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import csv
import hashlib
import io
import json
import requests
import shutil
from typing import Dict
from typing import List
from typing import Optional
from typing import TextIO
from urllib.parse import urljoin
import numpy as np
from gn3.debug import __pk__
from gn2.base.webqtlConfig import TMPDIR
from gn2.base.trait import create_trait
from gn2.utility.redis_tools import get_redis_conn
from gn2.utility.tools import locate, get_setting, GN3_LOCAL_URL
from gn2.wqflask.database import database_connection
def run_rqtl(trait_name, vals, samples, dataset, pair_scan, mapping_scale, model, method, num_perm, perm_strata_list, do_control, control_marker, manhattan_plot, cofactors):
"""Run R/qtl by making a request to the GN3 endpoint and reading in the output file(s)"""
pheno_file = write_phenotype_file(trait_name, samples, vals, dataset, cofactors, perm_strata_list)
if dataset.group.genofile:
geno_file = locate(dataset.group.genofile, "genotype")
else:
geno_file = locate(dataset.group.name + ".geno", "genotype")
post_data = {
"pheno_file": pheno_file,
"geno_file": geno_file,
"model": model,
"method": method,
"nperm": num_perm,
"scale": mapping_scale
}
if pair_scan:
post_data["pairscan"] = True
if cofactors:
covarstruct_file = write_covarstruct_file(cofactors)
post_data["covarstruct"] = covarstruct_file
if do_control == "true" and control_marker:
post_data["control"] = control_marker
if not manhattan_plot and not pair_scan:
post_data["interval"] = True
if cofactors:
post_data["addcovar"] = True
if perm_strata_list:
post_data["pstrata"] = True
rqtl_output = __pk__(
"R/qtl or Pair-Scan results",
requests.post(urljoin(GN3_LOCAL_URL, "api/rqtl/compute"),
data=post_data).json())
if num_perm > 0:
return rqtl_output['perm_results'], rqtl_output['suggestive'], rqtl_output['significant'], rqtl_output['results']
else:
return rqtl_output['results']
def get_hash_of_textio(the_file: TextIO) -> str:
"""Given a StringIO, return the hash of its contents"""
the_file.seek(0)
hash_of_file = hashlib.md5(the_file.read().encode()).hexdigest()
hash_of_file = hash_of_file.replace("/", "_") # Replace / with _ to prevent issue with filenames being translated to directories
return hash_of_file
def write_covarstruct_file(cofactors: str) -> str:
"""
Given list of cofactors (as comma-delimited string), write
a comma-delimited file where the first column consists of cofactor names
and the second column indicates whether they're numerical or categorical
"""
trait_datatype_json = None
with database_connection(get_setting("SQL_URI")) as conn, conn.cursor() as cursor:
cursor.execute("SELECT value FROM TraitMetadata WHERE type='trait_data_type'")
trait_datatype_json = json.loads(cursor.fetchone()[0])
covar_struct_file = io.StringIO()
writer = csv.writer(covar_struct_file, delimiter="\t", quoting = csv.QUOTE_NONE)
for cofactor in cofactors.split(","):
datatype = trait_datatype_json[cofactor] if cofactor in trait_datatype_json else "numerical"
cofactor_name = cofactor.split(":")[0]
writer.writerow([cofactor_name, datatype])
hash_of_file = get_hash_of_textio(covar_struct_file)
file_path = TMPDIR + hash_of_file + ".csv"
with open(file_path, "w") as fd:
covar_struct_file.seek(0)
shutil.copyfileobj(covar_struct_file, fd)
return file_path
def write_phenotype_file(trait_name: str,
samples: List[str],
vals: List,
dataset_ob,
cofactors: Optional[str] = None,
perm_strata_list: Optional[List] = None) -> TextIO:
"""Given trait name, sample list, value list, dataset ob, and optional string
representing cofactors, return the file's full path/name
"""
cofactor_data = cofactors_to_dict(cofactors, dataset_ob, samples)
pheno_file = io.StringIO()
writer = csv.writer(pheno_file, delimiter="\t", quoting=csv.QUOTE_NONE)
header_row = ["Samples", trait_name]
header_row += [cofactor for cofactor in cofactor_data]
if perm_strata_list:
header_row.append("Strata")
writer.writerow(header_row)
for i, sample in enumerate(samples):
this_row = [sample]
if vals[i] != "x":
this_row.append(str(round(float(vals[i]), 3)))
else:
this_row.append("NA")
for cofactor in cofactor_data:
this_row.append(cofactor_data[cofactor][i])
if perm_strata_list:
this_row.append(perm_strata_list[i])
writer.writerow(this_row)
hash_of_file = get_hash_of_textio(pheno_file)
file_path = TMPDIR + hash_of_file + ".csv"
with open(file_path, "w") as fd:
pheno_file.seek(0)
shutil.copyfileobj(pheno_file, fd)
return file_path
def cofactors_to_dict(cofactors: str, dataset_ob, samples) -> Dict:
"""Given a string of cofactors, the trait being mapped's dataset ob,
and list of samples, return cofactor data as a Dict
"""
cofactor_dict = {}
if cofactors:
dataset_ob.group.get_samplelist(redis_conn=get_redis_conn())
sample_list = dataset_ob.group.samplelist
for cofactor in cofactors.split(","):
cofactor_name, cofactor_dataset = cofactor.split(":")
if cofactor_dataset == dataset_ob.name:
cofactor_dict[cofactor_name] = []
trait_ob = create_trait(dataset=dataset_ob,
name=cofactor_name)
sample_data = trait_ob.data
for index, sample in enumerate(samples):
if sample in sample_data:
sample_value = str(round(float(sample_data[sample].value), 3))
cofactor_dict[cofactor_name].append(sample_value)
else:
cofactor_dict[cofactor_name].append("NA")
return cofactor_dict
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