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Diffstat (limited to 'wqflask/maintenance/quantile_normalize.py')
| -rw-r--r-- | wqflask/maintenance/quantile_normalize.py | 129 |
1 files changed, 129 insertions, 0 deletions
diff --git a/wqflask/maintenance/quantile_normalize.py b/wqflask/maintenance/quantile_normalize.py new file mode 100644 index 00000000..41a3aad8 --- /dev/null +++ b/wqflask/maintenance/quantile_normalize.py @@ -0,0 +1,129 @@ +from __future__ import absolute_import, print_function, division + +import sys +sys.path.insert(0,'./') + +from itertools import izip + +import MySQLdb +import urlparse + +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 = urlparse.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=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(izip(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)
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