trait_samples):
if sample in samples:
if sample in trait_sample_data:
sample_value = trait_sample_data[sample].value
this_covariate_data.append(sample_value)
else:
this_covariate_data.append("-9")
covariate_data_object.append(this_covariate_data)
filename = "COVAR_" + generate_hash_of_string(this_dataset.name + str(covariate_data_object)).replace("/", "_")
with open((f"{flat_files('mapping')}/"
f"{filename}.txt"),
"w") as outfile:
for i in range(len(covariate_data_object[0])):
for this_covariate in covariate_data_object:
outfile.write(str(this_covariate[i]) + "\t")
outfile.write("\n")
return filename
def parse_loco_output(this_dataset, gwa_output_filename, loco="True"):
output_filename = f"{TEMPDIR}/gn2/{gwa_output_filename}.json"
if os.stat(output_filename).st_size == 0:
return {}
output_filelist = []
with open(output_filename) as data_file:
data = json.load(data_file)
files = data['files']
for file in files:
output_filelist.append(file[2])
included_markers = []
p_values = []
marker_obs = []
previous_chr = 0
for this_file in output_filelist:
if not os.path.isfile(this_file):
break
with open(this_file) as output_file:
for line in output_file:
if line.startswith("chr\t"):
continue
else:
marker = {}
marker['name'] = line.split("\t")[1]
if line.split("\t")[0] != "X" and line.split("\t")[0] != "X/Y" and line.split("\t")[0] != "Y" and line.split("\t")[0] != "M":
if "chr" in line.split("\t")[0]:
marker['chr'] = int(line.split("\t")[0][3:])
else:
marker['chr'] = int(line.split("\t")[0])
if marker['chr'] > int(previous_chr):
previous_chr = marker['chr']
elif marker['chr'] < int(previous_chr):
break
else:
marker['chr'] = line.split("\t")[0]
marker['Mb'] = float(line.split("\t")[2]) / 1000000
marker['p_value'] = float(line.split("\t")[10])
marker['additive'] = -(float(line.split("\t")[7])/2)
if math.isnan(marker['p_value']) or (marker['p_value'] <= 0):
marker['lod_score'] = marker['p_value'] = 0
else:
marker['lod_score'] = -math.log10(marker['p_value'])
marker_obs.append(marker)
included_markers.append(line.split("\t")[1])
p_values.append(float(line.split("\t")[9]))
return marker_obs
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