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transform_gwas_to_long.py
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import os, json
from sys import platform, float_info
if platform=="win32":
file_sep = "\\"
else:
file_sep = "/"
# DATA_FOLDER = "data"
DATA_FOLDER = "."
NUM_SLICES = 128
NUM_COORDS = 256
NUM_COORDS_VALID = 246
NUM_SLICES_VALID = 119
SIG_THRESHOLD_BASE = 0.05
SIG_THRESHOLD = 5*(10**-8) /NUM_SLICES_VALID /NUM_COORDS_VALID # Vicki approximation of locus wide
SNIP_COUNTS = [842491, 913413, 774528, 796789, 706769, 733026, 639645, 600401, 468652, 553430, 539166, 524442, 398578,
353816, 309546, 335728, 293820, 309206, 252330, 240886, 148455, 149315, 354574]
NUM_SNIPS = sum(SNIP_COUNTS)
SIG_THRESHOLD_STRICT = 1/(NUM_SNIPS*NUM_SLICES_VALID*NUM_COORDS_VALID)
KEY_SIG_COORDS = "sig_coords"
KEY_SIG_COORDS_STRICT = "sig_coords_strict"
CHROMOSOMES = list(range(1, 23)) + ["X"]
SLICE_NUMS = range(1, NUM_SLICES+1)
COORD_NUMS = range(1, NUM_COORDS+1)
def safe_snipID(snipID_raw):
# TODO - fix for all windows and linux chars https://stackoverflow.com/questions/1976007/what-characters-are-forbidden-in-windows-and-linux-directory-names
if ":" in snipID_raw:
snipID = snipID_raw.split(":")[-1]
return snipID
else:
return snipID_raw
return snipID_raw
def process_chromosome(chromosomes=[22], slice_nums=[]):
chr_dict = {} # all chromosomes - combine at end instead!
if len(slice_nums)==0:
slice_nums2 = SLICE_NUMS
else:
slice_nums2 = slice_nums
for chdx, chromosome in enumerate(chromosomes): # for each chromosome or 1 target chromosome
print("processing chrom", chdx, chromosome)
# chr_folder = os.path.join(DATA_FOLDER, "chr{}".format(str(chromosome)))
chr_folder = os.path.join(DATA_FOLDER, "chr{}".format(str(chromosome)), "sigSNPs")
if not os.path.isdir(chr_folder):
continue
out_folder = os.path.join(chr_folder, "long")
if not os.path.isdir(out_folder):
os.makedirs(out_folder)
chrom_long_file = os.path.join(chr_folder, "coord_summary_{}_long.csv".format(chromosome))
if not os.path.isfile(chrom_long_file):
with open(chrom_long_file, 'w') as fout:
fout.write("snip,x,y,beta,pvalue,bonf\n")
fout.close()
snip_info_file = os.path.join(chr_folder, "snip_info_{}.csv".format(chromosome))
if not os.path.isfile(snip_info_file):
with open(snip_info_file, 'w') as fout:
fout.write("snip,pos,A1,snip_index\n")
fout.close()
elif len(slice_nums)>0:
snip_info_counter = 0
with open(snip_info_file, "r") as fin:
for l in fin:
l_toks = l.rstrip().split(",")
snip_info_counter +=1
if snip_info_counter==1:
snip_info_header = l_toks
else:
snipID,pos,A1,snip_index = l_toks
snip_info_dict[snipID] = 1
fin.close()
chr_snip_dict = {}
snip_info_dict = {}
for sdx, slice_num in enumerate(slice_nums2): # for each slice, open and process slice file
print("processing chrom,slice", chdx, chromosome, slice_num)
fname = "slice{}_result.txt".format(slice_num)
fpath = os.path.join(chr_folder, fname)
if not os.path.isfile(fpath):
print("slice data not found: ", slice_num, fpath)
continue
counter = 0
with open(fpath, 'r') as fin:
for l in fin:
counter+=1
l_toks = l.rstrip().split(",")
if counter==1:
header = l_toks
else:
cur_dict = dict(zip(header, l_toks)) # line dict
pos = cur_dict["POS"]
snipID_raw = cur_dict["ID"]
snipID = safe_snipID(snipID_raw)
snip_index = counter-1 # track which subfolder for netlify
if snipID not in snip_info_dict:
# snip_info_dict[snipID] = {"POS":pos, "chrom":chromosome, "A1":cur_dict["A1"]}
snip_info_dict[snipID] = 1 # so it only writes once
with open(snip_info_file, "a") as fout_info:
vals = [snipID, pos, cur_dict["A1"], snip_index]
fout_info.write("{}\n".format(",".join([str(x) for x in vals])))
fout_info.close()
if snipID not in chr_snip_dict: # init
chr_snip_dict[snipID] = {KEY_SIG_COORDS:[], KEY_SIG_COORDS_STRICT:[]}
out_sub_folder = os.path.join(out_folder, "{}".format(int(snip_index//25000)))
if not os.path.isdir(out_sub_folder):
os.makedirs(out_sub_folder)
snip_out_file = os.path.join(out_sub_folder, "{}_{}.txt".format(chromosome, snipID))
if not os.path.isfile(snip_out_file): # write header if not exist
with open(snip_out_file, 'w') as fout_snip_file:
fout_snip_file.write("x,y,beta,pval\n")
fout_snip_file.close()
for cdx, coord_num in enumerate(COORD_NUMS):
coord_beta_key = "{}_{}_BETA".format(slice_num, coord_num)
coord_pval_key = "{}_{}_P".format(slice_num, coord_num)
if coord_beta_key in cur_dict and coord_pval_key in cur_dict:
coord_beta = cur_dict[coord_beta_key]
coord_pval = cur_dict[coord_pval_key]
with open(snip_out_file, "a") as fout_snip_file2:
vals = [slice_num, coord_num, coord_beta, coord_pval]
fout_snip_file2.write("{}\n".format(",".join([str(x) for x in vals])))
fout_snip_file2.close()
# if float(coord_pval)<SIG_THRESHOLD:
# chr_snip_dict[snipID][KEY_SIG_COORDS].append((slice_num, coord_num))
# if float(coord_pval)<SIG_THRESHOLD_STRICT:
# chr_snip_dict[snipID][KEY_SIG_COORDS_STRICT].append((slice_num, coord_num))
if float(coord_pval) < SIG_THRESHOLD:
is_bonf = float(coord_pval)<SIG_THRESHOLD_STRICT
with open(chrom_long_file, "a") as fout_chrome_file:
vals = [snipID, slice_num, coord_num, coord_beta, coord_pval, int(is_bonf)]
fout_chrome_file.write("{}\n".format(",".join([str(x) for x in vals])))
fout_chrome_file.close()
fin.close()
print("processed chrom,slice", chdx, chromosome, slice_num)
print("processed chrom", chdx, chromosome)
# with open(os.path.join(out_folder, "snip_info.json"), "w") as fout:
# json.dump(snip_info_dict, fout)
# fout.close()
#
# with open(os.path.join(out_folder, "chr_snip.json"), "w") as fout:
# json.dump(chr_snip_dict, fout)
# fout.close()
return
def process_chromosome_memory(chromosomes=[22], slice_nums=[]):
chr_dict = {} # all chromosomes - combine at end instead!
if len(slice_nums)==0:
slice_nums2 = SLICE_NUMS
else:
slice_nums2 = slice_nums
for chdx, chromosome in enumerate(chromosomes): # for each chromosome or 1 target chromosome
print("processing chrom", chdx, chromosome)
# chr_folder = os.path.join(DATA_FOLDER, "chr{}".format(str(chromosome)))
chr_folder = os.path.join(DATA_FOLDER, "chr{}".format(str(chromosome)), "sigSNPs")
if not os.path.isdir(chr_folder):
continue
out_folder = os.path.join(chr_folder, "long")
if not os.path.isdir(out_folder):
os.makedirs(out_folder)
chrom_long_file = os.path.join(chr_folder, "coord_summary_{}_long.csv".format(chromosome))
if not os.path.isfile(chrom_long_file):
with open(chrom_long_file, 'w') as fout:
fout.write("snip,chr,x,y,beta,pvalue,bonf\n")
fout.close()
snip_info_file = os.path.join(chr_folder, "snip_info_{}.csv".format(chromosome))
if not os.path.isfile(snip_info_file):
with open(snip_info_file, 'w') as fout:
fout.write("snip,chr,pos,A1,snip_index\n")
fout.close()
snip_info_dict = {}
snip_data_dict = {} # collect across slices for each SNP
for sdx, slice_num in enumerate(slice_nums2): # for each slice, open and process slice file
slice_coord_dict = {} # collect SNP for each slice_num, coord_num and combine across chromosomes
print("processing chrom,slice", chdx, chromosome, slice_num)
fname = "slice{}_result.txt".format(slice_num)
fpath = os.path.join(chr_folder, fname)
if not os.path.isfile(fpath):
print("slice data not found: ", slice_num, fpath)
continue
counter = 0
with open(fpath, 'r') as fin:
for l in fin:
counter+=1
l_toks = l.rstrip().split(",")
if counter==1:
header = l_toks
else:
cur_dict = dict(zip(header, l_toks)) # line dict
pos = cur_dict["POS"]
snipID_raw = cur_dict["ID"]
snipID = safe_snipID(snipID_raw)
snip_index = counter-1 # track which subfolder for netlify
if snipID not in snip_info_dict:
snip_info_dict[snipID] = {"SNP":snipID_raw, "POS":pos, "chr":chromosome, "A1":cur_dict["A1"]}
for cdx, coord_num in enumerate(COORD_NUMS):
coord_beta_key = "{}_{}_BETA".format(slice_num, coord_num)
coord_pval_key = "{}_{}_P".format(slice_num, coord_num)
if coord_beta_key in cur_dict and coord_pval_key in cur_dict:
coord_beta = cur_dict[coord_beta_key]
coord_pval = cur_dict[coord_pval_key]
is_bonf = float(coord_pval) < SIG_THRESHOLD_STRICT
vals = [snipID_raw, slice_num, coord_num, coord_beta, coord_pval, is_bonf]
if snipID not in snip_data_dict:
snip_data_dict[snipID] = []
snip_data_dict[snipID].append(vals)
if coord_num not in slice_coord_dict:
slice_coord_dict[coord_num] = []
slice_coord_dict[coord_num].append(vals)
# end for across COORD_NUMS
# end if
# end readline
fin.close()
# location files
loc_file = os.path.join(out_folder, "slice{}.txt".format(slice_num))
with open(loc_file, 'w') as fout_loc:
fout_loc.write("slice_num,x,y,beta,pval\n")
for coord_num, coord_data in slice_coord_dict.items():
for d in coord_data:
vals = d
fout_loc.write("{}\n".format(",".join([str(x) for x in vals ])))
fout_loc.close()
print("processed chrom,slice", chdx, chromosome, slice_num)
# out_sub_folder = os.path.join(out_folder, "{}".format(int(snip_index // 25000)))
out_sub_folder = out_folder
if not os.path.isdir(out_sub_folder):
os.makedirs(out_sub_folder)
# SNP files
for snipID_raw, snipData in snip_data_dict.items():
snipID = safe_snipID(snipID_raw)
snipFile = os.path.join(out_sub_folder, "{}.csv".format(snipID))
with open(snipFile, "w") as fout_snp:
fout_snp.write("snip,slice_num,coord_num,beta,pval,is_bonf\n")
for d in snipData:
fout_snp.write("{}\n".format(",".join([str(x) for x in d ])))
fout_snp.close()
print("processed chrom", chdx, chromosome)
# output snip_info_dict
snip_info_file = os.path.join(out_folder, "snip_info.csv".format(snipID))
with open(snip_info_file, "w") as fout_info:
fout_info.write("snip,snip_raw,chr,pos,A1\n")
for snipID, info_data in snip_info_dict.items():
vals = [snipID, info_data["SNP"], info_data["chr"], info_data["POS"], info_data["A1"]]
fout_info.write("{}\n".format(",".join([str(x) for x in vals ])))
fout_info.close()
return
def convert_to_flat_csv(chr_num): # generates summary file for chromosome and snip_info
chr_long_folder = os.path.join(DATA_FOLDER, "chr{}".format(str(chr_num)), "long")
chr_path = os.path.join(chr_long_folder, "chr_snip.json")
chr_snip_dict = json.loads(open(chr_path).read())
snip_keys = list(chr_snip_dict.keys())
num_keys = len(snip_keys)
keys_num_sig_loc = [(key, len(item[KEY_SIG_COORDS])) for key, item in chr_snip_dict.items()]
keys_num_sig_loc_sorted = sorted(keys_num_sig_loc, key=lambda x: x[1], reverse=True)
# # sanity check
# print(chr_snip_dict[keys_num_sig_loc_sorted[0][0]])
# print(list(set(chr_snip_dict[keys_num_sig_loc_sorted[0][0]][KEY_SIG_COORDS])))
# # how many really significant
# num_significant_bonf = []
# for x in keys_num_sig_loc_sorted:
# if x[1]>0:
# num_significant_bonf.append(x)
csv_path = chr_path.replace(".json", ".csv")
with open(csv_path, "w") as fout:
fout.write("snip,chr,num_sig,num_sig_strict\n") # header
for key_count in keys_num_sig_loc_sorted:
snipID, num_sig = key_count
vals = [snipID, chr_num, num_sig,
len(chr_snip_dict[snipID][KEY_SIG_COORDS_STRICT] if KEY_SIG_COORDS_STRICT in chr_snip_dict[snipID] else [])]
fout.write("{}\n".format(",".join([str(x) for x in vals])))
fout.close()
snip_info_json_path = os.path.join(chr_long_folder, "snip_info.json")
snip_info_csv_path = snip_info_json_path.replace(".json", ".csv")
snip_info_dict = json.loads(open(snip_info_json_path).read())
with open(snip_info_csv_path, "w") as fout:
fout.write("snip,chr,pos,A1\n") # header
for snipID, snipData in snip_info_dict.items():
vals = [snipID, snipData['chrom'], snipData['POS'], snipData['A1']]
fout.write("{}\n".format(",".join([str(x) for x in vals])))
fout.close()
return
def summary_by_location():
loc_summary_folder = os.path.join(DATA_FOLDER, "location_summary")
if not os.path.isdir(loc_summary_folder):
os.makedirs(loc_summary_folder)
# for chdx, chr_num in enumerate(CHROMOSOMES):
for chdx, chr_num in enumerate([22]):
chr_long_folder = os.path.join(DATA_FOLDER, "chr{}".format(str(chr_num)), "long")
chr_path = os.path.join(chr_long_folder, "chr_snip.json")
if not os.path.isfile(chr_path):
print("missing chromosome summary file:", chr_num, chr_path)
continue
chr_snip_dict = json.loads(open(chr_path).read())
# snip_keys = list(chr_snip_dict.keys())
# num_keys = len(snip_keys)
snip_info_path = os.path.join(chr_long_folder, "snip_info.json")
snip_info_dict = json.loads(open(snip_info_path).read())
keys_num_sig_loc = [(key, len(item[KEY_SIG_COORDS])) for key, item in chr_snip_dict.items()]
keys_num_sig_loc_sorted = sorted(keys_num_sig_loc, key=lambda x: x[1], reverse=True)
snips_significant_bonf = []
for x in keys_num_sig_loc_sorted:
if x[1]>0:
snips_significant_bonf.append(x[0])
for snipID_raw in snips_significant_bonf:
snipID = safe_snipID(snipID_raw)
snip_path = os.path.join(chr_long_folder, "{}_{}.txt".format(chr_num, snipID))
if not os.path.isfile(snip_path):
print("missing snip file:", chr_num, snip_path)
continue
snip_dict = parse_snip_file(snip_path)
for sdx, slice_num in enumerate(SLICE_NUMS):
for cdx, coord_num in enumerate(COORD_NUMS):
summary_file_path = os.path.join(loc_summary_folder, "location_summary_{}_{}.csv".format(slice_num, coord_num))
if not os.path.isfile(summary_file_path):
with open(summary_file_path, "w") as fout:
fout.write("snip,chr,pos,beta,pvalue\n")
fout.close()
xy = "{},{}".format(slice_num, coord_num)
if xy not in snip_dict:
print("no data for coordinate:", slice_num, coord_num, snipID)
continue
cur_data = snip_dict[xy]
snipPOS = snip_info_dict[snipID]["POS"]
with open(summary_file_path, "a") as fout:
if cur_data[1] < SIG_THRESHOLD:
vals = [snipID, chr_num, snipPOS] +cur_data
fout.write("{}\n".format(",".join([str(x) for x in vals])))
fout.close()
return
def parse_snip_file(snip_path):
snip_dict = {}
counter = 0
with open(snip_path, "r") as fin:
for l in fin:
counter+=1
l_toks = l.rstrip().split(",")
if counter==1:
header = l_toks
else:
cur_dict = dict(zip(header, l_toks))
x = cur_dict["x"]
y = cur_dict["y"]
xy = "{},{}".format(x,y)
snip_dict[xy] = [float(cur_dict["beta"]), float(cur_dict["pval"])]
fin.close()
return snip_dict
if __name__ == "__main__":
# for parallel
import sys
print(sys.argv)
if len(sys.argv) > 1:
chr_num = sys.argv[1]
else: # default values
chr_num = 22
chromomsomes = [chr_num]
# process_chromosome(chromomsomes)
process_chromosome_memory(chromomsomes)
#process_chromosome_memory(CHROMOSOMES[:-1])
# convert_to_flat_csv(chr_num)
# summary_by_location()