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"""Procedures related rqtl computations"""
import os
from typing import Dict, List, Union
import numpy as np
from flask import current_app
from gn3.commands import compose_rqtl_cmd
from gn3.computations.gemma import generate_hash_of_string
from gn3.fs_helpers import get_hash_of_files
def generate_rqtl_cmd(rqtl_wrapper_cmd: str,
rqtl_wrapper_kwargs: Dict,
rqtl_wrapper_bool_kwargs: list) -> Dict:
"""Given the base rqtl_wrapper command and
dict of keyword arguments, return the full rqtl_wrapper command and an
output filename generated from a hash of the genotype and phenotype files
"""
# Generate a hash from contents of the genotype and phenotype files
_hash = get_hash_of_files(
[v for k, v in rqtl_wrapper_kwargs.items() if k in ["g", "p"]])
# Append to hash a hash of keyword arguments
_hash += generate_hash_of_string(
",".join([f"{k}:{v}" for k, v in rqtl_wrapper_kwargs.items() if k not in ["g", "p"]]))
# Append to hash a hash of boolean keyword arguments
_hash += generate_hash_of_string(
",".join(rqtl_wrapper_bool_kwargs))
# Temporarily substitute forward-slashes in hash with underscores
_hash = _hash.replace("/", "_")
_output_filename = f"{_hash}-output.csv"
rqtl_wrapper_kwargs["filename"] = _output_filename
return {
"output_file":
_output_filename,
"rqtl_cmd":
compose_rqtl_cmd(rqtl_wrapper_cmd=rqtl_wrapper_cmd,
rqtl_wrapper_kwargs=rqtl_wrapper_kwargs,
rqtl_wrapper_bool_kwargs=rqtl_wrapper_bool_kwargs)
}
def process_rqtl_mapping(file_name: str) -> List:
"""Given an output file name, read in R/qtl results and return
a List of marker objects
"""
marker_obs = []
# Later I should probably redo this using csv.read to avoid the
# awkwardness with removing quotes with [1:-1]
with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
"output", file_name), "r") as the_file:
for line in the_file:
line_items = line.split(",")
if line_items[1][1:-1] == "chr" or not line_items:
# Skip header line
continue
# Convert chr to int if possible
the_chr: Union[int, str]
try:
the_chr = int(line_items[1][1:-1])
except ValueError:
the_chr = line_items[1][1:-1]
this_marker = {
"name": line_items[0][1:-1],
"chr": the_chr,
"cM": float(line_items[2]),
"Mb": float(line_items[2]),
"lod_score": float(line_items[3])
}
marker_obs.append(this_marker)
return marker_obs
def process_rqtl_pairscan(file_name: str, geno_file: str) -> List:
"""Given an output file name, read in R/qtl pair-scan results and return
a list of both the JSON needed for the d3panels figure and a list of results
to be used when generating the results table (which will include marker names)
"""
figure_data = pairscan_for_figure(file_name)
table_data = pairscan_for_table(file_name, geno_file)
return [figure_data, table_data]
def pairscan_for_figure(file_name: str) -> Dict:
"""Given an output file name, read in R/qtl pair-scan results and return
the JSON needed for the d3panels figure
"""
figure_data = {}
# Open the file with the actual results, written as a list of lists
with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
"output", file_name), "r") as the_file:
lod_results = []
for i, line in enumerate(the_file):
if i == 0: # Skip first line
continue
line_items = [item.rstrip('\n') for item in line.split(",")]
lod_results.append(line_items[1:]) # Append all but first item in line
figure_data['lod'] = lod_results
# Open the map file with the list of markers/pseudomarkers and their positions
with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
"output", "MAP_" + file_name), "r") as the_file:
chr_list = []
pos_list = []
for i, line in enumerate(the_file):
if i == 0: # Skip first line
continue
line_items = [item.rstrip('\n') for item in line.split(",")]
chr_list.append(line_items[1][1:-1])
pos_list.append(line_items[2])
figure_data['chr'] = chr_list
figure_data['pos'] = pos_list
return figure_data
def pairscan_for_table(file_name: str, geno_file: str) -> List:
"""Given an output file name, read in R/qtl pair-scan results and return
a list of results to be used when generating the results table (which will include marker names)
"""
table_data = []
# Open the map file with the list of markers/pseudomarkers and create list of marker obs
with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
"output", "MAP_" + file_name), "r") as the_file:
marker_list = []
for i, line in enumerate(the_file.readlines()[1:]):
line_items = [item.rstrip('\n') for item in line.split(",")]
this_marker = {
'name': line_items[0],
'chr': line_items[1][1:-1], # Strip quotes from beginning and end of chr string
'pos': line_items[2]
}
marker_list.append(this_marker)
# Open the file with the actual results and write the results as
# they will be displayed in the results table
with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
"output", file_name), "r") as the_file:
for i, line in enumerate(the_file.readlines()[1:]):
marker_1 = marker_list[i]
line_items = [item.rstrip('\n') for item in line.split(",")]
for j, item in enumerate(line_items[1:]):
marker_2 = marker_list[j]
try:
lod_score = f"{float(item):.3f}"
except:
lod_score = f"{item}"
this_line = {
'marker1': f"{marker_1['name']}",
'pos1': f"Chr {marker_1['chr']} @ {float(marker_1['pos']):.1f} cM",
'lod': lod_score,
'marker2': f"{marker_2['name']}",
'pos2': f"Chr {marker_2['chr']} @ {float(marker_2['pos']):.1f} cM"
}
table_data.append(this_line)
return table_data
def process_perm_output(file_name: str):
"""Given base filename, read in R/qtl permutation output and calculate
suggestive and significant thresholds
"""
perm_results = []
with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
"output", "PERM_" + file_name), "r") as the_file:
for i, line in enumerate(the_file):
if i == 0:
# Skip header line
continue
line_items = line.split(",")
perm_results.append(float(line_items[1]))
suggestive = np.percentile(np.array(perm_results), 67)
significant = np.percentile(np.array(perm_results), 95)
return perm_results, suggestive, significant
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