# 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 json import gzip import cPickle as pickle import itertools 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 wqflask.my_pylmm.pyLMM import chunks 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): if not dataset_type: dataset_type = Dataset_Getter(dataset_name) #dataset_type = get_dataset_type_from_json(dataset_name) print("dataset_type is:", dataset_type) #query = """ # SELECT DBType.Name # FROM DBList, DBType # WHERE DBList.Name = '{}' and # DBType.Id = DBList.DBTypeId # """.format(escape(dataset_name)) #dataset_type = g.db.execute(query).fetchone().Name dataset_ob = DS_NAME_MAP[dataset_type] dataset_class = globals()[dataset_ob] return dataset_class(dataset_name) #def get_dataset_type_from_json(dataset_name): 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]: print("dataset is:", dataset) short_dataset_name = dataset[0] 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() # #print("Running at startup:", get_dataset_type_from_json("HBTRC-MLPFC_0611")) 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(os.path.join(webqtlConfig.NEWGENODIR + name + '.json')) self.markers = json.load(json_data_fh) def add_pvalues(self, p_values): #print("length of self.markers:", len(self.markers)) #print("length of p_values:", len(p_values)) # 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): marker['p_value'] = p_value if 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 class HumanMarkers(Markers): def __init__(self, name): marker_data_fh = open(os.path.join(webqtlConfig.PYLMM_PATH + name + '.bim')) self.markers = [] for line in marker_data_fh: splat = line.strip().split() marker = {} marker['chr'] = int(splat[0]) marker['name'] = splat[1] marker['Mb'] = float(splat[3]) / 1000000 self.markers.append(marker) def add_pvalues(self, p_values): #for marker, p_value in itertools.izip(self.markers, p_values): # if marker['Mb'] <= 0 and marker['chr'] == 0: # continue # marker['p_value'] = p_value # print("p_value is:", marker['p_value']) # 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 super(HumanMarkers, self).add_pvalues(p_values) with Bench("deleting markers"): markers = [] for marker in self.markers: if not marker['Mb'] <= 0 and not marker['chr'] == 0: markers.append(marker) self.markers = markers 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""" 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 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 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:v4:" + 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") try: self.samplelist = get_group_samplelists.get_samplelist(self.name + ".geno") except IOError: self.samplelist = None print("after get_samplelist") Redis.set(key, json.dumps(self.samplelist)) Redis.expire(key, 60*5) def read_genotype_file(self): '''Read genotype from .geno file instead of database''' #if self.group == 'BXD300': # self.group = 'BXD' # #assert self.group, "self.group needs to be set" #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(os.path.join(webqtlConfig.GENODIR, self.name + '.geno')) if os.path.isfile(full_filename): print("Reading file: ", full_filename) genotype_1.read(full_filename) print("File read") else: try: full_filename = str(os.path.join(webqtlConfig.TMPDIR, self.name + '.geno')) #print("Reading file") genotype_1.read(full_filename) #print("File read") except IOError: print("File doesn't exist!") 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) #class DataSets(object): # """Builds a list of DataSets""" # # def __init__(self): # self.datasets = list() # #query = """SELECT Name FROM ProbeSetFreeze # UNION # SELECT Name From PublishFreeze # UNION # SELECT Name From GenoFreeze""" # #for result in g.db.execute(query).fetchall(): # dataset = DataSet(result.Name) # self.datasets.append(dataset) #ds = DataSets() #print("[orange] ds:", ds.datasets) 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): assert name, "Need a name" self.name = name self.id = None self.shortname = None self.fullname = None self.type = None self.setup() self.check_confidentiality() self.retrieve_other_names() self.group = DatasetGroup(self) # sets self.group and self.group_id and gets genotype 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 #@staticmethod #def get_by_trait_id(trait_id): # """Gets the dataset object given the trait id""" # # # # name = g.db.execute(""" SELECT # # """) # # return DataSet(name) # Delete this eventually @property def riset(): Weve_Renamed_This_As_Group #@property #def group(self): # if not self._group: # self.get_group() # # return self._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. """ query_args = tuple(escape(x) for x in ( (self.type + "Freeze"), str(webqtlConfig.PUBLICTHRESH), self.name, self.name, self.name)) print("query_args are:", query_args) #print(""" # SELECT Id, Name, FullName, ShortName # FROM %s # WHERE public > %s AND # (Name = '%s' OR FullName = '%s' OR ShortName = '%s') # """ % (query_args)) try: 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): self.samplelist = self.group.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): #XZ, 09/24/2008: build one temporary table that only contains the records associated with the input GeneId #tempTable = None #if GeneId and db.type == "ProbeSet": # if method == "3": # tempTable = self.getTempLiteratureTable(species=species, # input_species_geneid=GeneId, # returnNumber=returnNumber) # # if method == "4" or method == "5": # tempTable = self.getTempTissueCorrTable(primaryTraitSymbol=GeneSymbol, # TissueProbeSetFreezeId=tissueProbeSetFreezeId, # method=method, # returnNumber=returnNumber) 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.PublicationId = {}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)) 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)) results = g.db.execute(query).fetchall() 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 = ['', 'ID', 'Description', 'Authors', 'Year', 'Max LRS', 'Max LRS Location'] 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: 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 this_trait.description_display = description if not this_trait.year.isdigit(): this_trait.pubmed_text = "N/A" 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() #result = self.cursor.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: %.4f Mb' % (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 = %d AND PublishData.StrainId = Strain.Id Order BY Strain.Name """ % (trait, self.id) results = g.db.execute(query).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 = ['', '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: %.4f' % (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 """ % (webqtlDatabaseFunction.retrieve_species_id(self.group.name), trait, self.name) results = g.db.execute(query).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 = ['', 'ID', 'Symbol', 'Description', 'Location', 'Mean Expr', 'Max LRS', 'Max LRS Location'] # 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() #print("After get_trait_list query") trait_data = {} for trait in results: print("Retrieving sample_data for ", trait[0]) trait_data[trait[0]] = self.retrieve_sample_data(trait[0]) #print("After retrieve_sample_data") return trait_data #def get_trait_data(self): # self.samplelist = self.group.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): # # #XZ, 09/24/2008: build one temporary table that only contains the records associated with the input GeneId # #tempTable = None # #if GeneId and db.type == "ProbeSet": # # if method == "3": # # tempTable = self.getTempLiteratureTable(species=species, # # input_species_geneid=GeneId, # # returnNumber=returnNumber) # # # # if method == "4" or method == "5": # # tempTable = self.getTempTissueCorrTable(primaryTraitSymbol=GeneSymbol, # # TissueProbeSetFreezeId=tissueProbeSetFreezeId, # # method=method, # # returnNumber=returnNumber) # # temp = ['T%s.value' % item for item in sample_ids_step] # query = "SELECT {}.Name,".format(escape(self.type)) # data_start_pos = 1 # query += string.join(temp, ', ') # query += ' FROM ({}, {}XRef, {}Freeze) '.format(*mescape(self.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)) # # 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, self.type, self.type, self.type, self.type)) # results = g.db.execute(query).fetchall() # 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:]) 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 = str(this_trait.description).strip() target_string = str(this_trait.probe_target_description).strip() 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: %.4f Mb' % (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 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: #if result[0] and result[1]: # lrs_chr = result[0] # lrs_mb = result[1] 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) #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 = '%3.1f' % this_trait.lrs this_trait.LRS_score_value = this_trait.lrs this_trait.LRS_location_repr = 'Chr %s: %.4f Mb' % (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() 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() print("in retrieve_genes results {}: {}".format(type(results), results)) return dict(results) #return {item[0]: item[1] for item in results} #symbol_dict = {} #for item in results: # symbol_dict[item[0]] = item[1] #return symbol_dict #def retrieve_gene_symbols(self): # query = """ # select ProbeSet.Name, ProbeSet.Symbol, ProbeSet.GeneId # from ProbeSet,ProbeSetXRef # where ProbeSetXRef.ProbeSetFreezeId = %s and # ProbeSetXRef.ProbeSetId=ProbeSet.Id; # """ % (self.id) # results = g.db.execute(query).fetchall() # symbol_dict = {} # for item in results: # symbol_dict[item[0]] = item[1] # return symbol_dict # #def retrieve_gene_ids(self): # query = """ # select ProbeSet.Name, ProbeSet.GeneId # from ProbeSet,ProbeSetXRef # where ProbeSetXRef.ProbeSetFreezeId = %s and # ProbeSetXRef.ProbeSetId=ProbeSet.Id; # """ % (self.id) # return process_and_run_query(query) # results = g.db.execute(query).fetchall() # symbol_dict = {} # for item in results: # symbol_dict[item[0]] = item[1] # return symbol_dict 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