import json
from bin_sample_composition import bin_reads
import glob
import os
from ncbi_taxonomy_utils import ncbi_taxonomy
ncbi = ncbi_taxonomy()
import gzip
import pandas as pd
results_dir = '/data/analysis_group1/idbd_rnd/results/191104_NB551543_0156_AHTLL7AFXY'
batch_file_paths = glob.glob(os.path.join(results_dir, 'batch', '*'))
results = {}
for batch_file in batch_file_paths:
with open(batch_file) as input:
batch_obj = json.load(input)
for lib in batch_obj['libraries']:
seq_sple = lib['seqSple']
accession = lib['bioSple']
print(accession)
total_reads = 0
for batch_lib in batch_obj['batch']['readsDist']['DNA']:
if batch_lib['bioSple'] == accession:
total_reads = batch_lib['postQualityReads']
print(total_reads)
if total_reads == 0:
continue
summary_paths = lib['diagnosticOutput']
composition_path = glob.glob(
os.path.join(results_dir, 'tax',
seq_sple + '*sample_composition.out'))
def bin_reads(sample_composition_path,
ncbi_class=None,
quantification='relative',
ctrl_taxids=None):
with open(sample_composition_path) as file:
reader = csv.DictReader(file,
delimiter='\t',
fieldnames=['taxid', 'count'])
taxid_counts = []
for entry in reader:
taxid_counts.append(entry)
if ncbi_class is None:
ncbi = ncbi_taxonomy()
else:
ncbi = ncbi_class
human_taxid = 9606
bacteria_taxid = 2
virus_taxid = 10239
eukaryota_taxid = 2759
parasite_taxids = {
7563, 188941, 6029, 5653, 6935, 6178, 5794, 6308, 31277, 119088, 6199,
85819, 33083, 33084, 75966, 41165, 7509, 6236, 198624, 33634, 5988,
6249, 5738, 1489900, 740972, 1485168, 37104, 10232
}
fungus_taxid = 4751
human_counts = []
bacteria_counts = []
virus_counts = []
ctrl_counts = []
parasite_counts = []
fungus_counts = []
unclassified_counts = []
for taxid in taxid_counts:
taxid_int = int(taxid['taxid'])
taxid_path = ncbi.get_path(taxid_int)
count_value = taxid['count']
if isinstance(count_value, int):
count = count_value
elif isinstance(count_value, str):
if len(count_value) > 0:
count = np.int64(taxid['count'].split('.')[0])
else:
continue
if taxid_int in ctrl_taxids:
ctrl_counts.append(count)
continue
if human_taxid in taxid_path:
human_counts.append(count)
elif fungus_taxid in taxid_path:
fungus_counts.append(count)
elif parasite_taxids.intersection(set(taxid_path)) != set():
parasite_counts.append(count)
elif bacteria_taxid in taxid_path:
bacteria_counts.append(count)
elif virus_taxid in taxid_path:
virus_counts.append(count)
else:
unclassified_counts.append(count)
human_sum = np.array(human_counts).sum()
bacteria_sum = np.array(bacteria_counts).sum()
virus_sum = np.array(virus_counts).sum()
parasite_sum = np.array(parasite_counts).sum()
fungus_sum = np.array(fungus_counts).sum()
unclassified_sum = np.array(unclassified_counts).sum()
ctrl_sum = np.array(ctrl_counts).sum()
cumalitve_sum = human_sum + bacteria_sum + virus_sum + parasite_sum + \
fungus_sum + unclassified_sum + ctrl_sum
if quantification == 'relative':
count_dict = {
"Human": 100 * human_sum / cumalitve_sum,
"Bacteria": 100 * bacteria_sum / cumalitve_sum,
"Virus": 100 * virus_sum / cumalitve_sum,
"Parasite": 100 * parasite_sum / cumalitve_sum,
"Fungus": 100 * fungus_sum / cumalitve_sum,
"Unclassified": 100 * unclassified_sum / cumalitve_sum,
"Controls": 100 * ctrl_sum / cumalitve_sum
}
elif quantification == 'absolute':
count_dict = {
"Human": human_sum,
"Bacteria": bacteria_sum,
"Virus": virus_sum,
"Parasite": parasite_sum,
"Fungus": fungus_sum,
"Unclassified": unclassified_sum,
"Controls": ctrl_sum
}
elif quantification == 'both':
count_dict = {
"Human": {
"relative": 100 * human_sum / cumalitve_sum,
"absolute": human_sum
},
"Bacteria": {
"relative": 100 * bacteria_sum / cumalitve_sum,
"absolute": bacteria_sum
},
"Virus": {
"relative": 100 * virus_sum / cumalitve_sum,
"absolute": virus_sum
},
"Parasite": {
"relative": 100 * parasite_sum / cumalitve_sum,
"absolute": parasite_sum
},
"Fungus": {
"relative": 100 * fungus_sum / cumalitve_sum,
"absolute": fungus_sum
},
"Unclassified": {
"relative": 100 * unclassified_sum / cumalitve_sum,
"absolute": unclassified_sum
},
"Controls": {
"relative": 100 * ctrl_sum / cumalitve_sum,
"absolute": ctrl_sum
}
}
return count_dict