# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License
# as published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero General Public License for more details.
#
# This program is available from Source Forge: at GeneNetwork Project
# (sourceforge.net/projects/genenetwork/).
#
# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
#
# This module is used by GeneNetwork project (www.genenetwork.org)
from __future__ import absolute_import, print_function, division
import os
import math
import string
import collections
import codecs
import json
import gzip
import cPickle as pickle
import itertools
from operator import itemgetter
from redis import Redis
Redis = Redis()
from flask import Flask, g
import reaper
from base import webqtlConfig
from base import species
from dbFunction import webqtlDatabaseFunction
from utility import webqtlUtil
from utility.benchmark import Bench
from utility import chunks
from utility.tools import locate, locate_ignore_error
from maintenance import get_group_samplelists
from MySQLdb import escape_string as escape
from pprint import pformat as pf
# Used by create_database to instantiate objects
# Each subclass will add to this
DS_NAME_MAP = {}
def create_dataset(dataset_name, dataset_type = None, get_samplelist = True):
if not dataset_type:
dataset_type = Dataset_Getter(dataset_name)
print("dataset_type is:", dataset_type)
dataset_ob = DS_NAME_MAP[dataset_type]
dataset_class = globals()[dataset_ob]
return dataset_class(dataset_name, get_samplelist)
class Dataset_Types(object):
def __init__(self):
self.datasets = {}
file_name = "wqflask/static/new/javascript/dataset_menu_structure.json"
with open(file_name, 'r') as fh:
data = json.load(fh)
print("*" * 70)
for species in data['datasets']:
for group in data['datasets'][species]:
for dataset_type in data['datasets'][species][group]:
for dataset in data['datasets'][species][group][dataset_type]:
short_dataset_name = dataset[1]
if dataset_type == "Phenotypes":
new_type = "Publish"
elif dataset_type == "Genotypes":
new_type = "Geno"
else:
new_type = "ProbeSet"
self.datasets[short_dataset_name] = new_type
def __call__(self, name):
return self.datasets[name]
# Do the intensive work at startup one time only
Dataset_Getter = Dataset_Types()
def create_datasets_list():
key = "all_datasets"
result = Redis.get(key)
if result:
print("Cache hit!!!")
datasets = pickle.loads(result)
else:
datasets = list()
with Bench("Creating DataSets object"):
type_dict = {'Publish': 'PublishFreeze',
'ProbeSet': 'ProbeSetFreeze',
'Geno': 'GenoFreeze'}
for dataset_type in type_dict:
query = "SELECT Name FROM {}".format(type_dict[dataset_type])
for result in g.db.execute(query).fetchall():
#The query at the beginning of this function isn't necessary here, but still would
#rather just reuse it
#print("type: {}\tname: {}".format(dataset_type, result.Name))
dataset = create_dataset(result.Name, dataset_type)
datasets.append(dataset)
Redis.set(key, pickle.dumps(datasets, pickle.HIGHEST_PROTOCOL))
Redis.expire(key, 60*60)
return datasets
def create_in_clause(items):
"""Create an in clause for mysql"""
in_clause = ', '.join("'{}'".format(x) for x in mescape(*items))
in_clause = '( {} )'.format(in_clause)
return in_clause
def mescape(*items):
"""Multiple escape"""
escaped = [escape(str(item)) for item in items]
#print("escaped is:", escaped)
return escaped
class Markers(object):
"""Todo: Build in cacheing so it saves us reading the same file more than once"""
def __init__(self, name):
json_data_fh = open(locate(name + '.json','genotype/json'))
try:
markers = json.load(json_data_fh)
except:
markers = []
for marker in markers:
if (marker['chr'] != "X") and (marker['chr'] != "Y"):
marker['chr'] = int(marker['chr'])
marker['Mb'] = float(marker['Mb'])
self.markers = markers
#print("self.markers:", self.markers)
def add_pvalues(self, p_values):
print("length of self.markers:", len(self.markers))
print("length of p_values:", len(p_values))
if type(p_values) is list:
# THIS IS only needed for the case when we are limiting the number of p-values calculated
#if len(self.markers) > len(p_values):
# self.markers = self.markers[:len(p_values)]
for marker, p_value in itertools.izip(self.markers, p_values):
if not p_value:
continue
marker['p_value'] = float(p_value)
if math.isnan(marker['p_value']) or marker['p_value'] <= 0:
marker['lod_score'] = 0
marker['lrs_value'] = 0
else:
marker['lod_score'] = -math.log10(marker['p_value'])
#Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values
marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61
elif type(p_values) is dict:
filtered_markers = []
for marker in self.markers:
#print("marker[name]", marker['name'])
#print("p_values:", p_values)
if marker['name'] in p_values:
#print("marker {} IS in p_values".format(i))
marker['p_value'] = p_values[marker['name']]
if math.isnan(marker['p_value']) or (marker['p_value'] <= 0):
marker['lod_score'] = 0
marker['lrs_value'] = 0
else:
marker['lod_score'] = -math.log10(marker['p_value'])
#Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values
marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61
filtered_markers.append(marker)
#else:
#print("marker {} NOT in p_values".format(i))
#self.markers.remove(marker)
#del self.markers[i]
self.markers = filtered_markers
class HumanMarkers(Markers):
def __init__(self, name, specified_markers = []):
marker_data_fh = open(locate('genotype') + '/' + name + '.bim')
self.markers = []
for line in marker_data_fh:
splat = line.strip().split()
#print("splat:", splat)
if len(specified_markers) > 0:
if splat[1] in specified_markers:
marker = {}
marker['chr'] = int(splat[0])
marker['name'] = splat[1]
marker['Mb'] = float(splat[3]) / 1000000
else:
continue
else:
marker = {}
marker['chr'] = int(splat[0])
marker['name'] = splat[1]
marker['Mb'] = float(splat[3]) / 1000000
self.markers.append(marker)
#print("markers is: ", pf(self.markers))
def add_pvalues(self, p_values):
super(HumanMarkers, self).add_pvalues(p_values)
class DatasetGroup(object):
"""
Each group has multiple datasets; each species has multiple groups.
For example, Mouse has multiple groups (BXD, BXA, etc), and each group
has multiple datasets associated with it.
"""
def __init__(self, dataset):
"""This sets self.group and self.group_id"""
#print("DATASET NAME2:", dataset.name)
self.name, self.id = g.db.execute(dataset.query_for_group).fetchone()
if self.name == 'BXD300':
self.name = "BXD"
self.f1list = None
self.parlist = None
self.get_f1_parent_strains()
#print("parents/f1s: {}:{}".format(self.parlist, self.f1list))
self.species = webqtlDatabaseFunction.retrieve_species(self.name)
self.incparentsf1 = False
self.allsamples = None
self._datasets = None
def get_specified_markers(self, markers = []):
self.markers = HumanMarkers(self.name, markers)
def get_markers(self):
#print("self.species is:", self.species)
if self.species == "human":
marker_class = HumanMarkers
else:
marker_class = Markers
self.markers = marker_class(self.name)
def datasets(self):
key = "group_dataset_menu:v2:" + self.name
print("key is2:", key)
dataset_menu = []
print("[tape4] webqtlConfig.PUBLICTHRESH:", webqtlConfig.PUBLICTHRESH)
print("[tape4] type webqtlConfig.PUBLICTHRESH:", type(webqtlConfig.PUBLICTHRESH))
results = g.db.execute('''
(SELECT '#PublishFreeze',PublishFreeze.FullName,PublishFreeze.Name
FROM PublishFreeze,InbredSet
WHERE PublishFreeze.InbredSetId = InbredSet.Id
and InbredSet.Name = %s
and PublishFreeze.public > %s)
UNION
(SELECT '#GenoFreeze',GenoFreeze.FullName,GenoFreeze.Name
FROM GenoFreeze, InbredSet
WHERE GenoFreeze.InbredSetId = InbredSet.Id
and InbredSet.Name = %s
and GenoFreeze.public > %s)
UNION
(SELECT Tissue.Name, ProbeSetFreeze.FullName,ProbeSetFreeze.Name
FROM ProbeSetFreeze, ProbeFreeze, InbredSet, Tissue
WHERE ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id
and ProbeFreeze.TissueId = Tissue.Id
and ProbeFreeze.InbredSetId = InbredSet.Id
and InbredSet.Name like %s
and ProbeSetFreeze.public > %s
ORDER BY Tissue.Name, ProbeSetFreeze.CreateTime desc, ProbeSetFreeze.AvgId)
''', (self.name, webqtlConfig.PUBLICTHRESH,
self.name, webqtlConfig.PUBLICTHRESH,
"%" + self.name + "%", webqtlConfig.PUBLICTHRESH))
the_results = results.fetchall()
#for tissue_name, dataset in itertools.groupby(the_results, itemgetter(0)):
for dataset_item in the_results:
tissue_name = dataset_item[0]
dataset = dataset_item[1]
dataset_short = dataset_item[2]
if tissue_name in ['#PublishFreeze', '#GenoFreeze']:
dataset_menu.append(dict(tissue=None, datasets=[(dataset, dataset_short)]))
else:
dataset_sub_menu = [item[1:] for item in dataset]
tissue_already_exists = False
tissue_position = None
for i, tissue_dict in enumerate(dataset_menu):
if tissue_dict['tissue'] == tissue_name:
tissue_already_exists = True
tissue_position = i
break
if tissue_already_exists:
#print("dataset_menu:", dataset_menu[i]['datasets'])
dataset_menu[i]['datasets'].append((dataset, dataset_short))
else:
dataset_menu.append(dict(tissue=tissue_name,
datasets=[(dataset, dataset_short)]))
Redis.set(key, pickle.dumps(dataset_menu, pickle.HIGHEST_PROTOCOL))
Redis.expire(key, 60*5)
self._datasets = dataset_menu
return self._datasets
def get_f1_parent_strains(self):
try:
# NL, 07/27/2010. ParInfo has been moved from webqtlForm.py to webqtlUtil.py;
f1, f12, maternal, paternal = webqtlUtil.ParInfo[self.name]
except KeyError:
f1 = f12 = maternal = paternal = None
if f1 and f12:
self.f1list = [f1, f12]
if maternal and paternal:
self.parlist = [maternal, paternal]
def get_samplelist(self):
key = "samplelist:v2:" + self.name
#print("key is:", key)
#with Bench("Loading cache"):
result = Redis.get(key)
if result:
#print("Sample List Cache hit!!!")
#print("Before unjsonifying {}: {}".format(type(result), result))
self.samplelist = json.loads(result)
#print(" type: ", type(self.samplelist))
#print(" self.samplelist: ", self.samplelist)
else:
print("Cache not hit")
genotype_fn = locate_ignore_error(self.name+".geno",'genotype')
mapping_fn = locate_ignore_error(self.name+".fam",'mapping')
if mapping_fn:
self.samplelist = get_group_samplelists.get_samplelist("plink", mapping_fn)
elif genotype_fn:
self.samplelist = get_group_samplelists.get_samplelist("geno", genotype_fn)
else:
self.samplelist = None
print("Sample list: ",self.samplelist)
Redis.set(key, json.dumps(self.samplelist))
Redis.expire(key, 60*5)
def all_samples_ordered(self):
result = []
lists = (self.parlist, self.f1list, self.samplelist)
[result.extend(l) for l in lists if l]
return result
def read_genotype_file(self):
'''Read genotype from .geno file instead of database'''
#genotype_1 is Dataset Object without parents and f1
#genotype_2 is Dataset Object with parents and f1 (not for intercross)
genotype_1 = reaper.Dataset()
# reaper barfs on unicode filenames, so here we ensure it's a string
full_filename = str(locate(self.name+'.geno','genotype'))
genotype_1.read(full_filename)
if genotype_1.type == "group" and self.parlist:
genotype_2 = genotype_1.add(Mat=self.parlist[0], Pat=self.parlist[1]) #, F1=_f1)
else:
genotype_2 = genotype_1
#determine default genotype object
if self.incparentsf1 and genotype_1.type != "intercross":
genotype = genotype_2
else:
self.incparentsf1 = 0
genotype = genotype_1
self.samplelist = list(genotype.prgy)
return genotype
class DataSet(object):
"""
DataSet class defines a dataset in webqtl, can be either Microarray,
Published phenotype, genotype, or user input dataset(temp)
"""
def __init__(self, name, get_samplelist = True):
assert name, "Need a name"
self.name = name
self.id = None
self.shortname = None
self.fullname = None
self.type = None
self.data_scale = None #ZS: For example log2
self.setup()
self.check_confidentiality()
self.retrieve_other_names()
self.group = DatasetGroup(self) # sets self.group and self.group_id and gets genotype
if get_samplelist == True:
self.group.get_samplelist()
self.species = species.TheSpecies(self)
def get_desc(self):
"""Gets overridden later, at least for Temp...used by trait's get_given_name"""
return None
# Delete this eventually
@property
def riset():
Weve_Renamed_This_As_Group
def retrieve_other_names(self):
"""
If the data set name parameter is not found in the 'Name' field of the data set table,
check if it is actually the FullName or ShortName instead.
This is not meant to retrieve the data set info if no name at all is passed.
"""
try:
if self.type == "ProbeSet":
query_args = tuple(escape(x) for x in (
str(webqtlConfig.PUBLICTHRESH),
self.name,
self.name,
self.name))
self.id, self.name, self.fullname, self.shortname, self.data_scale, self.tissue = g.db.execute("""
SELECT ProbeSetFreeze.Id, ProbeSetFreeze.Name, ProbeSetFreeze.FullName, ProbeSetFreeze.ShortName, ProbeSetFreeze.DataScale, Tissue.Name
FROM ProbeSetFreeze, ProbeFreeze, Tissue
WHERE ProbeSetFreeze.public > %s AND
ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id AND
ProbeFreeze.TissueId = Tissue.Id AND
(ProbeSetFreeze.Name = '%s' OR ProbeSetFreeze.FullName = '%s' OR ProbeSetFreeze.ShortName = '%s')
""" % (query_args)).fetchone()
else:
query_args = tuple(escape(x) for x in (
(self.type + "Freeze"),
str(webqtlConfig.PUBLICTHRESH),
self.name,
self.name,
self.name))
self.tissue = "N/A"
self.id, self.name, self.fullname, self.shortname = g.db.execute("""
SELECT Id, Name, FullName, ShortName
FROM %s
WHERE public > %s AND
(Name = '%s' OR FullName = '%s' OR ShortName = '%s')
""" % (query_args)).fetchone()
except TypeError:
print("Dataset {} is not yet available in GeneNetwork.".format(self.name))
pass
def get_trait_data(self, sample_list=None):
if sample_list:
self.samplelist = sample_list
else:
self.samplelist = self.group.samplelist
if self.group.parlist != None and self.group.f1list != None:
if (self.group.parlist + self.group.f1list) in self.samplelist:
self.samplelist += self.group.parlist + self.group.f1list
query = """
SELECT Strain.Name, Strain.Id FROM Strain, Species
WHERE Strain.Name IN {}
and Strain.SpeciesId=Species.Id
and Species.name = '{}'
""".format(create_in_clause(self.samplelist), *mescape(self.group.species))
results = dict(g.db.execute(query).fetchall())
sample_ids = [results[item] for item in self.samplelist]
# MySQL limits the number of tables that can be used in a join to 61,
# so we break the sample ids into smaller chunks
# Postgres doesn't have that limit, so we can get rid of this after we transition
chunk_size = 50
number_chunks = int(math.ceil(len(sample_ids) / chunk_size))
trait_sample_data = []
for sample_ids_step in chunks.divide_into_chunks(sample_ids, number_chunks):
if self.type == "Publish":
dataset_type = "Phenotype"
else:
dataset_type = self.type
temp = ['T%s.value' % item for item in sample_ids_step]
if self.type == "Publish":
query = "SELECT {}XRef.Id,".format(escape(self.type))
else:
query = "SELECT {}.Name,".format(escape(dataset_type))
data_start_pos = 1
query += string.join(temp, ', ')
query += ' FROM ({}, {}XRef, {}Freeze) '.format(*mescape(dataset_type,
self.type,
self.type))
for item in sample_ids_step:
query += """
left join {}Data as T{} on T{}.Id = {}XRef.DataId
and T{}.StrainId={}\n
""".format(*mescape(self.type, item, item, self.type, item, item))
if self.type == "Publish":
query += """
WHERE {}XRef.InbredSetId = {}Freeze.InbredSetId
and {}Freeze.Name = '{}'
and {}.Id = {}XRef.{}Id
order by {}.Id
""".format(*mescape(self.type, self.type, self.type, self.name,
dataset_type, self.type, dataset_type, dataset_type))
else:
query += """
WHERE {}XRef.{}FreezeId = {}Freeze.Id
and {}Freeze.Name = '{}'
and {}.Id = {}XRef.{}Id
order by {}.Id
""".format(*mescape(self.type, self.type, self.type, self.type,
self.name, dataset_type, self.type, self.type, dataset_type))
#print("trait data query: ", query)
results = g.db.execute(query).fetchall()
#print("query results:", results)
trait_sample_data.append(results)
trait_count = len(trait_sample_data[0])
self.trait_data = collections.defaultdict(list)
# put all of the separate data together into a dictionary where the keys are
# trait names and values are lists of sample values
for trait_counter in range(trait_count):
trait_name = trait_sample_data[0][trait_counter][0]
for chunk_counter in range(int(number_chunks)):
self.trait_data[trait_name] += (
trait_sample_data[chunk_counter][trait_counter][data_start_pos:])
class PhenotypeDataSet(DataSet):
DS_NAME_MAP['Publish'] = 'PhenotypeDataSet'
def setup(self):
#print("IS A PHENOTYPEDATASET")
# Fields in the database table
self.search_fields = ['Phenotype.Post_publication_description',
'Phenotype.Pre_publication_description',
'Phenotype.Pre_publication_abbreviation',
'Phenotype.Post_publication_abbreviation',
'Phenotype.Lab_code',
'Publication.PubMed_ID',
'Publication.Abstract',
'Publication.Title',
'Publication.Authors',
'PublishXRef.Id']
# Figure out what display_fields is
self.display_fields = ['name',
'pubmed_id',
'pre_publication_description',
'post_publication_description',
'original_description',
'pre_publication_abbreviation',
'post_publication_abbreviation',
'lab_code',
'submitter', 'owner',
'authorized_users',
'authors', 'title',
'abstract', 'journal',
'volume', 'pages',
'month', 'year',
'sequence', 'units', 'comments']
# Fields displayed in the search results table header
self.header_fields = ['Index',
'Record',
'Description',
'Authors',
'Year',
'Max LRS',
'Max LRS Location',
'Additive Effect']
self.type = 'Publish'
self.query_for_group = '''
SELECT
InbredSet.Name, InbredSet.Id
FROM
InbredSet, PublishFreeze
WHERE
PublishFreeze.InbredSetId = InbredSet.Id AND
PublishFreeze.Name = "%s"
''' % escape(self.name)
def check_confidentiality(self):
# (Urgently?) Need to write this
pass
def get_trait_list(self):
query = """
select PublishXRef.Id
from PublishXRef, PublishFreeze
where PublishFreeze.InbredSetId=PublishXRef.InbredSetId
and PublishFreeze.Id = {}
""".format(escape(str(self.id)))
results = g.db.execute(query).fetchall()
trait_data = {}
for trait in results:
trait_data[trait[0]] = self.retrieve_sample_data(trait[0])
return trait_data
def get_trait_info(self, trait_list, species = ''):
for this_trait in trait_list:
if not this_trait.haveinfo:
this_trait.retrieve_info(get_qtl_info=True)
description = this_trait.post_publication_description
#If the dataset is confidential and the user has access to confidential
#phenotype traits, then display the pre-publication description instead
#of the post-publication description
if this_trait.confidential:
this_trait.description_display = ""
continue # for now
if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(
privilege=self.privilege,
userName=self.userName,
authorized_users=this_trait.authorized_users):
description = this_trait.pre_publication_description
if len(description) > 0:
this_trait.description_display = description.strip()
else:
this_trait.description_display = ""
if not this_trait.year.isdigit():
this_trait.pubmed_text = "N/A"
else:
this_trait.pubmed_text = this_trait.year
if this_trait.pubmed_id:
this_trait.pubmed_link = webqtlConfig.PUBMEDLINK_URL % this_trait.pubmed_id
#LRS and its location
this_trait.LRS_score_repr = "N/A"
this_trait.LRS_score_value = 0
this_trait.LRS_location_repr = "N/A"
this_trait.LRS_location_value = 1000000
if this_trait.lrs:
result = g.db.execute("""
select Geno.Chr, Geno.Mb from Geno, Species
where Species.Name = %s and
Geno.Name = %s and
Geno.SpeciesId = Species.Id
""", (species, this_trait.locus)).fetchone()
if result:
if result[0] and result[1]:
LRS_Chr = result[0]
LRS_Mb = result[1]
#XZ: LRS_location_value is used for sorting
try:
LRS_location_value = int(LRS_Chr)*1000 + float(LRS_Mb)
except:
if LRS_Chr.upper() == 'X':
LRS_location_value = 20*1000 + float(LRS_Mb)
else:
LRS_location_value = ord(str(LRS_chr).upper()[0])*1000 + float(LRS_Mb)
this_trait.LRS_score_repr = LRS_score_repr = '%3.1f' % this_trait.lrs
this_trait.LRS_score_value = LRS_score_value = this_trait.lrs
this_trait.LRS_location_repr = LRS_location_repr = 'Chr%s: %.6f' % (LRS_Chr, float(LRS_Mb))
def retrieve_sample_data(self, trait):
query = """
SELECT
Strain.Name, PublishData.value, PublishSE.error, NStrain.count
FROM
(PublishData, Strain, PublishXRef, PublishFreeze)
left join PublishSE on
(PublishSE.DataId = PublishData.Id AND PublishSE.StrainId = PublishData.StrainId)
left join NStrain on
(NStrain.DataId = PublishData.Id AND
NStrain.StrainId = PublishData.StrainId)
WHERE
PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND
PublishData.Id = PublishXRef.DataId AND PublishXRef.Id = %s AND
PublishFreeze.Id = %s AND PublishData.StrainId = Strain.Id
Order BY
Strain.Name
"""
results = g.db.execute(query, (trait, self.id)).fetchall()
return results
class GenotypeDataSet(DataSet):
DS_NAME_MAP['Geno'] = 'GenotypeDataSet'
def setup(self):
# Fields in the database table
self.search_fields = ['Name',
'Chr']
# Find out what display_fields is
self.display_fields = ['name',
'chr',
'mb',
'source2',
'sequence']
# Fields displayed in the search results table header
self.header_fields = ['Index',
'ID',
'Location']
# Todo: Obsolete or rename this field
self.type = 'Geno'
self.query_for_group = '''
SELECT
InbredSet.Name, InbredSet.Id
FROM
InbredSet, GenoFreeze
WHERE
GenoFreeze.InbredSetId = InbredSet.Id AND
GenoFreeze.Name = "%s"
''' % escape(self.name)
def check_confidentiality(self):
return geno_mrna_confidentiality(self)
def get_trait_list(self):
query = """
select Geno.Name
from Geno, GenoXRef
where GenoXRef.GenoId = Geno.Id
and GenoFreezeId = {}
""".format(escape(str(self.id)))
results = g.db.execute(query).fetchall()
trait_data = {}
for trait in results:
trait_data[trait[0]] = self.retrieve_sample_data(trait[0])
return trait_data
def get_trait_info(self, trait_list, species=None):
for this_trait in trait_list:
if not this_trait.haveinfo:
this_trait.retrieveInfo()
#XZ: trait_location_value is used for sorting
trait_location_repr = 'N/A'
trait_location_value = 1000000
if this_trait.chr and this_trait.mb:
try:
trait_location_value = int(this_trait.chr)*1000 + this_trait.mb
except:
if this_trait.chr.upper() == 'X':
trait_location_value = 20*1000 + this_trait.mb
else:
trait_location_value = ord(str(this_trait.chr).upper()[0])*1000 + this_trait.mb
this_trait.location_repr = 'Chr%s: %.6f' % (this_trait.chr, float(this_trait.mb) )
this_trait.location_value = trait_location_value
def retrieve_sample_data(self, trait):
query = """
SELECT
Strain.Name, GenoData.value, GenoSE.error, GenoData.Id
FROM
(GenoData, GenoFreeze, Strain, Geno, GenoXRef)
left join GenoSE on
(GenoSE.DataId = GenoData.Id AND GenoSE.StrainId = GenoData.StrainId)
WHERE
Geno.SpeciesId = %s AND Geno.Name = %s AND GenoXRef.GenoId = Geno.Id AND
GenoXRef.GenoFreezeId = GenoFreeze.Id AND
GenoFreeze.Name = %s AND
GenoXRef.DataId = GenoData.Id AND
GenoData.StrainId = Strain.Id
Order BY
Strain.Name
"""
results = g.db.execute(query,
(webqtlDatabaseFunction.retrieve_species_id(self.group.name),
trait, self.name)).fetchall()
return results
class MrnaAssayDataSet(DataSet):
'''
An mRNA Assay is a quantitative assessment (assay) associated with an mRNA trait
This used to be called ProbeSet, but that term only refers specifically to the Affymetrix
platform and is far too specific.
'''
DS_NAME_MAP['ProbeSet'] = 'MrnaAssayDataSet'
def setup(self):
# Fields in the database table
self.search_fields = ['Name',
'Description',
'Probe_Target_Description',
'Symbol',
'Alias',
'GenbankId',
'UniGeneId',
'RefSeq_TranscriptId']
# Find out what display_fields is
self.display_fields = ['name', 'symbol',
'description', 'probe_target_description',
'chr', 'mb',
'alias', 'geneid',
'genbankid', 'unigeneid',
'omim', 'refseq_transcriptid',
'blatseq', 'targetseq',
'chipid', 'comments',
'strand_probe', 'strand_gene',
'probe_set_target_region',
'probe_set_specificity',
'probe_set_blat_score',
'probe_set_blat_mb_start',
'probe_set_blat_mb_end',
'probe_set_strand',
'probe_set_note_by_rw',
'flag']
# Fields displayed in the search results table header
self.header_fields = ['Index',
'Record',
'Symbol',
'Description',
'Location',
'Mean',
'Max LRS',
'Max LRS Location',
'Additive Effect']
# Todo: Obsolete or rename this field
self.type = 'ProbeSet'
self.query_for_group = '''
SELECT
InbredSet.Name, InbredSet.Id
FROM
InbredSet, ProbeSetFreeze, ProbeFreeze
WHERE
ProbeFreeze.InbredSetId = InbredSet.Id AND
ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId AND
ProbeSetFreeze.Name = "%s"
''' % escape(self.name)
def check_confidentiality(self):
return geno_mrna_confidentiality(self)
def get_trait_list_1(self):
query = """
select ProbeSet.Name
from ProbeSet, ProbeSetXRef
where ProbeSetXRef.ProbeSetId = ProbeSet.Id
and ProbeSetFreezeId = {}
""".format(escape(str(self.id)))
results = g.db.execute(query).fetchall()
trait_data = {}
for trait in results:
trait_data[trait[0]] = self.retrieve_sample_data(trait[0])
return trait_data
def get_trait_info(self, trait_list=None, species=''):
# Note: setting trait_list to [] is probably not a great idea.
if not trait_list:
trait_list = []
for this_trait in trait_list:
if not this_trait.haveinfo:
this_trait.retrieveInfo(QTL=1)
if not this_trait.symbol:
this_trait.symbol = "N/A"
#XZ, 12/08/2008: description
#XZ, 06/05/2009: Rob asked to add probe target description
description_string = unicode(str(this_trait.description).strip(codecs.BOM_UTF8), 'utf-8')
target_string = unicode(str(this_trait.probe_target_description).strip(codecs.BOM_UTF8), 'utf-8')
if len(description_string) > 1 and description_string != 'None':
description_display = description_string
else:
description_display = this_trait.symbol
if (len(description_display) > 1 and description_display != 'N/A' and
len(target_string) > 1 and target_string != 'None'):
description_display = description_display + '; ' + target_string.strip()
# Save it for the jinja2 template
this_trait.description_display = description_display
#XZ: trait_location_value is used for sorting
trait_location_repr = 'N/A'
trait_location_value = 1000000
if this_trait.chr and this_trait.mb:
#Checks if the chromosome number can be cast to an int (i.e. isn't "X" or "Y")
#This is so we can convert the location to a number used for sorting
trait_location_value = self.convert_location_to_value(this_trait.chr, this_trait.mb)
#try:
# trait_location_value = int(this_trait.chr)*1000 + this_trait.mb
#except ValueError:
# if this_trait.chr.upper() == 'X':
# trait_location_value = 20*1000 + this_trait.mb
# else:
# trait_location_value = (ord(str(this_trait.chr).upper()[0])*1000 +
# this_trait.mb)
#ZS: Put this in function currently called "convert_location_to_value"
this_trait.location_repr = 'Chr%s: %.6f' % (this_trait.chr,
float(this_trait.mb))
this_trait.location_value = trait_location_value
#Get mean expression value
query = (
"""select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet
where ProbeSetXRef.ProbeSetFreezeId = %s and
ProbeSet.Id = ProbeSetXRef.ProbeSetId and
ProbeSet.Name = '%s'
""" % (escape(str(this_trait.dataset.id)),
escape(this_trait.name)))
#print("query is:", pf(query))
result = g.db.execute(query).fetchone()
mean = result[0] if result else 0
if mean:
this_trait.mean = "%2.3f" % mean
#LRS and its location
this_trait.LRS_score_repr = 'N/A'
this_trait.LRS_score_value = 0
this_trait.LRS_location_repr = 'N/A'
this_trait.LRS_location_value = 1000000
#Max LRS and its Locus location
if this_trait.lrs and this_trait.locus:
query = """
select Geno.Chr, Geno.Mb from Geno, Species
where Species.Name = '{}' and
Geno.Name = '{}' and
Geno.SpeciesId = Species.Id
""".format(species, this_trait.locus)
result = g.db.execute(query).fetchone()
if result:
lrs_chr, lrs_mb = result
#XZ: LRS_location_value is used for sorting
lrs_location_value = self.convert_location_to_value(lrs_chr, lrs_mb)
this_trait.LRS_score_repr = '%3.1f' % this_trait.lrs
this_trait.LRS_score_value = this_trait.lrs
this_trait.LRS_location_repr = 'Chr%s: %.6f' % (lrs_chr, float(lrs_mb))
def convert_location_to_value(self, chromosome, mb):
try:
location_value = int(chromosome)*1000 + float(mb)
except ValueError:
if chromosome.upper() == 'X':
location_value = 20*1000 + float(mb)
else:
location_value = (ord(str(chromosome).upper()[0])*1000 +
float(mb))
return location_value
def get_sequence(self):
query = """
SELECT
ProbeSet.BlatSeq
FROM
ProbeSet, ProbeSetFreeze, ProbeSetXRef
WHERE
ProbeSet.Id=ProbeSetXRef.ProbeSetId and
ProbeSetFreeze.Id = ProbeSetXRef.ProbSetFreezeId and
ProbeSet.Name = %s
ProbeSetFreeze.Name = %s
""" % (escape(self.name), escape(self.dataset.name))
results = g.db.execute(query).fetchone()
return results[0]
def retrieve_sample_data(self, trait):
query = """
SELECT
Strain.Name, ProbeSetData.value, ProbeSetSE.error, ProbeSetData.Id
FROM
(ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef)
left join ProbeSetSE on
(ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId)
WHERE
ProbeSet.Name = '%s' AND ProbeSetXRef.ProbeSetId = ProbeSet.Id AND
ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND
ProbeSetFreeze.Name = '%s' AND
ProbeSetXRef.DataId = ProbeSetData.Id AND
ProbeSetData.StrainId = Strain.Id
Order BY
Strain.Name
""" % (escape(trait), escape(self.name))
results = g.db.execute(query).fetchall()
#print("RETRIEVED RESULTS HERE:", results)
return results
def retrieve_genes(self, column_name):
query = """
select ProbeSet.Name, ProbeSet.%s
from ProbeSet,ProbeSetXRef
where ProbeSetXRef.ProbeSetFreezeId = %s and
ProbeSetXRef.ProbeSetId=ProbeSet.Id;
""" % (column_name, escape(str(self.id)))
results = g.db.execute(query).fetchall()
return dict(results)
class TempDataSet(DataSet):
'''Temporary user-generated data set'''
def setup(self):
self.search_fields = ['name',
'description']
self.display_fields = ['name',
'description']
self.header_fields = ['Name',
'Description']
self.type = 'Temp'
# Need to double check later how these are used
self.id = 1
self.fullname = 'Temporary Storage'
self.shortname = 'Temp'
@staticmethod
def handle_pca(desc):
if 'PCA' in desc:
# Todo: Modernize below lines
desc = desc[desc.rindex(':')+1:].strip()
else:
desc = desc[:desc.index('entered')].strip()
return desc
def get_desc(self):
g.db.execute('SELECT description FROM Temp WHERE Name=%s', self.name)
desc = g.db.fetchone()[0]
desc = self.handle_pca(desc)
return desc
def get_group(self):
self.cursor.execute("""
SELECT
InbredSet.Name, InbredSet.Id
FROM
InbredSet, Temp
WHERE
Temp.InbredSetId = InbredSet.Id AND
Temp.Name = "%s"
""", self.name)
self.group, self.group_id = self.cursor.fetchone()
#return self.group
def retrieve_sample_data(self, trait):
query = """
SELECT
Strain.Name, TempData.value, TempData.SE, TempData.NStrain, TempData.Id
FROM
TempData, Temp, Strain
WHERE
TempData.StrainId = Strain.Id AND
TempData.Id = Temp.DataId AND
Temp.name = '%s'
Order BY
Strain.Name
""" % escape(trait.name)
results = g.db.execute(query).fetchall()
def geno_mrna_confidentiality(ob):
dataset_table = ob.type + "Freeze"
#print("dataset_table [%s]: %s" % (type(dataset_table), dataset_table))
query = '''SELECT Id, Name, FullName, confidentiality,
AuthorisedUsers FROM %s WHERE Name = %%s''' % (dataset_table)
result = g.db.execute(query, ob.name)
(dataset_id,
name,
full_name,
confidential,
authorized_users) = result.fetchall()[0]
if confidential:
return True
