1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
|
import sys
sys.path.insert(0, './')
import MySQLdb
import urllib.parse
import numpy as np
import pandas as pd
from elasticsearch import Elasticsearch, TransportError
from elasticsearch.helpers import bulk
from flask import Flask, g, request
from wqflask import app
from utility.elasticsearch_tools import get_elasticsearch_connection
from utility.tools import ELASTICSEARCH_HOST, ELASTICSEARCH_PORT, SQL_URI
def parse_db_uri():
"""Converts a database URI to the db name, host name, user name, and password"""
parsed_uri = urllib.parse.urlparse(SQL_URI)
db_conn_info = dict(
db=parsed_uri.path[1:],
host=parsed_uri.hostname,
user=parsed_uri.username,
passwd=parsed_uri.password)
print(db_conn_info)
return db_conn_info
def create_dataframe(input_file):
with open(input_file) as f:
ncols = len(f.readline().split("\t"))
input_array = np.loadtxt(open(
input_file, "rb"), delimiter="\t", skiprows=1, usecols=list(range(1, ncols)))
return pd.DataFrame(input_array)
# This function taken from https://github.com/ShawnLYU/Quantile_Normalize
def quantileNormalize(df_input):
df = df_input.copy()
# compute rank
dic = {}
for col in df:
dic.update({col: sorted(df[col])})
sorted_df = pd.DataFrame(dic)
rank = sorted_df.mean(axis=1).tolist()
# sort
for col in df:
t = np.searchsorted(np.sort(df[col]), df[col])
df[col] = [rank[i] for i in t]
return df
def set_data(dataset_name):
orig_file = "/home/zas1024/cfw_data/" + dataset_name + ".txt"
sample_list = []
with open(orig_file, 'r') as orig_fh, open('/home/zas1024/cfw_data/quant_norm.csv', 'r') as quant_fh:
for i, (line1, line2) in enumerate(zip(orig_fh, quant_fh)):
trait_dict = {}
sample_list = []
if i == 0:
sample_names = line1.split('\t')[1:]
else:
trait_name = line1.split('\t')[0]
for i, sample in enumerate(sample_names):
this_sample = {
"name": sample,
"value": line1.split('\t')[i + 1],
"qnorm": line2.split('\t')[i + 1]
}
sample_list.append(this_sample)
query = """SELECT Species.SpeciesName, InbredSet.InbredSetName, ProbeSetFreeze.FullName
FROM Species, InbredSet, ProbeSetFreeze, ProbeFreeze, ProbeSetXRef, ProbeSet
WHERE Species.Id = InbredSet.SpeciesId and
InbredSet.Id = ProbeFreeze.InbredSetId and
ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId and
ProbeSetFreeze.Name = '%s' and
ProbeSetFreeze.Id = ProbeSetXRef.ProbeSetFreezeId and
ProbeSetXRef.ProbeSetId = ProbeSet.Id and
ProbeSet.Name = '%s'""" % (dataset_name, line1.split('\t')[0])
Cursor.execute(query)
result_info = Cursor.fetchone()
yield {
"_index": "traits",
"_type": "trait",
"_source": {
"name": trait_name,
"species": result_info[0],
"group": result_info[1],
"dataset": dataset_name,
"dataset_fullname": result_info[2],
"samples": sample_list,
"transform_types": "qnorm"
}
}
if __name__ == '__main__':
Conn = MySQLdb.Connect(**parse_db_uri())
Cursor = Conn.cursor()
# es = Elasticsearch([{
# "host": ELASTICSEARCH_HOST, "port": ELASTICSEARCH_PORT
# }], timeout=60) if (ELASTICSEARCH_HOST and ELASTICSEARCH_PORT) else None
es = get_elasticsearch_connection(for_user=False)
#input_filename = "/home/zas1024/cfw_data/" + sys.argv[1] + ".txt"
#input_df = create_dataframe(input_filename)
#output_df = quantileNormalize(input_df)
#output_df.to_csv('quant_norm.csv', sep='\t')
#out_filename = sys.argv[1][:-4] + '_quantnorm.txt'
success, _ = bulk(es, set_data(sys.argv[1]))
response = es.search(
index="traits", doc_type="trait", body = {
"query": {"match": {"name": "ENSMUSG00000028982"}}
}
)
print(response)
|
