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import os
import math
import string
import random
import json
import re
from gn2.base import webqtlConfig
from gn2.base.trait import GeneralTrait
from gn2.base.data_set import create_dataset
from gn2.utility.tools import flat_files, REAPER_COMMAND, TEMPDIR
def run_reaper(this_trait, this_dataset, samples, vals, json_data, num_perm, boot_check, num_bootstrap, do_control, control_marker, manhattan_plot, first_run=True, output_files=None):
"""Generates p-values for each marker using qtlreaper"""
if first_run:
if this_dataset.group.genofile != None:
genofile_name = this_dataset.group.genofile[:-5]
else:
genofile_name = this_dataset.group.name
trait_filename = f"{str(this_trait.name)}_{str(this_dataset.name)}_pheno"
gen_pheno_txt_file(samples, vals, trait_filename)
output_filename = (f"{this_dataset.group.name}_GWA_"
+ ''.join(random.choice(string.ascii_uppercase + string.digits)
for _ in range(6))
)
bootstrap_filename = None
permu_filename = None
opt_list = []
if boot_check and num_bootstrap > 0:
bootstrap_filename = (f"{this_dataset.group.name}_BOOTSTRAP_"
+ ''.join(random.choice(string.ascii_uppercase + string.digits)
for _ in range(6))
)
opt_list.append("-b")
opt_list.append(f"--n_bootstrap {str(num_bootstrap)}")
opt_list.append(
f"--bootstrap_output {webqtlConfig.GENERATED_IMAGE_DIR}{bootstrap_filename}.txt")
if num_perm > 0:
permu_filename = ("{this_dataset.group.name}_PERM_"
+ ''.join(random.choice(string.ascii_uppercase
+ string.digits) for _ in range(6))
)
opt_list.append("-n " + str(num_perm))
opt_list.append(
"--permu_output " + webqtlConfig.GENERATED_IMAGE_DIR + permu_filename + ".txt")
if control_marker != "" and do_control == "true":
opt_list.append("-c " + control_marker)
if manhattan_plot != True:
opt_list.append("--interval 1")
reaper_command = (REAPER_COMMAND +
' --geno {0}/{1}.geno --traits {2}/gn2/{3}.txt {4} -o {5}{6}.txt'.format(flat_files('genotype'),
genofile_name,
TEMPDIR,
trait_filename,
" ".join(
opt_list),
webqtlConfig.GENERATED_IMAGE_DIR,
output_filename))
os.system(reaper_command)
else:
output_filename, permu_filename, bootstrap_filename = output_files
marker_obs, permu_vals, bootstrap_vals = parse_reaper_output(
output_filename, permu_filename, bootstrap_filename)
suggestive = 0
significant = 0
if len(permu_vals) > 0:
suggestive = permu_vals[int(num_perm * 0.37 - 1)]
significant = permu_vals[int(num_perm * 0.95 - 1)]
return (marker_obs, permu_vals, suggestive, significant, bootstrap_vals,
[output_filename, permu_filename, bootstrap_filename])
def gen_pheno_txt_file(samples, vals, trait_filename):
"""Generates phenotype file for GEMMA"""
with open(f"{TEMPDIR}/gn2/{trait_filename}.txt", "w") as outfile:
outfile.write("Trait\t")
filtered_sample_list = []
filtered_vals_list = []
for i, sample in enumerate(samples):
if vals[i] != "x":
filtered_sample_list.append(sample)
filtered_vals_list.append(vals[i])
samples_string = "\t".join(filtered_sample_list)
outfile.write(samples_string + "\n")
outfile.write("T1\t")
values_string = "\t".join(filtered_vals_list)
outfile.write(values_string)
def parse_reaper_output(gwa_filename, permu_filename, bootstrap_filename):
included_markers = []
p_values = []
marker_obs = []
only_cm = False
only_mb = False
with open(f"{webqtlConfig.GENERATED_IMAGE_DIR}{gwa_filename}.txt") as output_file:
for line in output_file:
if line.startswith("ID\t"):
if len(line.split("\t")) < 8:
if 'cM' in line.split("\t"):
only_cm = True
else:
only_mb = True
continue
else:
marker = {}
marker['name'] = line.split("\t")[1]
try:
marker['chr'] = int(line.split("\t")[2])
except:
marker['chr'] = line.split("\t")[2]
if only_cm or only_mb:
if only_cm:
marker['cM'] = float(line.split("\t")[3])
else:
if float(line.split("\t")[3]) > 1000:
marker['Mb'] = float(line.split("\t")[3]) / 1000000
else:
marker['Mb'] = float(line.split("\t")[3])
if float(line.split("\t")[6]) != 1:
marker['p_value'] = float(line.split("\t")[6])
marker['lrs_value'] = float(line.split("\t")[4])
marker['lod_score'] = marker['lrs_value'] / 4.61
marker['additive'] = float(line.split("\t")[5])
else:
marker['cM'] = float(line.split("\t")[3])
if float(line.split("\t")[4]) > 1000:
marker['Mb'] = float(line.split("\t")[4]) / 1000000
else:
marker['Mb'] = float(line.split("\t")[4])
if float(line.split("\t")[7]) != 1:
marker['p_value'] = float(line.split("\t")[7])
marker['lrs_value'] = float(line.split("\t")[5])
marker['lod_score'] = marker['lrs_value'] / 4.61
marker['additive'] = float(line.split("\t")[6])
marker_obs.append(marker)
# ZS: Results have to be reordered because the new reaper returns results sorted alphabetically by chr for some reason, resulting in chr 1 being followed by 10, etc
sorted_indices = natural_sort(marker_obs)
permu_vals = []
if permu_filename:
with open(f"{webqtlConfig.GENERATED_IMAGE_DIR}{permu_filename}.txt") as permu_file:
for line in permu_file:
permu_vals.append(float(line))
bootstrap_vals = []
if bootstrap_filename:
with open(f"{webqtlConfig.GENERATED_IMAGE_DIR}{bootstrap_filename}.txt") as bootstrap_file:
for line in bootstrap_file:
bootstrap_vals.append(int(line))
marker_obs = [marker_obs[i] for i in sorted_indices]
if len(bootstrap_vals) > 0:
bootstrap_vals = [bootstrap_vals[i] for i in sorted_indices]
return marker_obs, permu_vals, bootstrap_vals
def natural_sort(marker_list):
"""
Function to naturally sort numbers + strings, adopted from user Mark Byers here: https://stackoverflow.com/questions/4836710/does-python-have-a-built-in-function-for-string-natural-sort
Changed to return indices instead of values, though, since the same reordering needs to be applied to bootstrap results
"""
def convert(text):
if text.isdigit():
return int(text)
else:
if text != "M":
return text.lower()
else:
return "z"
alphanum_key = lambda key: [convert(c) for c in re.split(
'([0-9]+)', str(marker_list[key]['chr']))]
return sorted(list(range(len(marker_list))), key=alphanum_key)
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