diff options
45 files changed, 2 insertions, 8031 deletions
diff --git a/default_settings.py b/default_settings.py deleted file mode 100644 index 9cdc665..0000000 --- a/default_settings.py +++ /dev/null @@ -1,18 +0,0 @@ -"""module contains default settings for genenetwork""" -import os - - -USE_REDIS = True - -GN2_BASE_URL = "https://genenetwork.org/" - - -HOME = os.environ['HOME'] - -# SQL_URI = "mysql://gn2:mysql_password@localhost/db_webqtl_s" - -SQL_URI = os.environ.get("SQL_URI","mysql+pymysql://kabui:1234@localhost/db_webqtl") - -SECRET_HMAC_CODE = '\x08\xdf\xfa\x93N\x80\xd9\\H@\\\x9f`\x98d^\xb4a;\xc6OM\x946a\xbc\xfc\x80:*\xebc' - -GENENETWORK_FILES = os.environ.get("GENENETWORK_FILES",HOME+"/data/genotype_files") diff --git a/docs/correlation.md b/docs/correlation.md deleted file mode 100644 index bd1b278..0000000 --- a/docs/correlation.md +++ /dev/null @@ -1,42 +0,0 @@ -### endpoint for correlation endpoint - -- The endpoint for correlation is -```python - - /api/correlation/compute/corr_compute -``` - - -**To be noted before spinning the server for correlation computation\which can be set for example env -SQL_URI=mysql://user:password@localhost/db_webqtl and also to GENENETWORK_FILES default is HOME+"/data/genotype_files** - -(required input data *should be in json format*) -- "primary_samples": "", -- "trait_id" -- "dataset" -- "sample_vals" -- "corr_type" -- "corr_dataset" -- "corr_return_results" -- "corr_samples_group" -- "corr_sample_method" -- "min_expr" -- "location_type" -- "loc_chr" -- "min_loc_mb" -- "max_loc_mb" -- "p_range_lower" -- "p_range_upper" - -- example - -```bash -curl -X POST -H "Content-Type: application/json" \ - -d '{"primary_samles":"",trait_id:"","dataset":"","sample_vals":"","corr_type":"",corr_sample_group:"",corr_sample_method:""}' \ - localhost:5000/api/correlation/correlation_compute - - ``` - - -- output data is correlation_json - diff --git a/gn3/api/correlation.py b/gn3/api/correlation.py index 56b8381..53ea6a7 100644 --- a/gn3/api/correlation.py +++ b/gn3/api/correlation.py @@ -60,4 +60,4 @@ def compute_tissue_corr(corr_method="pearson"): target_tissues_dict_list=target_tissues_dict_list, corr_method=corr_method) - return jsonify(results)
\ No newline at end of file + return jsonify(results) diff --git a/gn3/base/__init__.py b/gn3/base/__init__.py deleted file mode 100644 index e69de29..0000000 --- a/gn3/base/__init__.py +++ /dev/null diff --git a/gn3/base/data_set.py b/gn3/base/data_set.py deleted file mode 100644 index 01913f2..0000000 --- a/gn3/base/data_set.py +++ /dev/null @@ -1,882 +0,0 @@ - -import json -import math -import collections -import requests -from redis import Redis -from flask import g -from gn3.utility.db_tools import escape -from gn3.utility.db_tools import mescape -from gn3.utility.db_tools import create_in_clause -from gn3.utility.tools import locate_ignore_error -from gn3.db.calls import fetch1 -from gn3.db.calls import fetchone -from gn3.db.webqtlDatabaseFunction import retrieve_species -from gn3.utility import chunks - -from gn3.utility import get_group_samplelists -from gn3.base.species import TheSpecies -r = Redis() - -# should probably move this to its own configuration files - -USE_REDIS = True - -# todo move to config file -GN2_BASE_URL = "https://genenetwork.org/" - -DS_NAME_MAP = {} - -# pylint: disable-all -#todo file not linted -# pylint: disable=C0103 - - - -def create_dataset(dataset_name, dataset_type=None, get_samplelist=True, group_name=None): - - if dataset_name == "Temp": - dataset_type = "Temp" - - if dataset_type is None: - dataset_type = Dataset_Getter(dataset_name) - dataset_ob = DS_NAME_MAP[dataset_type] - dataset_class = globals()[dataset_ob] - - if dataset_type == "Temp": - results = dataset_class(dataset_name, get_samplelist, group_name) - - else: - results = dataset_class(dataset_name, get_samplelist) - - return results - - -class DatasetType: - def __init__(self, redis_instance): - self.redis_instance = redis_instance - self.datasets = {} - - data = self.redis_instance.get("dataset_structure") - if data: - self.datasets = json.loads(data) - - else: - - try: - - data = json.loads(requests.get( - GN2_BASE_URL + "/api/v_pre1/gen_dropdown", timeout=5).content) - - # todo:Refactor code below n^4 loop - - 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 - - except Exception as e: - raise e - - self.redis_instance.set( - "dataset_structure", json.dumps(self.datasets)) - - def set_dataset_key(self, t, name): - """If name is not in the object's dataset dictionary, set it, and update - dataset_structure in Redis - - args: - t: Type of dataset structure which can be: 'mrna_expr', 'pheno', - 'other_pheno', 'geno' - name: The name of the key to inserted in the datasets dictionary - - """ - - sql_query_mapping = { - 'mrna_expr': ("""SELECT ProbeSetFreeze.Id FROM """ + - """ProbeSetFreeze WHERE ProbeSetFreeze.Name = "{}" """), - 'pheno': ("""SELECT InfoFiles.GN_AccesionId """ + - """FROM InfoFiles, PublishFreeze, InbredSet """ + - """WHERE InbredSet.Name = '{}' AND """ + - """PublishFreeze.InbredSetId = InbredSet.Id AND """ + - """InfoFiles.InfoPageName = PublishFreeze.Name"""), - 'other_pheno': ("""SELECT PublishFreeze.Name """ + - """FROM PublishFreeze, InbredSet """ + - """WHERE InbredSet.Name = '{}' AND """ + - """PublishFreeze.InbredSetId = InbredSet.Id"""), - 'geno': ("""SELECT GenoFreeze.Id FROM GenoFreeze WHERE """ + - """GenoFreeze.Name = "{}" """) - } - - dataset_name_mapping = { - "mrna_expr": "ProbeSet", - "pheno": "Publish", - "other_pheno": "Publish", - "geno": "Geno", - } - - group_name = name - if t in ['pheno', 'other_pheno']: - group_name = name.replace("Publish", "") - - results = g.db.execute( - sql_query_mapping[t].format(group_name)).fetchone() - if results: - self.datasets[name] = dataset_name_mapping[t] - self.redis_instance.set( - "dataset_structure", json.dumps(self.datasets)) - - return True - - return None - - def __call__(self, name): - if name not in self.datasets: - for t in ["mrna_expr", "pheno", "other_pheno", "geno"]: - - if(self.set_dataset_key(t, name)): - # This has side-effects, with the end result being a truth-y value - break - - return self.datasets.get(name, None) - - -# Do the intensive work at startup one time only -# could replace the code below -Dataset_Getter = DatasetType(r) - - -class DatasetGroup: - """ - 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, name=None): - """This sets self.group and self.group_id""" - if name == None: - self.name, self.id, self.genetic_type = fetchone( - dataset.query_for_group) - - else: - self.name, self.id, self.genetic_type = fetchone( - "SELECT InbredSet.Name, InbredSet.Id, InbredSet.GeneticType FROM InbredSet where Name='%s'" % name) - - if self.name == 'BXD300': - self.name = "BXD" - - self.f1list = None - - self.parlist = None - - self.get_f1_parent_strains() - - # remove below not used in correlation - - self.mapping_id, self.mapping_names = self.get_mapping_methods() - - self.species = retrieve_species(self.name) - - def get_f1_parent_strains(self): - try: - # should import ParInfo - raise e - # NL, 07/27/2010. ParInfo has been moved from webqtlForm.py to webqtlUtil.py; - f1, f12, maternal, paternal = webqtlUtil.ParInfo[self.name] - except Exception as e: - f1 = f12 = maternal = paternal = None - - if f1 and f12: - self.f1list = [f1, f12] - - if maternal and paternal: - self.parlist = [maternal, paternal] - - def get_mapping_methods(self): - mapping_id = g.db.execute( - "select MappingMethodId from InbredSet where Name= '%s'" % self.name).fetchone()[0] - - if mapping_id == "1": - mapping_names = ["GEMMA", "QTLReaper", "R/qtl"] - elif mapping_id == "2": - mapping_names = ["GEMMA"] - - elif mapping_id == "3": - mapping_names = ["R/qtl"] - - elif mapping_id == "4": - mapping_names = ["GEMMA", "PLINK"] - - else: - mapping_names = [] - - return mapping_id, mapping_names - - def get_samplelist(self): - result = None - key = "samplelist:v3:" + self.name - if USE_REDIS: - result = r.get(key) - - if result is not None: - - self.samplelist = json.loads(result) - - else: - # logger.debug("Cache not hit") - # should enable logger - genotype_fn = locate_ignore_error(self.name+".geno", 'genotype') - if genotype_fn: - self.samplelist = get_group_samplelists.get_samplelist( - "geno", genotype_fn) - - else: - self.samplelist = None - - if USE_REDIS: - r.set(key, json.dumps(self.samplelist)) - r.expire(key, 60*5) - - -class DataSet: - """ - 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, group_name=None): - - 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() - - if self.type == "Temp": # Need to supply group name as input if temp trait - # sets self.group and self.group_id and gets genotype - self.group = DatasetGroup(self, name=group_name) - else: - self.check_confidentiality() - self.retrieve_other_names() - # sets self.group and self.group_id and gets genotype - self.group = DatasetGroup(self) - self.accession_id = self.get_accession_id() - if get_samplelist == True: - self.group.get_samplelist() - self.species = TheSpecies(self) - - def get_desc(self): - """Gets overridden later, at least for Temp...used by trait's get_given_name""" - return None - - - def get_accession_id(self): - if self.type == "Publish": - results = g.db.execute("""select InfoFiles.GN_AccesionId from InfoFiles, PublishFreeze, InbredSet where - InbredSet.Name = %s and - PublishFreeze.InbredSetId = InbredSet.Id and - InfoFiles.InfoPageName = PublishFreeze.Name and - PublishFreeze.public > 0 and - PublishFreeze.confidentiality < 1 order by - PublishFreeze.CreateTime desc""", (self.group.name)).fetchone() - elif self.type == "Geno": - results = g.db.execute("""select InfoFiles.GN_AccesionId from InfoFiles, GenoFreeze, InbredSet where - InbredSet.Name = %s and - GenoFreeze.InbredSetId = InbredSet.Id and - InfoFiles.InfoPageName = GenoFreeze.ShortName and - GenoFreeze.public > 0 and - GenoFreeze.confidentiality < 1 order by - GenoFreeze.CreateTime desc""", (self.group.name)).fetchone() - else: - results = None - - if results != None: - return str(results[0]) - else: - return "None" - - def retrieve_other_names(self): - """This method fetches the the dataset names in search_result. - - 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 ( - self.name, - self.name, - self.name)) - - self.id, self.name, self.fullname, self.shortname, self.data_scale, self.tissue = fetch1(""" - SELECT ProbeSetFreeze.Id, ProbeSetFreeze.Name, ProbeSetFreeze.FullName, ProbeSetFreeze.ShortName, ProbeSetFreeze.DataScale, Tissue.Name - FROM ProbeSetFreeze, ProbeFreeze, Tissue - WHERE ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id - AND ProbeFreeze.TissueId = Tissue.Id - AND (ProbeSetFreeze.Name = '%s' OR ProbeSetFreeze.FullName = '%s' OR ProbeSetFreeze.ShortName = '%s') - """ % (query_args), "/dataset/"+self.name+".json", - lambda r: (r["id"], r["name"], r["full_name"], - r["short_name"], r["data_scale"], r["tissue"]) - ) - else: - query_args = tuple(escape(x) for x in ( - (self.type + "Freeze"), - self.name, - self.name, - self.name)) - - self.tissue = "N/A" - self.id, self.name, self.fullname, self.shortname = fetchone(""" - SELECT Id, Name, FullName, ShortName - FROM %s - WHERE (Name = '%s' OR FullName = '%s' OR ShortName = '%s') - """ % (query_args)) - - except TypeError as e: - logger.debug( - "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)) - # logger.sql(query) - 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 += ', '.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)) - - 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 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', - 'proteinid', 'uniprotid', - '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, InbredSet.GeneticType - 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_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( - str(this_trait.description).strip(codecs.BOM_UTF8), 'utf-8') - target_string = str( - 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 - - if this_trait.chr and this_trait.mb: - this_trait.location_repr = 'Chr%s: %.6f' % ( - this_trait.chr, float(this_trait.mb)) - - # 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))) - - #logger.debug("query is:", pf(query)) - logger.sql(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_location_repr = 'N/A' - - # 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) - logger.sql(query) - result = g.db.execute(query).fetchone() - - if result: - lrs_chr, lrs_mb = result - this_trait.LRS_score_repr = '%3.1f' % this_trait.lrs - this_trait.LRS_location_repr = 'Chr%s: %.6f' % ( - lrs_chr, float(lrs_mb)) - - return trait_list - - def retrieve_sample_data(self, trait): - query = """ - SELECT - Strain.Name, ProbeSetData.value, ProbeSetSE.error, NStrain.count, Strain.Name2 - FROM - (ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef) - left join ProbeSetSE on - (ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId) - left join NStrain on - (NStrain.DataId = ProbeSetData.Id AND - NStrain.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)) - # logger.sql(query) - results = g.db.execute(query).fetchall() - #logger.debug("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))) - # logger.sql(query) - results = g.db.execute(query).fetchall() - - return dict(results) - - -class TempDataSet(DataSet): - '''Temporary user-generated data set''' - - DS_NAME_MAP['Temp'] = 'TempDataSet' - - 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' - - -class PhenotypeDataSet(DataSet): - DS_NAME_MAP['Publish'] = 'PhenotypeDataSet' - - def setup(self): - - #logger.debug("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', - 'PublishXRef.mean', - 'Phenotype.Lab_code', - 'Publication.PubMed_ID', - 'Publication.Abstract', - 'Publication.Title', - 'Publication.Authors', - 'PublishXRef.Id'] - - # Figure out what display_fields is - self.display_fields = ['name', 'group_code', - 'pubmed_id', - 'pre_publication_description', - 'post_publication_description', - 'original_description', - 'pre_publication_abbreviation', - 'post_publication_abbreviation', - 'mean', - '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, InbredSet.GeneticType - 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_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 # todo for now, because no authorization features - - if not webqtlUtil.has_access_to_confidentail_phenotype_trait( - 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_location_repr = "N/A" - - if this_trait.lrs: - query = """ - 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) - - result = g.db.execute(query).fetchone() - - if result: - if result[0] and result[1]: - LRS_Chr = result[0] - LRS_Mb = result[1] - - this_trait.LRS_score_repr = LRS_score_repr = '%3.1f' % 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, Strain.Name2 - 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, InbredSet.GeneticType - 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_info(self, trait_list, species=None): - for this_trait in trait_list: - if not this_trait.haveinfo: - this_trait.retrieveInfo() - - if this_trait.chr and this_trait.mb: - this_trait.location_repr = 'Chr%s: %.6f' % ( - this_trait.chr, float(this_trait.mb)) - - def retrieve_sample_data(self, trait): - query = """ - SELECT - Strain.Name, GenoData.value, GenoSE.error, "N/A", Strain.Name2 - 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 - - -def geno_mrna_confidentiality(ob): - dataset_table = ob.type + "Freeze" - #logger.debug("dataset_table [%s]: %s" % (type(dataset_table), dataset_table)) - - query = '''SELECT Id, Name, FullName, confidentiality, - AuthorisedUsers FROM %s WHERE Name = "%s"''' % (dataset_table, ob.name) - # - result = g.db.execute(query) - - (_dataset_id, - _name, - _full_name, - confidential, - _authorized_users) = result.fetchall()[0] - - if confidential: - return True diff --git a/gn3/base/mrna_assay_tissue_data.py b/gn3/base/mrna_assay_tissue_data.py deleted file mode 100644 index 0f51ade..0000000 --- a/gn3/base/mrna_assay_tissue_data.py +++ /dev/null @@ -1,94 +0,0 @@ - -# pylint: disable-all -import collections - -from flask import g - -from gn3.utility.db_tools import create_in_clause -from gn3.utility.db_tools import escape -from gn3.utility.bunch import Bunch - - -# from utility.logger import getLogger -# logger = getLogger(__name__ ) - -class MrnaAssayTissueData(object): - - def __init__(self, gene_symbols=None): - self.gene_symbols = gene_symbols - if self.gene_symbols == None: - self.gene_symbols = [] - - self.data = collections.defaultdict(Bunch) - - query = '''select t.Symbol, t.GeneId, t.DataId, t.Chr, t.Mb, t.description, t.Probe_Target_Description - from ( - select Symbol, max(Mean) as maxmean - from TissueProbeSetXRef - where TissueProbeSetFreezeId=1 and ''' - - # Note that inner join is necessary in this query to get distinct record in one symbol group - # with highest mean value - # Due to the limit size of TissueProbeSetFreezeId table in DB, - # performance of inner join is acceptable.MrnaAssayTissueData(gene_symbols=symbol_list) - if len(gene_symbols) == 0: - query += '''Symbol!='' and Symbol Is Not Null group by Symbol) - as x inner join TissueProbeSetXRef as t on t.Symbol = x.Symbol - and t.Mean = x.maxmean; - ''' - else: - in_clause = create_in_clause(gene_symbols) - - # ZS: This was in the query, not sure why: http://docs.python.org/2/library/string.html?highlight=lower#string.lower - query += ''' Symbol in {} group by Symbol) - as x inner join TissueProbeSetXRef as t on t.Symbol = x.Symbol - and t.Mean = x.maxmean; - '''.format(in_clause) - - results = g.db.execute(query).fetchall() - - lower_symbols = [] - for gene_symbol in gene_symbols: - if gene_symbol != None: - lower_symbols.append(gene_symbol.lower()) - - for result in results: - symbol = result[0] - if symbol.lower() in lower_symbols: - symbol = symbol.lower() - - self.data[symbol].gene_id = result.GeneId - self.data[symbol].data_id = result.DataId - self.data[symbol].chr = result.Chr - self.data[symbol].mb = result.Mb - self.data[symbol].description = result.description - self.data[symbol].probe_target_description = result.Probe_Target_Description - - ########################################################################### - # Input: cursor, symbolList (list), dataIdDict(Dict) - # output: symbolValuepairDict (dictionary):one dictionary of Symbol and Value Pair, - # key is symbol, value is one list of expression values of one probeSet; - # function: get one dictionary whose key is gene symbol and value is tissue expression data (list type). - # Attention! All keys are lower case! - ########################################################################### - - def get_symbol_values_pairs(self): - id_list = [self.data[symbol].data_id for symbol in self.data] - - symbol_values_dict = {} - - if len(id_list) > 0: - query = """SELECT TissueProbeSetXRef.Symbol, TissueProbeSetData.value - FROM TissueProbeSetXRef, TissueProbeSetData - WHERE TissueProbeSetData.Id IN {} and - TissueProbeSetXRef.DataId = TissueProbeSetData.Id""".format(create_in_clause(id_list)) - - results = g.db.execute(query).fetchall() - for result in results: - if result.Symbol.lower() not in symbol_values_dict: - symbol_values_dict[result.Symbol.lower()] = [result.value] - else: - symbol_values_dict[result.Symbol.lower()].append( - result.value) - - return symbol_values_dict diff --git a/gn3/base/species.py b/gn3/base/species.py deleted file mode 100644 index 9fb08fb..0000000 --- a/gn3/base/species.py +++ /dev/null @@ -1,64 +0,0 @@ - -# pylint: disable-all -import collections -from flask import g -from dataclasses import dataclass - -class TheSpecies: - def __init__(self, dataset=None, species_name=None): - if species_name is not None: - self.name = species_name - - self.chromosomes = Chromosomes(species=self.name) - - else: - self.dataset = dataset - self.chromosomes = Chromosomes(dataset=self.dataset) - - -class Chromosomes: - def __init__(self, dataset=None, species=None): - self.chromosomes = collections.OrderedDict() - - if species is not None: - query = """ - Select - Chr_Length.Name, Chr_Length.OrderId, Length from Chr_Length, Species - where - Chr_Length.SpeciesId = Species.SpeciesId AND - Species.Name = '%s' - Order by OrderId - """ % species.capitalize() - - else: - self.dataset = dataset - - query = """ - Select - Chr_Length.Name, Chr_Length.OrderId, Length from Chr_Length, InbredSet - where - Chr_Length.SpeciesId = InbredSet.SpeciesId AND - InbredSet.Name = '%s' - Order by OrderId - """ % self.dataset.group.name - - # logger.sql(query) - - results = g.db.execute(query).fetchall() - - for item in results: - self.chromosomes[item.OrderId] = IndChromosome( - item.Name, item.Length) - - -# @dataclass -class IndChromosome: - def __init__(self,name,length): - self.name= name - self.length = length - - @property - def mb_length(self): - """Chromosome length in megabases""" - return self.length/ 1000000 - diff --git a/gn3/base/trait.py b/gn3/base/trait.py deleted file mode 100644 index f4be61c..0000000 --- a/gn3/base/trait.py +++ /dev/null @@ -1,366 +0,0 @@ - -# pylint: disable-all -from flask import g -from redis import Redis -from gn3.utility.db_tools import escape -from gn3.base.webqtlCaseData import webqtlCaseData - - -def check_resource_availability(dataset, name=None): - - # todo add code for this - # should probably work on this has to do with authentication - return {'data': ['no-access', 'view'], 'metadata': ['no-access', 'view'], 'admin': ['not-admin']} - - -def create_trait(**kw): - # work on check resource availability deals with authentication - assert bool(kw.get("dataset")) != bool( - kw.get('dataset_name')), "Needs dataset ob. or name" - - assert bool(kw.get("name")), "Need trait name" - - if kw.get('dataset_name'): - if kw.get('dataset_name') != "Temp": - dataset = create_dataset(kw.get('dataset_name')) - else: - dataset = kw.get('dataset') - - if dataset.type == 'Publish': - permissions = check_resource_availability( - dataset, kw.get('name')) - else: - permissions = check_resource_availability(dataset) - - if "view" in permissions['data']: - the_trait = GeneralTrait(**kw) - if the_trait.dataset.type != "Temp": - the_trait = retrieve_trait_info( - the_trait, - the_trait.dataset, - get_qtl_info=kw.get('get_qtl_info')) - - - return the_trait - - return None - - -class GeneralTrait: - def __init__(self, get_qtl_info=False, get_sample_info=True, **kw): - assert bool(kw.get('dataset')) != bool( - kw.get('dataset_name')), "Needs dataset ob. or name" - # Trait ID, ProbeSet ID, Published ID, etc. - self.name = kw.get('name') - if kw.get('dataset_name'): - if kw.get('dataset_name') == "Temp": - temp_group = self.name.split("_")[2] - self.dataset = create_dataset( - dataset_name="Temp", - dataset_type="Temp", - group_name=temp_group) - - else: - self.dataset = create_dataset(kw.get('dataset_name')) - - else: - self.dataset = kw.get("dataset") - - self.cellid = kw.get('cellid') - self.identification = kw.get('identification', 'un-named trait') - self.haveinfo = kw.get('haveinfo', False) - self.sequence = kw.get('sequence') - self.data = kw.get('data', {}) - self.view = True - - # Sets defaults - self.locus = None - self.lrs = None - self.pvalue = None - self.mean = None - self.additive = None - self.num_overlap = None - self.strand_probe = None - self.symbol = None - self.display_name = self.name - self.LRS_score_repr = "N/A" - self.LRS_location_repr = "N/A" - - if kw.get('fullname'): - name2 = value.split("::") - if len(name2) == 2: - self.dataset, self.name = name2 - - elif len(name2) == 3: - self.dataset, self.name, self.cellid = name2 - - # Todo: These two lines are necessary most of the time, but - # perhaps not all of the time So we could add a simple if - # statement to short-circuit this if necessary - if get_sample_info is not False: - self = retrieve_sample_data(self, self.dataset) - - -def retrieve_sample_data(trait, dataset, samplelist=None): - if samplelist is None: - samplelist = [] - - if dataset.type == "Temp": - results = Redis.get(trait.name).split() - - else: - results = dataset.retrieve_sample_data(trait.name) - - # Todo: is this necessary? If not remove - trait.data.clear() - - if results: - if dataset.type == "Temp": - all_samples_ordered = dataset.group.all_samples_ordered() - for i, item in enumerate(results): - try: - trait.data[all_samples_ordered[i]] = webqtlCaseData( - all_samples_ordered[i], float(item)) - - except Exception as e: - pass - - - else: - for item in results: - name, value, variance, num_cases, name2 = item - if not samplelist or (samplelist and name in samplelist): - trait.data[name] = webqtlCaseData(*item) - - return trait - -def retrieve_trait_info(trait, dataset, get_qtl_info=False): - assert dataset, "Dataset doesn't exist" - - the_url = None - # some code should be added added here - - try: - response = requests.get(the_url).content - trait_info = json.loads(response) - except: # ZS: I'm assuming the trait is viewable if the try fails for some reason; it should never reach this point unless the user has privileges, since that's dealt with in create_trait - if dataset.type == 'Publish': - query = """ - SELECT - PublishXRef.Id, InbredSet.InbredSetCode, Publication.PubMed_ID, - CAST(Phenotype.Pre_publication_description AS BINARY), - CAST(Phenotype.Post_publication_description AS BINARY), - CAST(Phenotype.Original_description AS BINARY), - CAST(Phenotype.Pre_publication_abbreviation AS BINARY), - CAST(Phenotype.Post_publication_abbreviation AS BINARY), PublishXRef.mean, - Phenotype.Lab_code, Phenotype.Submitter, Phenotype.Owner, Phenotype.Authorized_Users, - CAST(Publication.Authors AS BINARY), CAST(Publication.Title AS BINARY), CAST(Publication.Abstract AS BINARY), - CAST(Publication.Journal AS BINARY), Publication.Volume, Publication.Pages, - Publication.Month, Publication.Year, PublishXRef.Sequence, - Phenotype.Units, PublishXRef.comments - FROM - PublishXRef, Publication, Phenotype, PublishFreeze, InbredSet - WHERE - PublishXRef.Id = %s AND - Phenotype.Id = PublishXRef.PhenotypeId AND - Publication.Id = PublishXRef.PublicationId AND - PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND - PublishXRef.InbredSetId = InbredSet.Id AND - PublishFreeze.Id = %s - """ % (trait.name, dataset.id) - - trait_info = g.db.execute(query).fetchone() - - # XZ, 05/08/2009: Xiaodong add this block to use ProbeSet.Id to find the probeset instead of just using ProbeSet.Name - # XZ, 05/08/2009: to avoid the problem of same probeset name from different platforms. - elif dataset.type == 'ProbeSet': - display_fields_string = ', ProbeSet.'.join(dataset.display_fields) - display_fields_string = 'ProbeSet.' + display_fields_string - query = """ - SELECT %s - FROM ProbeSet, ProbeSetFreeze, ProbeSetXRef - WHERE - ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND - ProbeSetXRef.ProbeSetId = ProbeSet.Id AND - ProbeSetFreeze.Name = '%s' AND - ProbeSet.Name = '%s' - """ % (escape(display_fields_string), - escape(dataset.name), - escape(str(trait.name))) - - trait_info = g.db.execute(query).fetchone() - # XZ, 05/08/2009: We also should use Geno.Id to find marker instead of just using Geno.Name - # to avoid the problem of same marker name from different species. - elif dataset.type == 'Geno': - display_fields_string = ',Geno.'.join(dataset.display_fields) - display_fields_string = 'Geno.' + display_fields_string - query = """ - SELECT %s - FROM Geno, GenoFreeze, GenoXRef - WHERE - GenoXRef.GenoFreezeId = GenoFreeze.Id AND - GenoXRef.GenoId = Geno.Id AND - GenoFreeze.Name = '%s' AND - Geno.Name = '%s' - """ % (escape(display_fields_string), - escape(dataset.name), - escape(trait.name)) - - trait_info = g.db.execute(query).fetchone() - else: # Temp type - query = """SELECT %s FROM %s WHERE Name = %s""" - - trait_info = g.db.execute(query, - ','.join(dataset.display_fields), - dataset.type, trait.name).fetchone() - - if trait_info: - trait.haveinfo = True - for i, field in enumerate(dataset.display_fields): - holder = trait_info[i] - if isinstance(holder, bytes): - holder = holder.decode("utf-8", errors="ignore") - setattr(trait, field, holder) - - if dataset.type == 'Publish': - if trait.group_code: - trait.display_name = trait.group_code + "_" + str(trait.name) - - trait.confidential = 0 - if trait.pre_publication_description and not trait.pubmed_id: - trait.confidential = 1 - - description = 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 - trait.description_display = "" - if not trait.pubmed_id: - trait.abbreviation = trait.pre_publication_abbreviation - trait.description_display = trait.pre_publication_description - else: - trait.abbreviation = trait.post_publication_abbreviation - if description: - trait.description_display = description.strip() - - if not trait.year.isdigit(): - trait.pubmed_text = "N/A" - else: - trait.pubmed_text = trait.year - - # moved to config - - PUBMEDLINK_URL = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=%s&dopt=Abstract" - - if trait.pubmed_id: - trait.pubmed_link = PUBMEDLINK_URL % trait.pubmed_id - - if dataset.type == 'ProbeSet' and dataset.group: - description_string = trait.description - target_string = trait.probe_target_description - - if str(description_string or "") != "" and description_string != 'None': - description_display = description_string - else: - description_display = trait.symbol - - if (str(description_display or "") != "" and - description_display != 'N/A' and - str(target_string or "") != "" and target_string != 'None'): - description_display = description_display + '; ' + target_string.strip() - - # Save it for the jinja2 template - trait.description_display = description_display - - trait.location_repr = 'N/A' - if trait.chr and trait.mb: - trait.location_repr = 'Chr%s: %.6f' % ( - trait.chr, float(trait.mb)) - - elif dataset.type == "Geno": - trait.location_repr = 'N/A' - if trait.chr and trait.mb: - trait.location_repr = 'Chr%s: %.6f' % ( - trait.chr, float(trait.mb)) - - if get_qtl_info: - # LRS and its location - trait.LRS_score_repr = "N/A" - trait.LRS_location_repr = "N/A" - trait.locus = trait.locus_chr = trait.locus_mb = trait.lrs = trait.pvalue = trait.additive = "" - if dataset.type == 'ProbeSet' and not trait.cellid: - trait.mean = "" - query = """ - SELECT - ProbeSetXRef.Locus, ProbeSetXRef.LRS, ProbeSetXRef.pValue, ProbeSetXRef.mean, ProbeSetXRef.additive - FROM - ProbeSetXRef, ProbeSet - WHERE - ProbeSetXRef.ProbeSetId = ProbeSet.Id AND - ProbeSet.Name = "{}" AND - ProbeSetXRef.ProbeSetFreezeId ={} - """.format(trait.name, dataset.id) - - trait_qtl = g.db.execute(query).fetchone() - if trait_qtl: - trait.locus, trait.lrs, trait.pvalue, trait.mean, trait.additive = trait_qtl - if trait.locus: - query = """ - select Geno.Chr, Geno.Mb from Geno, Species - where Species.Name = '{}' and - Geno.Name = '{}' and - Geno.SpeciesId = Species.Id - """.format(dataset.group.species, trait.locus) - - result = g.db.execute(query).fetchone() - if result: - trait.locus_chr = result[0] - trait.locus_mb = result[1] - else: - trait.locus = trait.locus_chr = trait.locus_mb = trait.additive = "" - else: - trait.locus = trait.locus_chr = trait.locus_mb = trait.additive = "" - - if dataset.type == 'Publish': - query = """ - SELECT - PublishXRef.Locus, PublishXRef.LRS, PublishXRef.additive - FROM - PublishXRef, PublishFreeze - WHERE - PublishXRef.Id = %s AND - PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND - PublishFreeze.Id =%s - """ % (trait.name, dataset.id) - - trait_qtl = g.db.execute(query).fetchone() - if trait_qtl: - trait.locus, trait.lrs, trait.additive = trait_qtl - if trait.locus: - query = """ - select Geno.Chr, Geno.Mb from Geno, Species - where Species.Name = '{}' and - Geno.Name = '{}' and - Geno.SpeciesId = Species.Id - """.format(dataset.group.species, trait.locus) - - result = g.db.execute(query).fetchone() - if result: - trait.locus_chr = result[0] - trait.locus_mb = result[1] - else: - trait.locus = trait.locus_chr = trait.locus_mb = trait.additive = "" - else: - trait.locus = trait.locus_chr = trait.locus_mb = trait.additive = "" - else: - trait.locus = trait.lrs = trait.additive = "" - if (dataset.type == 'Publish' or dataset.type == "ProbeSet") and str(trait.locus_chr or "") != "" and str(trait.locus_mb or "") != "": - trait.LRS_location_repr = LRS_location_repr = 'Chr%s: %.6f' % ( - trait.locus_chr, float(trait.locus_mb)) - if str(trait.lrs or "") != "": - trait.LRS_score_repr = LRS_score_repr = '%3.1f' % trait.lrs - else: - raise KeyError(repr(trait.name) + - ' information is not found in the database.') - return trait diff --git a/gn3/base/webqtlCaseData.py b/gn3/base/webqtlCaseData.py deleted file mode 100644 index 8395af8..0000000 --- a/gn3/base/webqtlCaseData.py +++ /dev/null @@ -1,84 +0,0 @@ -# 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) -# -# Created by GeneNetwork Core Team 2010/08/10 - - -# uncomment below - -# from utility.logger import getLogger -# logger = getLogger(__name__) - -# import utility.tools - -# utility.tools.show_settings() -# pylint: disable-all - -class webqtlCaseData: - """one case data in one trait""" - - def __init__(self, name, value=None, variance=None, num_cases=None, name2=None): - self.name = name - self.name2 = name2 # Other name (for traits like BXD65a) - self.value = value # Trait Value - self.variance = variance # Trait Variance - self.num_cases = num_cases # Number of individuals/cases - self.extra_attributes = None - self.this_id = None # Set a sane default (can't be just "id" cause that's a reserved word) - self.outlier = None # Not set to True/False until later - - def __repr__(self): - case_data_string = "<webqtlCaseData> " - if self.value is not None: - case_data_string += "value=%2.3f" % self.value - if self.variance is not None: - case_data_string += " variance=%2.3f" % self.variance - if self.num_cases: - case_data_string += " ndata=%s" % self.num_cases - if self.name: - case_data_string += " name=%s" % self.name - if self.name2: - case_data_string += " name2=%s" % self.name2 - return case_data_string - - @property - def class_outlier(self): - """Template helper""" - if self.outlier: - return "outlier" - return "" - - @property - def display_value(self): - if self.value is not None: - return "%2.3f" % self.value - return "x" - - @property - def display_variance(self): - if self.variance is not None: - return "%2.3f" % self.variance - return "x" - - @property - def display_num_cases(self): - if self.num_cases is not None: - return "%s" % self.num_cases - return "x"
\ No newline at end of file diff --git a/gn3/correlation/__init__.py b/gn3/correlation/__init__.py deleted file mode 100644 index e69de29..0000000 --- a/gn3/correlation/__init__.py +++ /dev/null diff --git a/gn3/correlation/correlation_computations.py b/gn3/correlation/correlation_computations.py deleted file mode 100644 index 6a3f2bb..0000000 --- a/gn3/correlation/correlation_computations.py +++ /dev/null @@ -1,32 +0,0 @@ -"""module contains code for any computation in correlation""" - -import json -from .show_corr_results import CorrelationResults - -def compute_correlation(correlation_input_data, - correlation_results=CorrelationResults): - """function that does correlation .creates Correlation results instance - - correlation_input_data structure is a dict with - - { - "trait_id":"valid trait id", - "dataset":"", - "sample_vals":{}, - "primary_samples":"", - "corr_type":"", - corr_dataset:"", - "corr_return_results":"", - - - } - - """ - - corr_object = correlation_results( - start_vars=correlation_input_data) - - corr_results = corr_object.do_correlation(start_vars=correlation_input_data) - # possibility of file being so large cause of the not sure whether to return a file - - return corr_results diff --git a/gn3/correlation/correlation_functions.py b/gn3/correlation/correlation_functions.py deleted file mode 100644 index be08c96..0000000 --- a/gn3/correlation/correlation_functions.py +++ /dev/null @@ -1,96 +0,0 @@ - -""" -# 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) -# -# Created by GeneNetwork Core Team 2010/08/10 -# -# Last updated by NL 2011/03/23 - - -""" - -import rpy2.robjects -from gn3.base.mrna_assay_tissue_data import MrnaAssayTissueData - - -##################################################################################### -# Input: primaryValue(list): one list of expression values of one probeSet, -# targetValue(list): one list of expression values of one probeSet, -# method(string): indicate correlation method ('pearson' or 'spearman') -# Output: corr_result(list): first item is Correlation Value, second item is tissue number, -# third item is PValue -# Function: get correlation value,Tissue quantity ,p value result by using R; -# Note : This function is special case since both primaryValue and targetValue are from -# the same dataset. So the length of these two parameters is the same. They are pairs. -# Also, in the datatable TissueProbeSetData, all Tissue values are loaded based on -# the same tissue order -##################################################################################### - -def cal_zero_order_corr_for_tiss(primaryValue=[], targetValue=[], method='pearson'): - """refer above for info on the function""" - # pylint: disable = E, W, R, C - - #nb disabled pylint until tests are written for this function - - R_primary = rpy2.robjects.FloatVector(list(range(len(primaryValue)))) - N = len(primaryValue) - for i in range(len(primaryValue)): - R_primary[i] = primaryValue[i] - - R_target = rpy2.robjects.FloatVector(list(range(len(targetValue)))) - for i in range(len(targetValue)): - R_target[i] = targetValue[i] - - R_corr_test = rpy2.robjects.r['cor.test'] - if method == 'spearman': - R_result = R_corr_test(R_primary, R_target, method='spearman') - else: - R_result = R_corr_test(R_primary, R_target) - - corr_result = [] - corr_result.append(R_result[3][0]) - corr_result.append(N) - corr_result.append(R_result[2][0]) - - return corr_result - - -#################################################### -#################################################### -# input: cursor, symbolList (list), dataIdDict(Dict): key is symbol -# output: SymbolValuePairDict(dictionary):one dictionary of Symbol and Value Pair. -# key is symbol, value is one list of expression values of one probeSet. -# function: wrapper function for getSymbolValuePairDict function -# build gene symbol list if necessary, cut it into small lists if necessary, -# then call getSymbolValuePairDict function and merge the results. -################################################### -##################################################### - -def get_trait_symbol_and_tissue_values(symbol_list=None): - """function to get trait symbol and tissues values refer above""" - tissue_data = MrnaAssayTissueData(gene_symbols=symbol_list) - - if len(tissue_data.gene_symbols) >= 1: - return tissue_data.get_symbol_values_pairs() - - return None diff --git a/gn3/correlation/correlation_utility.py b/gn3/correlation/correlation_utility.py deleted file mode 100644 index 7583bd7..0000000 --- a/gn3/correlation/correlation_utility.py +++ /dev/null @@ -1,22 +0,0 @@ -"""module contains utility functions for correlation""" - - -class AttributeSetter: - """class for setting Attributes""" - - def __init__(self, trait_obj): - for key, value in trait_obj.items(): - setattr(self, key, value) - - def __str__(self): - return self.__class__.__name__ - - def get_dict(self): - """dummy function to get dict object""" - return self.__dict__ - - -def get_genofile_samplelist(dataset): - """mock function to get genofile samplelist""" - - return ["C57BL/6J"] diff --git a/gn3/correlation/show_corr_results.py b/gn3/correlation/show_corr_results.py deleted file mode 100644 index 55d8366..0000000 --- a/gn3/correlation/show_corr_results.py +++ /dev/null @@ -1,735 +0,0 @@ -"""module contains code for doing correlation""" - -import json -import collections -import numpy -import scipy.stats -import rpy2.robjects as ro -from flask import g -from gn3.base.data_set import create_dataset -from gn3.utility.db_tools import escape -from gn3.utility.helper_functions import get_species_dataset_trait -from gn3.utility.corr_result_helpers import normalize_values -from gn3.base.trait import create_trait -from gn3.utility import hmac -from . import correlation_functions - - -class CorrelationResults: - """class for computing correlation""" - # pylint: disable=too-many-instance-attributes - # pylint:disable=attribute-defined-outside-init - - def __init__(self, start_vars): - self.assertion_for_start_vars(start_vars) - - @staticmethod - def assertion_for_start_vars(start_vars): - # pylint: disable = E, W, R, C - - # should better ways to assert the variables - # example includes sample - assert("corr_type" in start_vars) - assert(isinstance(start_vars['corr_type'], str)) - # example includes pearson - assert('corr_sample_method' in start_vars) - assert('corr_dataset' in start_vars) - # means the limit - assert('corr_return_results' in start_vars) - - if "loc_chr" in start_vars: - assert('min_loc_mb' in start_vars) - assert('max_loc_mb' in start_vars) - - def get_formatted_corr_type(self): - """method to formatt corr_types""" - self.formatted_corr_type = "" - if self.corr_type == "lit": - self.formatted_corr_type += "Literature Correlation " - elif self.corr_type == "tissue": - self.formatted_corr_type += "Tissue Correlation " - elif self.corr_type == "sample": - self.formatted_corr_type += "Genetic Correlation " - - if self.corr_method == "pearson": - self.formatted_corr_type += "(Pearson's r)" - elif self.corr_method == "spearman": - self.formatted_corr_type += "(Spearman's rho)" - elif self.corr_method == "bicor": - self.formatted_corr_type += "(Biweight r)" - - def process_samples(self, start_vars, sample_names, excluded_samples=None): - """method to process samples""" - - - if not excluded_samples: - excluded_samples = () - - sample_val_dict = json.loads(start_vars["sample_vals"]) - print(sample_val_dict) - if sample_names is None: - raise NotImplementedError - - for sample in sample_names: - if sample not in excluded_samples: - value = sample_val_dict[sample] - - if not value.strip().lower() == "x": - self.sample_data[str(sample)] = float(value) - - def do_tissue_correlation_for_trait_list(self, tissue_dataset_id=1): - """Given a list of correlation results (self.correlation_results),\ - gets the tissue correlation value for each""" - # pylint: disable = E, W, R, C - - # Gets tissue expression values for the primary trait - primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list=[self.this_trait.symbol]) - - if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict: - primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower( - )] - gene_symbol_list = [ - trait.symbol for trait in self.correlation_results if trait.symbol] - - corr_result_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list=gene_symbol_list) - - for trait in self.correlation_results: - if trait.symbol and trait.symbol.lower() in corr_result_tissue_vals_dict: - this_trait_tissue_values = corr_result_tissue_vals_dict[trait.symbol.lower( - )] - - result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values, - this_trait_tissue_values, - self.corr_method) - - trait.tissue_corr = result[0] - trait.tissue_pvalue = result[2] - - def do_lit_correlation_for_trait_list(self): - # pylint: disable = E, W, R, C - - input_trait_mouse_gene_id = self.convert_to_mouse_gene_id( - self.dataset.group.species.lower(), self.this_trait.geneid) - - for trait in self.correlation_results: - - if trait.geneid: - trait.mouse_gene_id = self.convert_to_mouse_gene_id( - self.dataset.group.species.lower(), trait.geneid) - else: - trait.mouse_gene_id = None - - if trait.mouse_gene_id and str(trait.mouse_gene_id).find(";") == -1: - result = g.db.execute( - """SELECT value - FROM LCorrRamin3 - WHERE GeneId1='%s' and - GeneId2='%s' - """ % (escape(str(trait.mouse_gene_id)), escape(str(input_trait_mouse_gene_id))) - ).fetchone() - if not result: - result = g.db.execute("""SELECT value - FROM LCorrRamin3 - WHERE GeneId2='%s' and - GeneId1='%s' - """ % (escape(str(trait.mouse_gene_id)), escape(str(input_trait_mouse_gene_id))) - ).fetchone() - - if result: - lit_corr = result.value - trait.lit_corr = lit_corr - else: - trait.lit_corr = 0 - else: - trait.lit_corr = 0 - - def do_lit_correlation_for_all_traits(self): - """method for lit_correlation for all traits""" - # pylint: disable = E, W, R, C - input_trait_mouse_gene_id = self.convert_to_mouse_gene_id( - self.dataset.group.species.lower(), self.this_trait.geneid) - - lit_corr_data = {} - for trait, gene_id in list(self.trait_geneid_dict.items()): - mouse_gene_id = self.convert_to_mouse_gene_id( - self.dataset.group.species.lower(), gene_id) - - if mouse_gene_id and str(mouse_gene_id).find(";") == -1: - #print("gene_symbols:", input_trait_mouse_gene_id + " / " + mouse_gene_id) - result = g.db.execute( - """SELECT value - FROM LCorrRamin3 - WHERE GeneId1='%s' and - GeneId2='%s' - """ % (escape(mouse_gene_id), escape(input_trait_mouse_gene_id)) - ).fetchone() - if not result: - result = g.db.execute("""SELECT value - FROM LCorrRamin3 - WHERE GeneId2='%s' and - GeneId1='%s' - """ % (escape(mouse_gene_id), escape(input_trait_mouse_gene_id)) - ).fetchone() - if result: - #print("result:", result) - lit_corr = result.value - lit_corr_data[trait] = [gene_id, lit_corr] - else: - lit_corr_data[trait] = [gene_id, 0] - else: - lit_corr_data[trait] = [gene_id, 0] - - lit_corr_data = collections.OrderedDict(sorted(list(lit_corr_data.items()), - key=lambda t: -abs(t[1][1]))) - - return lit_corr_data - - def do_tissue_correlation_for_all_traits(self, tissue_dataset_id=1): - # Gets tissue expression values for the primary trait - # pylint: disable = E, W, R, C - primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list=[self.this_trait.symbol]) - - if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict: - primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower( - )] - - #print("trait_gene_symbols: ", pf(trait_gene_symbols.values())) - corr_result_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list=list(self.trait_symbol_dict.values())) - - #print("corr_result_tissue_vals: ", pf(corr_result_tissue_vals_dict)) - - #print("trait_gene_symbols: ", pf(trait_gene_symbols)) - - tissue_corr_data = {} - for trait, symbol in list(self.trait_symbol_dict.items()): - if symbol and symbol.lower() in corr_result_tissue_vals_dict: - this_trait_tissue_values = corr_result_tissue_vals_dict[symbol.lower( - )] - - result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values, - this_trait_tissue_values, - self.corr_method) - - tissue_corr_data[trait] = [symbol, result[0], result[2]] - - tissue_corr_data = collections.OrderedDict(sorted(list(tissue_corr_data.items()), - key=lambda t: -abs(t[1][1]))) - - def get_sample_r_and_p_values(self, trait, target_samples): - """Calculates the sample r (or rho) and p-value - - Given a primary trait and a target trait's sample values, - calculates either the pearson r or spearman rho and the p-value - using the corresponding scipy functions. - - """ - # pylint: disable = E, W, R, C - self.this_trait_vals = [] - target_vals = [] - - for index, sample in enumerate(self.target_dataset.samplelist): - if sample in self.sample_data: - sample_value = self.sample_data[sample] - target_sample_value = target_samples[index] - self.this_trait_vals.append(sample_value) - target_vals.append(target_sample_value) - - self.this_trait_vals, target_vals, num_overlap = normalize_values( - self.this_trait_vals, target_vals) - - if num_overlap > 5: - # ZS: 2015 could add biweight correlation, see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465711/ - if self.corr_method == 'bicor': - sample_r, sample_p = do_bicor( - self.this_trait_vals, target_vals) - - elif self.corr_method == 'pearson': - sample_r, sample_p = scipy.stats.pearsonr( - self.this_trait_vals, target_vals) - - else: - sample_r, sample_p = scipy.stats.spearmanr( - self.this_trait_vals, target_vals) - - if numpy.isnan(sample_r): - pass - - else: - - self.correlation_data[trait] = [ - sample_r, sample_p, num_overlap] - - def convert_to_mouse_gene_id(self, species=None, gene_id=None): - """If the species is rat or human, translate the gene_id to the mouse geneid - - If there is no input gene_id or there's no corresponding mouse gene_id, return None - - """ - if not gene_id: - return None - - mouse_gene_id = None - if "species" == "mouse": - mouse_gene_id = gene_id - - elif species == 'rat': - query = """SELECT mouse - FROM GeneIDXRef - WHERE rat='%s'""" % escape(gene_id) - - result = g.db.execute(query).fetchone() - if result != None: - mouse_gene_id = result.mouse - - elif species == "human": - - query = """SELECT mouse - FROM GeneIDXRef - WHERE human='%s'""" % escape(gene_id) - - result = g.db.execute(query).fetchone() - if result != None: - mouse_gene_id = result.mouse - - return mouse_gene_id - - def do_correlation(self, start_vars, create_dataset=create_dataset, - create_trait=create_trait, - get_species_dataset_trait=get_species_dataset_trait): - # pylint: disable = E, W, R, C - # probably refactor start_vars being passed twice - # this method aims to replace the do_correlation but also add dependendency injection - # to enable testing - - # should maybe refactor below code more or less works the same - if start_vars["dataset"] == "Temp": - self.dataset = create_dataset( - dataset_name="Temp", dataset_type="Temp", group_name=start_vars['group']) - - self.trait_id = start_vars["trait_id"] - - self.this_trait = create_trait(dataset=self.dataset, - name=self.trait_id, - cellid=None) - - else: - - get_species_dataset_trait(self, start_vars) - - corr_samples_group = start_vars['corr_samples_group'] - self.sample_data = {} - self.corr_type = start_vars['corr_type'] - self.corr_method = start_vars['corr_sample_method'] - self.min_expr = float( - start_vars["min_expr"]) if start_vars["min_expr"] != "" else None - self.p_range_lower = float( - start_vars["p_range_lower"]) if start_vars["p_range_lower"] != "" else -1.0 - self.p_range_upper = float( - start_vars["p_range_upper"]) if start_vars["p_range_upper"] != "" else 1.0 - - if ("loc_chr" in start_vars and "min_loc_mb" in start_vars and "max_loc_mb" in start_vars): - self.location_type = str(start_vars['location_type']) - self.location_chr = str(start_vars['loc_chr']) - - try: - - # the code is below is basically a temporary fix - self.min_location_mb = int(start_vars['min_loc_mb']) - self.max_location_mb = int(start_vars['max_loc_mb']) - except Exception as e: - self.min_location_mb = None - self.max_location_mb = None - - else: - self.location_type = self.location_chr = self.min_location_mb = self.max_location_mb = None - - self.get_formatted_corr_type() - - self.return_number = int(start_vars['corr_return_results']) - - primary_samples = self.dataset.group.samplelist - - - # The two if statements below append samples to the sample list based upon whether the user - # rselected Primary Samples Only, Other Samples Only, or All Samples - - if self.dataset.group.parlist != None: - primary_samples += self.dataset.group.parlist - - if self.dataset.group.f1list != None: - - primary_samples += self.dataset.group.f1list - - # If either BXD/whatever Only or All Samples, append all of that group's samplelist - - if corr_samples_group != 'samples_other': - - # print("primary samples are *****",primary_samples) - - self.process_samples(start_vars, primary_samples) - - if corr_samples_group != 'samples_primary': - if corr_samples_group == 'samples_other': - primary_samples = [x for x in primary_samples if x not in ( - self.dataset.group.parlist + self.dataset.group.f1list)] - - self.process_samples(start_vars, list(self.this_trait.data.keys()), primary_samples) - - self.target_dataset = create_dataset(start_vars['corr_dataset']) - # when you add code to retrieve the trait_data for target dataset got gets very slow - import time - - init_time = time.time() - self.target_dataset.get_trait_data(list(self.sample_data.keys())) - - aft_time = time.time() - init_time - - self.header_fields = get_header_fields( - self.target_dataset.type, self.corr_method) - - if self.target_dataset.type == "ProbeSet": - self.filter_cols = [7, 6] - - elif self.target_dataset.type == "Publish": - self.filter_cols = [6, 0] - - else: - self.filter_cols = [4, 0] - - self.correlation_results = [] - - self.correlation_data = {} - - if self.corr_type == "tissue": - self.trait_symbol_dict = self.dataset.retrieve_genes("Symbol") - - tissue_corr_data = self.do_tissue_correlation_for_all_traits() - if tissue_corr_data != None: - for trait in list(tissue_corr_data.keys())[:self.return_number]: - self.get_sample_r_and_p_values( - trait, self.target_dataset.trait_data[trait]) - else: - for trait, values in list(self.target_dataset.trait_data.items()): - self.get_sample_r_and_p_values(trait, values) - - elif self.corr_type == "lit": - self.trait_geneid_dict = self.dataset.retrieve_genes("GeneId") - lit_corr_data = self.do_lit_correlation_for_all_traits() - - for trait in list(lit_corr_data.keys())[:self.return_number]: - self.get_sample_r_and_p_values( - trait, self.target_dataset.trait_data[trait]) - - elif self.corr_type == "sample": - for trait, values in list(self.target_dataset.trait_data.items()): - self.get_sample_r_and_p_values(trait, values) - - self.correlation_data = collections.OrderedDict(sorted(list(self.correlation_data.items()), - key=lambda t: -abs(t[1][0]))) - - # ZS: Convert min/max chromosome to an int for the location range option - - """ - took 20.79 seconds took compute all the above majority of time taken on retrieving target dataset trait - info - """ - - initial_time_chr = time.time() - - range_chr_as_int = None - for order_id, chr_info in list(self.dataset.species.chromosomes.chromosomes.items()): - if 'loc_chr' in start_vars: - if chr_info.name == self.location_chr: - range_chr_as_int = order_id - - for _trait_counter, trait in enumerate(list(self.correlation_data.keys())[:self.return_number]): - trait_object = create_trait( - dataset=self.target_dataset, name=trait, get_qtl_info=True, get_sample_info=False) - if not trait_object: - continue - - chr_as_int = 0 - for order_id, chr_info in list(self.dataset.species.chromosomes.chromosomes.items()): - if self.location_type == "highest_lod": - if chr_info.name == trait_object.locus_chr: - chr_as_int = order_id - else: - if chr_info.name == trait_object.chr: - chr_as_int = order_id - - if (float(self.correlation_data[trait][0]) >= self.p_range_lower and - float(self.correlation_data[trait][0]) <= self.p_range_upper): - - if (self.target_dataset.type == "ProbeSet" or self.target_dataset.type == "Publish") and bool(trait_object.mean): - if (self.min_expr != None) and (float(trait_object.mean) < self.min_expr): - continue - - if range_chr_as_int != None and (chr_as_int != range_chr_as_int): - continue - if self.location_type == "highest_lod": - if (self.min_location_mb != None) and (float(trait_object.locus_mb) < float(self.min_location_mb)): - continue - if (self.max_location_mb != None) and (float(trait_object.locus_mb) > float(self.max_location_mb)): - continue - else: - if (self.min_location_mb != None) and (float(trait_object.mb) < float(self.min_location_mb)): - continue - if (self.max_location_mb != None) and (float(trait_object.mb) > float(self.max_location_mb)): - continue - - (trait_object.sample_r, - trait_object.sample_p, - trait_object.num_overlap) = self.correlation_data[trait] - - # Set some sane defaults - trait_object.tissue_corr = 0 - trait_object.tissue_pvalue = 0 - trait_object.lit_corr = 0 - if self.corr_type == "tissue" and tissue_corr_data != None: - trait_object.tissue_corr = tissue_corr_data[trait][1] - trait_object.tissue_pvalue = tissue_corr_data[trait][2] - elif self.corr_type == "lit": - trait_object.lit_corr = lit_corr_data[trait][1] - - self.correlation_results.append(trait_object) - - """ - above takes time with respect to size of traits i.e n=100,500,.....t_size - """ - - if self.corr_type != "lit" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet": - # self.do_lit_correlation_for_trait_list() - self.do_lit_correlation_for_trait_list() - - if self.corr_type != "tissue" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet": - self.do_tissue_correlation_for_trait_list() - # self.do_lit_correlation_for_trait_list() - - self.json_results = generate_corr_json( - self.correlation_results, self.this_trait, self.dataset, self.target_dataset) - - # org mode by bons - - # DVORAKS - # klavaro for touch typing - # archwiki for documentation - # exwm for window manager ->13 - - # will fit perfectly with genenetwork 2 with change of anything if return self - - # alternative for this - return self.json_results - # return { - # # "Results": "succeess", - # # "return_number": self.return_number, - # # "primary_samples": primary_samples, - # # "time_taken": 12, - # # "correlation_data": self.correlation_data, - # "correlation_json": self.json_results - # } - - -def do_bicor(this_trait_vals, target_trait_vals): - # pylint: disable = E, W, R, C - r_library = ro.r["library"] # Map the library function - r_options = ro.r["options"] # Map the options function - - r_library("WGCNA") - r_bicor = ro.r["bicorAndPvalue"] # Map the bicorAndPvalue function - - r_options(stringsAsFactors=False) - - this_vals = ro.Vector(this_trait_vals) - target_vals = ro.Vector(target_trait_vals) - - the_r, the_p, _fisher_transform, _the_t, _n_obs = [ - numpy.asarray(x) for x in r_bicor(x=this_vals, y=target_vals)] - - return the_r, the_p - - -def get_header_fields(data_type, corr_method): - """function to get header fields when doing correlation""" - if data_type == "ProbeSet": - if corr_method == "spearman": - - header_fields = ['Index', - 'Record', - 'Symbol', - 'Description', - 'Location', - 'Mean', - 'Sample rho', - 'N', - 'Sample p(rho)', - 'Lit rho', - 'Tissue rho', - 'Tissue p(rho)', - 'Max LRS', - 'Max LRS Location', - 'Additive Effect'] - - else: - header_fields = ['Index', - 'Record', - 'Abbreviation', - 'Description', - 'Mean', - 'Authors', - 'Year', - 'Sample r', - 'N', - 'Sample p(r)', - 'Max LRS', - 'Max LRS Location', - 'Additive Effect'] - - elif data_type == "Publish": - if corr_method == "spearman": - - header_fields = ['Index', - 'Record', - 'Abbreviation', - 'Description', - 'Mean', - 'Authors', - 'Year', - 'Sample rho', - 'N', - 'Sample p(rho)', - 'Max LRS', - 'Max LRS Location', - 'Additive Effect'] - - else: - header_fields = ['Index', - 'Record', - 'Abbreviation', - 'Description', - 'Mean', - 'Authors', - 'Year', - 'Sample r', - 'N', - 'Sample p(r)', - 'Max LRS', - 'Max LRS Location', - 'Additive Effect'] - - else: - if corr_method == "spearman": - header_fields = ['Index', - 'ID', - 'Location', - 'Sample rho', - 'N', - 'Sample p(rho)'] - - else: - header_fields = ['Index', - 'ID', - 'Location', - 'Sample r', - 'N', - 'Sample p(r)'] - - return header_fields - - -def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_api=False): - """function to generate corr json data""" - #todo refactor this function - results_list = [] - for i, trait in enumerate(corr_results): - if trait.view == False: - continue - results_dict = {} - results_dict['index'] = i + 1 - results_dict['trait_id'] = trait.name - results_dict['dataset'] = trait.dataset.name - results_dict['hmac'] = hmac.data_hmac( - '{}:{}'.format(trait.name, trait.dataset.name)) - if target_dataset.type == "ProbeSet": - results_dict['symbol'] = trait.symbol - results_dict['description'] = "N/A" - results_dict['location'] = trait.location_repr - results_dict['mean'] = "N/A" - results_dict['additive'] = "N/A" - if bool(trait.description_display): - results_dict['description'] = trait.description_display - if bool(trait.mean): - results_dict['mean'] = f"{float(trait.mean):.3f}" - try: - results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}" - except: - results_dict['lod_score'] = "N/A" - results_dict['lrs_location'] = trait.LRS_location_repr - if bool(trait.additive): - results_dict['additive'] = f"{float(trait.additive):.3f}" - results_dict['sample_r'] = f"{float(trait.sample_r):.3f}" - results_dict['num_overlap'] = trait.num_overlap - results_dict['sample_p'] = f"{float(trait.sample_p):.3e}" - results_dict['lit_corr'] = "--" - results_dict['tissue_corr'] = "--" - results_dict['tissue_pvalue'] = "--" - if bool(trait.lit_corr): - results_dict['lit_corr'] = f"{float(trait.lit_corr):.3f}" - if bool(trait.tissue_corr): - results_dict['tissue_corr'] = f"{float(trait.tissue_corr):.3f}" - results_dict['tissue_pvalue'] = f"{float(trait.tissue_pvalue):.3e}" - elif target_dataset.type == "Publish": - results_dict['abbreviation_display'] = "N/A" - results_dict['description'] = "N/A" - results_dict['mean'] = "N/A" - results_dict['authors_display'] = "N/A" - results_dict['additive'] = "N/A" - if for_api: - results_dict['pubmed_id'] = "N/A" - results_dict['year'] = "N/A" - else: - results_dict['pubmed_link'] = "N/A" - results_dict['pubmed_text'] = "N/A" - - if bool(trait.abbreviation): - results_dict['abbreviation_display'] = trait.abbreviation - if bool(trait.description_display): - results_dict['description'] = trait.description_display - if bool(trait.mean): - results_dict['mean'] = f"{float(trait.mean):.3f}" - if bool(trait.authors): - authors_list = trait.authors.split(',') - if len(authors_list) > 6: - results_dict['authors_display'] = ", ".join( - authors_list[:6]) + ", et al." - else: - results_dict['authors_display'] = trait.authors - if bool(trait.pubmed_id): - if for_api: - results_dict['pubmed_id'] = trait.pubmed_id - results_dict['year'] = trait.pubmed_text - else: - results_dict['pubmed_link'] = trait.pubmed_link - results_dict['pubmed_text'] = trait.pubmed_text - try: - results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}" - except: - results_dict['lod_score'] = "N/A" - results_dict['lrs_location'] = trait.LRS_location_repr - if bool(trait.additive): - results_dict['additive'] = f"{float(trait.additive):.3f}" - results_dict['sample_r'] = f"{float(trait.sample_r):.3f}" - results_dict['num_overlap'] = trait.num_overlap - results_dict['sample_p'] = f"{float(trait.sample_p):.3e}" - else: - results_dict['location'] = trait.location_repr - results_dict['sample_r'] = f"{float(trait.sample_r):.3f}" - results_dict['num_overlap'] = trait.num_overlap - results_dict['sample_p'] = f"{float(trait.sample_p):.3e}" - - results_list.append(results_dict) - - return json.dumps(results_list) diff --git a/gn3/db/__init__.py b/gn3/db/__init__.py deleted file mode 100644 index e69de29..0000000 --- a/gn3/db/__init__.py +++ /dev/null diff --git a/gn3/db/calls.py b/gn3/db/calls.py deleted file mode 100644 index 547bccf..0000000 --- a/gn3/db/calls.py +++ /dev/null @@ -1,51 +0,0 @@ -"""module contains calls method for db""" -import json -import urllib -from flask import g -from gn3.utility.logger import getLogger -logger = getLogger(__name__) -# should probably put this is env -USE_GN_SERVER = False -LOG_SQL = False - -GN_SERVER_URL = None - - -def fetch1(query, path=None, func=None): - """fetch1 method""" - if USE_GN_SERVER and path: - result = gn_server(path) - if func is not None: - res2 = func(result) - - else: - res2 = result - - if LOG_SQL: - pass - # should probably and logger - # logger.debug("Replaced SQL call", query) - - # logger.debug(path,res2) - return res2 - - return fetchone(query) - - -def gn_server(path): - """Return JSON record by calling GN_SERVER - - """ - res = urllib.request.urlopen(GN_SERVER_URL+path) - rest = res.read() - res2 = json.loads(rest) - return res2 - - -def fetchone(query): - """method to fetchone item from db""" - def helper(query): - res = g.db.execute(query) - return res.fetchone() - - return logger.sql(query, helper) diff --git a/gn3/db/webqtlDatabaseFunction.py b/gn3/db/webqtlDatabaseFunction.py deleted file mode 100644 index 9e9982b..0000000 --- a/gn3/db/webqtlDatabaseFunction.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -# 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 gn3.db.calls import fetch1 - -from gn3.utility.logger import getLogger -logger = getLogger(__name__) - -########################################################################### -# output: cursor instance -# function: connect to database and return cursor instance -########################################################################### - - -def retrieve_species(group): - """Get the species of a group (e.g. returns string "mouse" on "BXD" - - """ - result = fetch1("select Species.Name from Species, InbredSet where InbredSet.Name = '%s' and InbredSet.SpeciesId = Species.Id" % ( - group), "/cross/"+group+".json", lambda r: (r["species"],))[0] - # logger.debug("retrieve_species result:", result) - return result - - -def retrieve_species_id(group): - """retrieve species id method""" - - result = fetch1("select SpeciesId from InbredSet where Name = '%s'" % ( - group), "/cross/"+group+".json", lambda r: (r["species_id"],))[0] - logger.debug("retrieve_species_id result:", result) - return result diff --git a/gn3/utility/__init__.py b/gn3/utility/__init__.py deleted file mode 100644 index e69de29..0000000 --- a/gn3/utility/__init__.py +++ /dev/null diff --git a/gn3/utility/bunch.py b/gn3/utility/bunch.py deleted file mode 100644 index c1fd907..0000000 --- a/gn3/utility/bunch.py +++ /dev/null @@ -1,16 +0,0 @@ -"""module contains Bunch class a dictionary like with object notation """ - -from pprint import pformat as pf - - -class Bunch: - """Like a dictionary but using object notation""" - - def __init__(self, **kw): - self.__dict__ = kw - - def __repr__(self): - return pf(self.__dict__) - - def __str__(self): - return self.__class__.__name__ diff --git a/gn3/utility/chunks.py b/gn3/utility/chunks.py deleted file mode 100644 index fa27a39..0000000 --- a/gn3/utility/chunks.py +++ /dev/null @@ -1,32 +0,0 @@ -"""module for chunks functions""" - -import math - - -def divide_into_chunks(the_list, number_chunks): - """Divides a list into approximately number_chunks smaller lists - - >>> divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 3) - [[1, 2, 7], [3, 22, 8], [5, 22, 333]] - >>> divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 4) - [[1, 2, 7], [3, 22, 8], [5, 22, 333]] - >>> divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 5) - [[1, 2], [7, 3], [22, 8], [5, 22], [333]] - >>> - - """ - length = len(the_list) - - if length == 0: - return [[]] - - if length <= number_chunks: - number_chunks = length - - chunksize = int(math.ceil(length / number_chunks)) - - chunks = [] - for counter in range(0, length, chunksize): - chunks.append(the_list[counter:counter+chunksize]) - - return chunks diff --git a/gn3/utility/corr_result_helpers.py b/gn3/utility/corr_result_helpers.py deleted file mode 100644 index a68308e..0000000 --- a/gn3/utility/corr_result_helpers.py +++ /dev/null @@ -1,45 +0,0 @@ -"""module contains helper function for corr results""" - -#pylint:disable=C0103 -#above disable snake_case for variable tod refactor -def normalize_values(a_values, b_values): - """ - Trim two lists of values to contain only the values they both share - - Given two lists of sample values, trim each list so that it contains - only the samples that contain a value in both lists. Also returns - the number of such samples. - - >>> normalize_values([2.3, None, None, 3.2, 4.1, 5], [3.4, 7.2, 1.3, None, 6.2, 4.1]) - ([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3) - - """ - a_new = [] - b_new = [] - for a, b in zip(a_values, b_values): - if (a and b is not None): - a_new.append(a) - b_new.append(b) - return a_new, b_new, len(a_new) - - -def common_keys(a_samples, b_samples): - """ - >>> a = dict(BXD1 = 9.113, BXD2 = 9.825, BXD14 = 8.985, BXD15 = 9.300) - >>> b = dict(BXD1 = 9.723, BXD3 = 9.825, BXD14 = 9.124, BXD16 = 9.300) - >>> sorted(common_keys(a, b)) - ['BXD1', 'BXD14'] - """ - return set(a_samples.keys()).intersection(set(b_samples.keys())) - - -def normalize_values_with_samples(a_samples, b_samples): - """function to normalize values with samples""" - common_samples = common_keys(a_samples, b_samples) - a_new = {} - b_new = {} - for sample in common_samples: - a_new[sample] = a_samples[sample] - b_new[sample] = b_samples[sample] - - return a_new, b_new, len(a_new) diff --git a/gn3/utility/db_tools.py b/gn3/utility/db_tools.py deleted file mode 100644 index 446acda..0000000 --- a/gn3/utility/db_tools.py +++ /dev/null @@ -1,19 +0,0 @@ -"""module for db_tools""" -from MySQLdb import escape_string as escape_ - - -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""" - return [escape_(str(item)).decode('utf8') for item in items] - - -def escape(string_): - """escape function""" - return escape_(string_).decode('utf8') diff --git a/gn3/utility/get_group_samplelists.py b/gn3/utility/get_group_samplelists.py deleted file mode 100644 index 8fb322a..0000000 --- a/gn3/utility/get_group_samplelists.py +++ /dev/null @@ -1,47 +0,0 @@ - -"""module for group samplelist""" -import os - -#todo close the files after opening -def get_samplelist(file_type, geno_file): - """get samplelist function""" - if file_type == "geno": - return get_samplelist_from_geno(geno_file) - elif file_type == "plink": - return get_samplelist_from_plink(geno_file) - -def get_samplelist_from_geno(genofilename): - if os.path.isfile(genofilename + '.gz'): - genofilename += '.gz' - genofile = gzip.open(genofilename) - else: - genofile = open(genofilename) - - for line in genofile: - line = line.strip() - if not line: - continue - if line.startswith(("#", "@")): - continue - break - - headers = line.split("\t") - - if headers[3] == "Mb": - samplelist = headers[4:] - else: - samplelist = headers[3:] - return samplelist - - - -def get_samplelist_from_plink(genofilename): - """get samplelist from plink""" - genofile = open(genofilename) - - samplelist = [] - for line in genofile: - line = line.split(" ") - samplelist.append(line[1]) - - return samplelist diff --git a/gn3/utility/helper_functions.py b/gn3/utility/helper_functions.py deleted file mode 100644 index f5a8b80..0000000 --- a/gn3/utility/helper_functions.py +++ /dev/null @@ -1,24 +0,0 @@ -"""module contains general helper functions """ -from gn3.base.data_set import create_dataset -from gn3.base.trait import create_trait -from gn3.base.species import TheSpecies - - -def get_species_dataset_trait(self, start_vars): - """function to get species dataset and trait""" - if "temp_trait" in list(start_vars.keys()): - if start_vars['temp_trait'] == "True": - self.dataset = create_dataset( - dataset_name="Temp", dataset_type="Temp", group_name=start_vars['group']) - - else: - self.dataset = create_dataset(start_vars['dataset']) - - else: - self.dataset = create_dataset(start_vars['dataset']) - self.species = TheSpecies(dataset=self.dataset) - - self.this_trait = create_trait(dataset=self.dataset, - name=start_vars['trait_id'], - cellid=None, - get_qtl_info=True) diff --git a/gn3/utility/hmac.py b/gn3/utility/hmac.py deleted file mode 100644 index eb39e59..0000000 --- a/gn3/utility/hmac.py +++ /dev/null @@ -1,50 +0,0 @@ -"""module for hmac """ - -# pylint: disable-all -import hmac -import hashlib - -# xtodo work on this file - -# from main import app - - -def hmac_creation(stringy): - """Helper function to create the actual hmac""" - - # secret = app.config['SECRET_HMAC_CODE'] - # put in config - secret = "my secret" - hmaced = hmac.new(bytearray(secret, "latin-1"), - bytearray(stringy, "utf-8"), - hashlib.sha1) - hm = hmaced.hexdigest() - # ZS: Leaving the below comment here to ask Pjotr about - # "Conventional wisdom is that you don't lose much in terms of security if you throw away up to half of the output." - # http://www.w3.org/QA/2009/07/hmac_truncation_in_xml_signatu.html - hm = hm[:20] - return hm - - -def data_hmac(stringy): - """Takes arbitrary data string and appends :hmac so we know data hasn't been tampered with""" - return stringy + ":" + hmac_creation(stringy) - - -def url_for_hmac(endpoint, **values): - """Like url_for but adds an hmac at the end to insure the url hasn't been tampered with""" - - url = url_for(endpoint, **values) - - hm = hmac_creation(url) - if '?' in url: - combiner = "&" - else: - combiner = "?" - return url + combiner + "hm=" + hm - - - -# todo -# app.jinja_env.globals.update(url_for_hmac=url_for_hmac, -# data_hmac=data_hmac) diff --git a/gn3/utility/logger.py b/gn3/utility/logger.py deleted file mode 100644 index 4245a02..0000000 --- a/gn3/utility/logger.py +++ /dev/null @@ -1,163 +0,0 @@ -""" -# GeneNetwork logger -# -# The standard python logging module is very good. This logger adds a -# few facilities on top of that. Main one being that it picks up -# settings for log levels (global and by module) and (potentially) -# offers some fine grained log levels for the standard levels. -# -# All behaviour is defined here. Global settings (defined in -# default_settings.py). -# -# To use logging and settings put this at the top of a module: -# -# import utility.logger -# logger = utility.logger.getLogger(__name__ ) -# -# To override global behaviour set the LOG_LEVEL in default_settings.py -# or use an environment variable, e.g. -# -# env LOG_LEVEL=INFO ./bin/genenetwork2 -# -# To override log level for a module replace that with, for example, -# -# import logging -# import utility.logger -# logger = utility.logger.getLogger(__name__,level=logging.DEBUG) -# -# We'll add more overrides soon. -""" -# todo incomplete file - -# pylint: disable-all -import logging -import datetime -from inspect import isfunction -from inspect import stack - -from pprint import pformat as pf - - -# from utility.tools import LOG_LEVEL, LOG_LEVEL_DEBUG, LOG_SQL - -LOG_SQL = True - - -class GNLogger: - """A logger class with some additional functionality, such as - multiple parameter logging, SQL logging, timing, colors, and lazy - functions. - - """ - - def __init__(self, name): - self.logger = logging.getLogger(name) - - def setLevel(self, value): - """Set the undelying log level""" - self.logger.setLevel(value) - - def debug(self, *args): - """Call logging.debug for multiple args. Use (lazy) debugf and -level=num to filter on LOG_LEVEL_DEBUG. - - """ - self.collect(self.logger.debug, *args) - - def debug20(self, *args): - """Call logging.debug for multiple args. Use level=num to filter on -LOG_LEVEL_DEBUG (NYI). - - """ - if level <= LOG_LEVEL_DEBUG: - if self.logger.getEffectiveLevel() < 20: - self.collect(self.logger.debug, *args) - - def info(self, *args): - """Call logging.info for multiple args""" - self.collect(self.logger.info, *args) - - def warning(self, *args): - """Call logging.warning for multiple args""" - self.collect(self.logger.warning, *args) - # self.logger.warning(self.collect(*args)) - - def error(self, *args): - """Call logging.error for multiple args""" - now = datetime.datetime.utcnow() - time_str = now.strftime('%H:%M:%S UTC %Y%m%d') - l = [time_str]+list(args) - self.collect(self.logger.error, *l) - - def infof(self, *args): - """Call logging.info for multiple args lazily""" - # only evaluate function when logging - if self.logger.getEffectiveLevel() < 30: - self.collectf(self.logger.debug, *args) - - def debugf(self, level=0, *args): - """Call logging.debug for multiple args lazily and handle - LOG_LEVEL_DEBUG correctly - - """ - # only evaluate function when logging - if level <= LOG_LEVEL_DEBUG: - if self.logger.getEffectiveLevel() < 20: - self.collectf(self.logger.debug, *args) - - def sql(self, sqlcommand, fun=None): - """Log SQL command, optionally invoking a timed fun""" - if LOG_SQL: - caller = stack()[1][3] - if caller in ['fetchone', 'fetch1', 'fetchall']: - caller = stack()[2][3] - self.info(caller, sqlcommand) - if fun: - result = fun(sqlcommand) - if LOG_SQL: - self.info(result) - return result - - def collect(self, fun, *args): - """Collect arguments and use fun to output""" - out = "."+stack()[2][3] - for a in args: - if len(out) > 1: - out += ": " - if isinstance(a, str): - out = out + a - else: - out = out + pf(a, width=160) - fun(out) - - def collectf(self, fun, *args): - """Collect arguments and use fun to output one by one""" - out = "."+stack()[2][3] - for a in args: - if len(out) > 1: - out += ": " - if isfunction(a): - out += a() - else: - if isinstance(a, str): - out = out + a - else: - out = out + pf(a, width=160) - fun(out) - -# Get the module logger. You can override log levels at the -# module level - - -def getLogger(name, level=None): - """method to get logger""" - gnlogger = GNLogger(name) - _logger = gnlogger.logger - - # if level: - # logger.setLevel(level) - # else: - # logger.setLevel(LOG_LEVEL) - - # logger.info("Log level of "+name+" set to "+logging.getLevelName(logger.getEffectiveLevel())) - return gnlogger diff --git a/gn3/utility/species.py b/gn3/utility/species.py deleted file mode 100644 index 0140d41..0000000 --- a/gn3/utility/species.py +++ /dev/null @@ -1,71 +0,0 @@ -"""module contains species and chromosomes classes""" -import collections - -from flask import g - - -from gn3.utility.logger import getLogger -logger = getLogger(__name__) - - # pylint: disable=too-few-public-methods - # intentionally disabled check for few public methods - -class TheSpecies: - """class for Species""" - - def __init__(self, dataset=None, species_name=None): - if species_name is not None: - self.name = species_name - self.chromosomes = Chromosomes(species=self.name) - else: - self.dataset = dataset - self.chromosomes = Chromosomes(dataset=self.dataset) - - - -class IndChromosome: - """class for IndChromosome""" - - def __init__(self, name, length): - self.name = name - self.length = length - - @property - def mb_length(self): - """Chromosome length in megabases""" - return self.length / 1000000 - - - - -class Chromosomes: - """class for Chromosomes""" - - def __init__(self, dataset=None, species=None): - self.chromosomes = collections.OrderedDict() - if species is not None: - query = """ - Select - Chr_Length.Name, Chr_Length.OrderId, Length from Chr_Length, Species - where - Chr_Length.SpeciesId = Species.SpeciesId AND - Species.Name = '%s' - Order by OrderId - """ % species.capitalize() - else: - self.dataset = dataset - - query = """ - Select - Chr_Length.Name, Chr_Length.OrderId, Length from Chr_Length, InbredSet - where - Chr_Length.SpeciesId = InbredSet.SpeciesId AND - InbredSet.Name = '%s' - Order by OrderId - """ % self.dataset.group.name - logger.sql(query) - results = g.db.execute(query).fetchall() - - for item in results: - self.chromosomes[item.OrderId] = IndChromosome( - item.Name, item.Length) diff --git a/gn3/utility/tools.py b/gn3/utility/tools.py deleted file mode 100644 index 85df9f6..0000000 --- a/gn3/utility/tools.py +++ /dev/null @@ -1,37 +0,0 @@ -"""module contains general tools forgenenetwork""" - -import os - -from default_settings import GENENETWORK_FILES - - -def valid_file(file_name): - """check if file is valid""" - if os.path.isfile(file_name): - return file_name - return None - - -def valid_path(dir_name): - """check if path is valid""" - if os.path.isdir(dir_name): - return dir_name - return None - - -def locate_ignore_error(name, subdir=None): - """ - Locate a static flat file in the GENENETWORK_FILES environment. - - This function does not throw an error when the file is not found - but returns None. - """ - base = GENENETWORK_FILES - if subdir: - base = base+"/"+subdir - if valid_path(base): - lookfor = base + "/" + name - if valid_file(lookfor): - return lookfor - - return None diff --git a/gn3/utility/webqtlUtil.py b/gn3/utility/webqtlUtil.py deleted file mode 100644 index 1c76410..0000000 --- a/gn3/utility/webqtlUtil.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -# 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) -# -# Created by GeneNetwork Core Team 2010/08/10 -# -# Last updated by GeneNetwork Core Team 2010/10/20 - -# from base import webqtlConfig - -# NL, 07/27/2010. moved from webqtlForm.py -# Dict of Parents and F1 information, In the order of [F1, Mat, Pat] - -""" -ParInfo = { - 'BXH': ['BHF1', 'HBF1', 'C57BL/6J', 'C3H/HeJ'], - 'AKXD': ['AKF1', 'KAF1', 'AKR/J', 'DBA/2J'], - 'BXD': ['B6D2F1', 'D2B6F1', 'C57BL/6J', 'DBA/2J'], - 'C57BL-6JxC57BL-6NJF2': ['', '', 'C57BL/6J', 'C57BL/6NJ'], - 'BXD300': ['B6D2F1', 'D2B6F1', 'C57BL/6J', 'DBA/2J'], - 'B6BTBRF2': ['B6BTBRF1', 'BTBRB6F1', 'C57BL/6J', 'BTBRT<+>tf/J'], - 'BHHBF2': ['B6HF2', 'HB6F2', 'C57BL/6J', 'C3H/HeJ'], - 'BHF2': ['B6HF2', 'HB6F2', 'C57BL/6J', 'C3H/HeJ'], - 'B6D2F2': ['B6D2F1', 'D2B6F1', 'C57BL/6J', 'DBA/2J'], - 'BDF2-1999': ['B6D2F2', 'D2B6F2', 'C57BL/6J', 'DBA/2J'], - 'BDF2-2005': ['B6D2F1', 'D2B6F1', 'C57BL/6J', 'DBA/2J'], - 'CTB6F2': ['CTB6F2', 'B6CTF2', 'C57BL/6J', 'Castaneous'], - 'CXB': ['CBF1', 'BCF1', 'C57BL/6ByJ', 'BALB/cByJ'], - 'AXBXA': ['ABF1', 'BAF1', 'C57BL/6J', 'A/J'], - 'AXB': ['ABF1', 'BAF1', 'C57BL/6J', 'A/J'], - 'BXA': ['BAF1', 'ABF1', 'C57BL/6J', 'A/J'], - 'LXS': ['LSF1', 'SLF1', 'ISS', 'ILS'], - 'HXBBXH': ['SHR_BNF1', 'BN_SHRF1', 'BN-Lx/Cub', 'SHR/OlaIpcv'], - 'BayXSha': ['BayXShaF1', 'ShaXBayF1', 'Bay-0', 'Shahdara'], - 'ColXBur': ['ColXBurF1', 'BurXColF1', 'Col-0', 'Bur-0'], - 'ColXCvi': ['ColXCviF1', 'CviXColF1', 'Col-0', 'Cvi'], - 'SXM': ['SMF1', 'MSF1', 'Steptoe', 'Morex'], - 'HRDP': ['SHR_BNF1', 'BN_SHRF1', 'BN-Lx/Cub', 'SHR/OlaIpcv'] -} - - -def has_access_to_confidentail_phenotype_trait(privilege, username, authorized_users): - """function to access to confidential phenotype Traits further implementation needed""" - access_to_confidential_phenotype_trait = 0 - - results = (privilege, username, authorized_users) - return access_to_confidential_phenotype_trait @@ -72,19 +72,11 @@ ("python" ,python-wrapper) ("python-flask" ,python-flask) ("python-pylint" python-pylint) - ("python-numpy" ,python-numpy) ("python-scipy" ,python-scipy) ("python-mypy" ,python-mypy) ("python-mypy-extensions" ,python-mypy-extensions) ("python-redis" ,python-redis) - ("python-scipy" ,python-scipy) - ;; Remove one of these! - ("python-sqlalchemy" ,python-sqlalchemy) - ("python-sqlalchemy-stubs" ,python-sqlalchemy-stubs) - ("python-mysqlclient" ,python-mysqlclient) - ;; This requires R in it's path - ;; TODO: Remove! - ("python-rpy2" ,python-rpy2))) + ("python-scipy" ,python-scipy))) (build-system python-build-system) (home-page "https://github.com/genenetwork/genenetwork3") (synopsis "GeneNetwork3 API for data science and machine learning.") diff --git a/mypy.ini b/mypy.ini deleted file mode 100644 index cbc71ab..0000000 --- a/mypy.ini +++ /dev/null @@ -1,11 +0,0 @@ -[mypy] -plugins = sqlmypy - -[mypy-scipy.*] -ignore_missing_imports = True - -[mypy-MySQLdb.*] -ignore_missing_imports = True - -[mypy-rpy2.*] -ignore_missing_imports = True
\ No newline at end of file diff --git a/tests/integration/correlation_data.json b/tests/integration/correlation_data.json deleted file mode 100644 index 87d24e3..0000000 --- a/tests/integration/correlation_data.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "primary_samples": "C57BL/6J,DBA/2J,B6D2F1,D2B6F1,BXD1,BXD2,BXD5,BXD6,BXD8,BXD9,BXD11,BXD12,BXD13,BXD14,BXD15,BXD16,BXD18,BXD19,BXD20,BXD21,BXD22,BXD23,BXD24,BXD24a,BXD25,BXD27,BXD28,BXD29,BXD30,BXD31,BXD32,BXD33,BXD34,BXD35,BXD36,BXD37,BXD38,BXD39,BXD40,BXD41,BXD42,BXD43,BXD44,BXD45,BXD48,BXD48a,BXD49,BXD50,BXD51,BXD52,BXD53,BXD54,BXD55,BXD56,BXD59,BXD60,BXD61,BXD62,BXD63,BXD64,BXD65,BXD65a,BXD65b,BXD66,BXD67,BXD68,BXD69,BXD70,BXD71,BXD72,BXD73,BXD73a,BXD73b,BXD74,BXD75,BXD76,BXD77,BXD78,BXD79,BXD81,BXD83,BXD84,BXD85,BXD86,BXD87,BXD88,BXD89,BXD90,BXD91,BXD93,BXD94,BXD95,BXD98,BXD99,BXD100,BXD101,BXD102,BXD104,BXD105,BXD106,BXD107,BXD108,BXD109,BXD110,BXD111,BXD112,BXD113,BXD114,BXD115,BXD116,BXD117,BXD119,BXD120,BXD121,BXD122,BXD123,BXD124,BXD125,BXD126,BXD127,BXD128,BXD128a,BXD130,BXD131,BXD132,BXD133,BXD134,BXD135,BXD136,BXD137,BXD138,BXD139,BXD141,BXD142,BXD144,BXD145,BXD146,BXD147,BXD148,BXD149,BXD150,BXD151,BXD152,BXD153,BXD154,BXD155,BXD156,BXD157,BXD160,BXD161,BXD162,BXD165,BXD168,BXD169,BXD170,BXD171,BXD172,BXD173,BXD174,BXD175,BXD176,BXD177,BXD178,BXD180,BXD181,BXD183,BXD184,BXD186,BXD187,BXD188,BXD189,BXD190,BXD191,BXD192,BXD193,BXD194,BXD195,BXD196,BXD197,BXD198,BXD199,BXD200,BXD201,BXD202,BXD203,BXD204,BXD205,BXD206,BXD207,BXD208,BXD209,BXD210,BXD211,BXD212,BXD213,BXD214,BXD215,BXD216,BXD217,BXD218,BXD219,BXD220", - "trait_id": "1444666_at", - "dataset": "HC_M2_0606_P", - "sample_vals": "{\"C57BL/6J\":\"6.638\",\"DBA/2J\":\"6.266\",\"B6D2F1\":\"6.494\",\"D2B6F1\":\"6.565\",\"BXD1\":\"6.357\",\"BXD2\":\"6.456\",\"BXD5\":\"6.590\",\"BXD6\":\"6.568\",\"BXD8\":\"6.581\",\"BXD9\":\"6.322\",\"BXD11\":\"6.519\",\"BXD12\":\"6.543\",\"BXD13\":\"6.636\",\"BXD14\":\"x\",\"BXD15\":\"6.578\",\"BXD16\":\"6.636\",\"BXD18\":\"x\",\"BXD19\":\"6.562\",\"BXD20\":\"6.610\",\"BXD21\":\"6.668\",\"BXD22\":\"6.607\",\"BXD23\":\"6.513\",\"BXD24\":\"6.601\",\"BXD24a\":\"x\",\"BXD25\":\"x\",\"BXD27\":\"6.573\",\"BXD28\":\"6.639\",\"BXD29\":\"6.656\",\"BXD30\":\"x\",\"BXD31\":\"6.549\",\"BXD32\":\"6.502\",\"BXD33\":\"6.584\",\"BXD34\":\"6.261\",\"BXD35\":\"x\",\"BXD36\":\"x\",\"BXD37\":\"x\",\"BXD38\":\"6.646\",\"BXD39\":\"6.584\",\"BXD40\":\"6.790\",\"BXD41\":\"x\",\"BXD42\":\"6.536\",\"BXD43\":\"6.476\",\"BXD44\":\"6.545\",\"BXD45\":\"6.742\",\"BXD48\":\"6.393\",\"BXD48a\":\"6.618\",\"BXD49\":\"x\",\"BXD50\":\"6.496\",\"BXD51\":\"6.494\",\"BXD52\":\"x\",\"BXD53\":\"x\",\"BXD54\":\"x\",\"BXD55\":\"6.263\",\"BXD56\":\"x\",\"BXD59\":\"x\",\"BXD60\":\"6.541\",\"BXD61\":\"6.662\",\"BXD62\":\"6.628\",\"BXD63\":\"6.556\",\"BXD64\":\"6.572\",\"BXD65\":\"6.530\",\"BXD65a\":\"6.280\",\"BXD65b\":\"6.490\",\"BXD66\":\"6.608\",\"BXD67\":\"6.534\",\"BXD68\":\"6.352\",\"BXD69\":\"6.548\",\"BXD70\":\"6.520\",\"BXD71\":\"x\",\"BXD72\":\"x\",\"BXD73\":\"6.484\",\"BXD73a\":\"6.486\",\"BXD73b\":\"x\",\"BXD74\":\"6.639\",\"BXD75\":\"6.401\",\"BXD76\":\"6.452\",\"BXD77\":\"6.568\",\"BXD78\":\"x\",\"BXD79\":\"6.642\",\"BXD81\":\"x\",\"BXD83\":\"6.446\",\"BXD84\":\"6.582\",\"BXD85\":\"6.484\",\"BXD86\":\"6.877\",\"BXD87\":\"6.474\",\"BXD88\":\"x\",\"BXD89\":\"6.676\",\"BXD90\":\"6.644\",\"BXD91\":\"x\",\"BXD93\":\"6.620\",\"BXD94\":\"6.528\",\"BXD95\":\"x\",\"BXD98\":\"6.486\",\"BXD99\":\"6.530\",\"BXD100\":\"x\",\"BXD101\":\"x\",\"BXD102\":\"x\",\"BXD104\":\"x\",\"BXD105\":\"x\",\"BXD106\":\"x\",\"BXD107\":\"x\",\"BXD108\":\"x\",\"BXD109\":\"x\",\"BXD110\":\"x\",\"BXD111\":\"x\",\"BXD112\":\"x\",\"BXD113\":\"x\",\"BXD114\":\"x\",\"BXD115\":\"x\",\"BXD116\":\"x\",\"BXD117\":\"x\",\"BXD119\":\"x\",\"BXD120\":\"x\",\"BXD121\":\"x\",\"BXD122\":\"x\",\"BXD123\":\"x\",\"BXD124\":\"x\",\"BXD125\":\"x\",\"BXD126\":\"x\",\"BXD127\":\"x\",\"BXD128\":\"x\",\"BXD128a\":\"x\",\"BXD130\":\"x\",\"BXD131\":\"x\",\"BXD132\":\"x\",\"BXD133\":\"x\",\"BXD134\":\"x\",\"BXD135\":\"x\",\"BXD136\":\"x\",\"BXD137\":\"x\",\"BXD138\":\"x\",\"BXD139\":\"x\",\"BXD141\":\"x\",\"BXD142\":\"x\",\"BXD144\":\"x\",\"BXD145\":\"x\",\"BXD146\":\"x\",\"BXD147\":\"x\",\"BXD148\":\"x\",\"BXD149\":\"x\",\"BXD150\":\"x\",\"BXD151\":\"x\",\"BXD152\":\"x\",\"BXD153\":\"x\",\"BXD154\":\"x\",\"BXD155\":\"x\",\"BXD156\":\"x\",\"BXD157\":\"x\",\"BXD160\":\"x\",\"BXD161\":\"x\",\"BXD162\":\"x\",\"BXD165\":\"x\",\"BXD168\":\"x\",\"BXD169\":\"x\",\"BXD170\":\"x\",\"BXD171\":\"x\",\"BXD172\":\"x\",\"BXD173\":\"x\",\"BXD174\":\"x\",\"BXD175\":\"x\",\"BXD176\":\"x\",\"BXD177\":\"x\",\"BXD178\":\"x\",\"BXD180\":\"x\",\"BXD181\":\"x\",\"BXD183\":\"x\",\"BXD184\":\"x\",\"BXD186\":\"x\",\"BXD187\":\"x\",\"BXD188\":\"x\",\"BXD189\":\"x\",\"BXD190\":\"x\",\"BXD191\":\"x\",\"BXD192\":\"x\",\"BXD193\":\"x\",\"BXD194\":\"x\",\"BXD195\":\"x\",\"BXD196\":\"x\",\"BXD197\":\"x\",\"BXD198\":\"x\",\"BXD199\":\"x\",\"BXD200\":\"x\",\"BXD201\":\"x\",\"BXD202\":\"x\",\"BXD203\":\"x\",\"BXD204\":\"x\",\"BXD205\":\"x\",\"BXD206\":\"x\",\"BXD207\":\"x\",\"BXD208\":\"x\",\"BXD209\":\"x\",\"BXD210\":\"x\",\"BXD211\":\"x\",\"BXD212\":\"x\",\"BXD213\":\"x\",\"BXD214\":\"x\",\"BXD215\":\"x\",\"BXD216\":\"x\",\"BXD217\":\"x\",\"BXD218\":\"x\",\"BXD219\":\"x\",\"BXD220\":\"x\"}", - "corr_type": "lit", - "corr_dataset": "HC_M2_0606_P", - "corr_return_results": "100", - "corr_samples_group": "samples_primary", - "corr_sample_method": "pearson", - "min_expr": "", - "location_type": "gene", - "loc_chr": "", - "min_loc_mb": "", - "max_loc_mb": "", - "p_range_lower": "-0.60", - "p_range_upper": "0.74" -}
\ No newline at end of file diff --git a/tests/integration/expected_corr_results.json b/tests/integration/expected_corr_results.json deleted file mode 100644 index b5bbc2d..0000000 --- a/tests/integration/expected_corr_results.json +++ /dev/null @@ -1,1902 +0,0 @@ -[ - { - "index": 1, - "trait_id": "1415758_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415758_at:HC_M2_0606_P:da50fa1141a7d608ab20", - "symbol": "Fryl", - "description": "furry homolog-like; far 3' UTR", - "location": "Chr5: 72.964984", - "mean": "9.193", - "additive": "-0.081", - "lod_score": "4.4", - "lrs_location": "Chr1: 196.404284", - "sample_r": "-0.407", - "num_overlap": 67, - "sample_p": "6.234e-04", - "lit_corr": "--", - "tissue_corr": "-0.221", - "tissue_pvalue": "2.780e-01" - }, - { - "index": 2, - "trait_id": "1415693_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415693_at:HC_M2_0606_P:0959e913366f559ea22b", - "symbol": "Derl1", - "description": "derlin 1; proximal to mid 3' UTR", - "location": "Chr15: 57.702171", - "mean": "9.445", - "additive": "0.056", - "lod_score": "2.1", - "lrs_location": "Chr1: 193.731996", - "sample_r": "0.398", - "num_overlap": 67, - "sample_p": "8.614e-04", - "lit_corr": "--", - "tissue_corr": "0.114", - "tissue_pvalue": "5.800e-01" - }, - { - "index": 3, - "trait_id": "1415753_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415753_at:HC_M2_0606_P:d75ca42e7fa1613364bb", - "symbol": "Fam108a", - "description": "abhydrolase domain-containing protein FAM108A; last two exons and proximal 3' UTR", - "location": "Chr10: 80.046470", - "mean": "12.731", - "additive": "0.050", - "lod_score": "1.5", - "lrs_location": "ChrX: 103.404884", - "sample_r": "0.384", - "num_overlap": 67, - "sample_p": "1.344e-03", - "lit_corr": "--", - "tissue_corr": "0.108", - "tissue_pvalue": "5.990e-01" - }, - { - "index": 4, - "trait_id": "1415740_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415740_at:HC_M2_0606_P:755cdc41d0d50a03b647", - "symbol": "Psmc5", - "description": "protease (prosome, macropain) 26S subunit, ATPase 5; exons 7, 8, 9", - "location": "Chr11: 106.123450", - "mean": "12.424", - "additive": "0.059", - "lod_score": "2.6", - "lrs_location": "Chr9: 34.013550", - "sample_r": "0.364", - "num_overlap": 67, - "sample_p": "2.476e-03", - "lit_corr": "--", - "tissue_corr": "0.333", - "tissue_pvalue": "9.696e-02" - }, - { - "index": 5, - "trait_id": "1415757_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415757_at:HC_M2_0606_P:8bbf06aa2e3aa5530934", - "symbol": "Gbf1", - "description": "Golgi-specific brefeldin A-resistance factor 1; last exon and proximal 3' UTR", - "location": "Chr19: 46.360410", - "mean": "9.800", - "additive": "-0.062", - "lod_score": "2.0", - "lrs_location": "Chr17: 52.750885", - "sample_r": "0.363", - "num_overlap": 67, - "sample_p": "2.539e-03", - "lit_corr": "--", - "tissue_corr": "-0.059", - "tissue_pvalue": "7.741e-01" - }, - { - "index": 6, - "trait_id": "1415768_a_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415768_a_at:HC_M2_0606_P:5e67109eee04f5da3393", - "symbol": "Ube2r2", - "description": "ubiquitin-conjugating enzyme E2R 2", - "location": "Chr4: 41.137929", - "mean": "9.811", - "additive": "-0.087", - "lod_score": "3.3", - "lrs_location": "Chr12: 114.553844", - "sample_r": "-0.312", - "num_overlap": 67, - "sample_p": "1.019e-02", - "lit_corr": "--", - "tissue_corr": "-0.007", - "tissue_pvalue": "9.711e-01" - }, - { - "index": 7, - "trait_id": "1415670_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415670_at:HC_M2_0606_P:4f82d7374f29ebfacaaf", - "symbol": "Copg", - "description": "coatomer protein complex, subunit gamma 1; two of the three last exons and proximal 3' UTR", - "location": "Chr6: 87.859681", - "mean": "11.199", - "additive": "-0.113", - "lod_score": "3.7", - "lrs_location": "Chr1: 157.588921", - "sample_r": "0.305", - "num_overlap": 67, - "sample_p": "1.200e-02", - "lit_corr": "--", - "tissue_corr": "-0.405", - "tissue_pvalue": "4.032e-02" - }, - { - "index": 8, - "trait_id": "1415742_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415742_at:HC_M2_0606_P:b72a582a1f840a18c3e7", - "symbol": "Aup1", - "description": "ancient ubiquitous protein 1", - "location": "Chr6: 83.006784", - "mean": "9.529", - "additive": "-0.062", - "lod_score": "2.4", - "lrs_location": "Chr19: 16.955950", - "sample_r": "0.295", - "num_overlap": 67, - "sample_p": "1.523e-02", - "lit_corr": "--", - "tissue_corr": "-0.033", - "tissue_pvalue": "8.716e-01" - }, - { - "index": 9, - "trait_id": "1415743_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415743_at:HC_M2_0606_P:3187245a079e824b4236", - "symbol": "Hdac5", - "description": "histone deacetylase 5; last four exons", - "location": "Chr11: 102.057397", - "mean": "11.009", - "additive": "0.081", - "lod_score": "2.1", - "lrs_location": "Chr7: 125.263073", - "sample_r": "0.285", - "num_overlap": 67, - "sample_p": "1.950e-02", - "lit_corr": "--", - "tissue_corr": "0.005", - "tissue_pvalue": "9.823e-01" - }, - { - "index": 10, - "trait_id": "1415690_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415690_at:HC_M2_0606_P:603b215ede00b6fe1104", - "symbol": "Mrp127", - "description": "39S ribosomal protein L27, mitochondrial; last three exons", - "location": "Chr11: 94.517922", - "mean": "12.569", - "additive": "0.063", - "lod_score": "1.9", - "lrs_location": "Chr2: 164.779024", - "sample_r": "0.266", - "num_overlap": 67, - "sample_p": "2.986e-02", - "lit_corr": "--", - "tissue_corr": "--", - "tissue_pvalue": "--" - }, - { - "index": 11, - "trait_id": "1415727_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415727_at:HC_M2_0606_P:cb40b8cba0eee75781a6", - "symbol": "Apoa1bp", - "description": "apolipoprotein A-I binding protein; 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\ No newline at end of file diff --git a/tests/unit/correlation/__init__.py b/tests/unit/correlation/__init__.py deleted file mode 100644 index e69de29..0000000 --- a/tests/unit/correlation/__init__.py +++ /dev/null diff --git a/tests/unit/correlation/correlation_test_data.json b/tests/unit/correlation/correlation_test_data.json deleted file mode 100644 index 87d24e3..0000000 --- a/tests/unit/correlation/correlation_test_data.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "primary_samples": "C57BL/6J,DBA/2J,B6D2F1,D2B6F1,BXD1,BXD2,BXD5,BXD6,BXD8,BXD9,BXD11,BXD12,BXD13,BXD14,BXD15,BXD16,BXD18,BXD19,BXD20,BXD21,BXD22,BXD23,BXD24,BXD24a,BXD25,BXD27,BXD28,BXD29,BXD30,BXD31,BXD32,BXD33,BXD34,BXD35,BXD36,BXD37,BXD38,BXD39,BXD40,BXD41,BXD42,BXD43,BXD44,BXD45,BXD48,BXD48a,BXD49,BXD50,BXD51,BXD52,BXD53,BXD54,BXD55,BXD56,BXD59,BXD60,BXD61,BXD62,BXD63,BXD64,BXD65,BXD65a,BXD65b,BXD66,BXD67,BXD68,BXD69,BXD70,BXD71,BXD72,BXD73,BXD73a,BXD73b,BXD74,BXD75,BXD76,BXD77,BXD78,BXD79,BXD81,BXD83,BXD84,BXD85,BXD86,BXD87,BXD88,BXD89,BXD90,BXD91,BXD93,BXD94,BXD95,BXD98,BXD99,BXD100,BXD101,BXD102,BXD104,BXD105,BXD106,BXD107,BXD108,BXD109,BXD110,BXD111,BXD112,BXD113,BXD114,BXD115,BXD116,BXD117,BXD119,BXD120,BXD121,BXD122,BXD123,BXD124,BXD125,BXD126,BXD127,BXD128,BXD128a,BXD130,BXD131,BXD132,BXD133,BXD134,BXD135,BXD136,BXD137,BXD138,BXD139,BXD141,BXD142,BXD144,BXD145,BXD146,BXD147,BXD148,BXD149,BXD150,BXD151,BXD152,BXD153,BXD154,BXD155,BXD156,BXD157,BXD160,BXD161,BXD162,BXD165,BXD168,BXD169,BXD170,BXD171,BXD172,BXD173,BXD174,BXD175,BXD176,BXD177,BXD178,BXD180,BXD181,BXD183,BXD184,BXD186,BXD187,BXD188,BXD189,BXD190,BXD191,BXD192,BXD193,BXD194,BXD195,BXD196,BXD197,BXD198,BXD199,BXD200,BXD201,BXD202,BXD203,BXD204,BXD205,BXD206,BXD207,BXD208,BXD209,BXD210,BXD211,BXD212,BXD213,BXD214,BXD215,BXD216,BXD217,BXD218,BXD219,BXD220", - 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"corr_type": "lit", - "corr_dataset": "HC_M2_0606_P", - "corr_return_results": "100", - "corr_samples_group": "samples_primary", - "corr_sample_method": "pearson", - "min_expr": "", - "location_type": "gene", - "loc_chr": "", - "min_loc_mb": "", - "max_loc_mb": "", - "p_range_lower": "-0.60", - "p_range_upper": "0.74" -}
\ No newline at end of file diff --git a/tests/unit/correlation/dataset.json b/tests/unit/correlation/dataset.json deleted file mode 100644 index 8a53ed5..0000000 --- a/tests/unit/correlation/dataset.json +++ /dev/null @@ -1,64 +0,0 @@ -{ - "name":"HC_M2_0606_P", - "id":112, - "shortname":"Hippocampus M430v2 BXD 06/06 PDNN", - "fullname":"Hippocampus Consortium M430v2 (Jun06) PDNN", - "type":"ProbeSet", - "data_scale":"log2", - "search_fields":[ - "Name", - "Description", - "Probe_Target_Description", - "Symbol", - "Alias", - "GenbankId", - "UniGeneId", - "RefSeq_TranscriptId" - ], - "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", - "proteinid", - "uniprotid", - "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" - ], - "header_fields":[ - "Index", - "Record", - "Symbol", - "Description", - "Location", - "Mean", - "Max LRS", - "Max LRS Location", - "Additive Effect" - ], - "query_for_group":"\n SELECT\n InbredSet.Name, InbredSet.Id, InbredSet.GeneticType\n FROM\n InbredSet, ProbeSetFreeze, ProbeFreeze\n WHERE\n ProbeFreeze.InbredSetId = InbredSet.Id AND\n ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId AND\n ProbeSetFreeze.Name = \"HC_M2_0606_P\"\n ", - "tissue":"Hippocampus mRNA", - "group":"None", - "accession_id":"None", - "species":"None" -}
\ No newline at end of file diff --git a/tests/unit/correlation/expected_correlation_results.json b/tests/unit/correlation/expected_correlation_results.json deleted file mode 100644 index b5bbc2d..0000000 --- a/tests/unit/correlation/expected_correlation_results.json +++ /dev/null @@ -1,1902 +0,0 @@ -[ - { - "index": 1, - "trait_id": "1415758_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415758_at:HC_M2_0606_P:da50fa1141a7d608ab20", - "symbol": "Fryl", - "description": "furry homolog-like; far 3' UTR", - "location": "Chr5: 72.964984", - "mean": "9.193", - "additive": "-0.081", - "lod_score": "4.4", - "lrs_location": "Chr1: 196.404284", - "sample_r": "-0.407", - "num_overlap": 67, - "sample_p": "6.234e-04", - "lit_corr": "--", - "tissue_corr": "-0.221", - "tissue_pvalue": "2.780e-01" - }, - { - "index": 2, - "trait_id": "1415693_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415693_at:HC_M2_0606_P:0959e913366f559ea22b", - "symbol": "Derl1", - "description": "derlin 1; proximal to mid 3' UTR", - "location": "Chr15: 57.702171", - "mean": "9.445", - "additive": "0.056", - "lod_score": "2.1", - "lrs_location": "Chr1: 193.731996", - "sample_r": "0.398", - "num_overlap": 67, - "sample_p": "8.614e-04", - "lit_corr": "--", - "tissue_corr": "0.114", - "tissue_pvalue": "5.800e-01" - }, - { - "index": 3, - "trait_id": "1415753_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415753_at:HC_M2_0606_P:d75ca42e7fa1613364bb", - "symbol": "Fam108a", - "description": "abhydrolase domain-containing protein FAM108A; last two exons and proximal 3' UTR", - "location": "Chr10: 80.046470", - "mean": "12.731", - "additive": "0.050", - "lod_score": "1.5", - "lrs_location": "ChrX: 103.404884", - "sample_r": "0.384", - "num_overlap": 67, - "sample_p": "1.344e-03", - "lit_corr": "--", - "tissue_corr": "0.108", - "tissue_pvalue": "5.990e-01" - }, - { - "index": 4, - "trait_id": "1415740_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415740_at:HC_M2_0606_P:755cdc41d0d50a03b647", - "symbol": "Psmc5", - "description": "protease (prosome, macropain) 26S subunit, ATPase 5; exons 7, 8, 9", - "location": "Chr11: 106.123450", - "mean": "12.424", - "additive": "0.059", - "lod_score": "2.6", - "lrs_location": "Chr9: 34.013550", - "sample_r": "0.364", - "num_overlap": 67, - "sample_p": "2.476e-03", - "lit_corr": "--", - "tissue_corr": "0.333", - "tissue_pvalue": "9.696e-02" - }, - { - "index": 5, - "trait_id": "1415757_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415757_at:HC_M2_0606_P:8bbf06aa2e3aa5530934", - "symbol": "Gbf1", - "description": "Golgi-specific brefeldin A-resistance factor 1; last exon and proximal 3' UTR", - "location": "Chr19: 46.360410", - "mean": "9.800", - "additive": "-0.062", - "lod_score": "2.0", - "lrs_location": "Chr17: 52.750885", - "sample_r": "0.363", - "num_overlap": 67, - "sample_p": "2.539e-03", - "lit_corr": "--", - "tissue_corr": "-0.059", - "tissue_pvalue": "7.741e-01" - }, - { - "index": 6, - "trait_id": "1415768_a_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415768_a_at:HC_M2_0606_P:5e67109eee04f5da3393", - "symbol": "Ube2r2", - "description": "ubiquitin-conjugating enzyme E2R 2", - "location": "Chr4: 41.137929", - "mean": "9.811", - "additive": "-0.087", - "lod_score": "3.3", - "lrs_location": "Chr12: 114.553844", - "sample_r": "-0.312", - "num_overlap": 67, - "sample_p": "1.019e-02", - "lit_corr": "--", - "tissue_corr": "-0.007", - "tissue_pvalue": "9.711e-01" - }, - { - "index": 7, - "trait_id": "1415670_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415670_at:HC_M2_0606_P:4f82d7374f29ebfacaaf", - "symbol": "Copg", - "description": "coatomer protein complex, subunit gamma 1; two of the three last exons and proximal 3' UTR", - "location": "Chr6: 87.859681", - "mean": "11.199", - "additive": "-0.113", - "lod_score": "3.7", - "lrs_location": "Chr1: 157.588921", - "sample_r": "0.305", - "num_overlap": 67, - "sample_p": "1.200e-02", - "lit_corr": "--", - "tissue_corr": "-0.405", - "tissue_pvalue": "4.032e-02" - }, - { - "index": 8, - "trait_id": "1415742_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415742_at:HC_M2_0606_P:b72a582a1f840a18c3e7", - "symbol": "Aup1", - "description": "ancient ubiquitous protein 1", - "location": "Chr6: 83.006784", - "mean": "9.529", - "additive": "-0.062", - "lod_score": "2.4", - "lrs_location": "Chr19: 16.955950", - "sample_r": "0.295", - "num_overlap": 67, - "sample_p": "1.523e-02", - "lit_corr": "--", - "tissue_corr": "-0.033", - "tissue_pvalue": "8.716e-01" - }, - { - "index": 9, - "trait_id": "1415743_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415743_at:HC_M2_0606_P:3187245a079e824b4236", - "symbol": "Hdac5", - "description": "histone deacetylase 5; last four exons", - "location": "Chr11: 102.057397", - "mean": "11.009", - "additive": "0.081", - "lod_score": "2.1", - "lrs_location": "Chr7: 125.263073", - "sample_r": "0.285", - "num_overlap": 67, - "sample_p": "1.950e-02", - "lit_corr": "--", - "tissue_corr": "0.005", - "tissue_pvalue": "9.823e-01" - }, - { - "index": 10, - "trait_id": "1415690_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415690_at:HC_M2_0606_P:603b215ede00b6fe1104", - "symbol": "Mrp127", - "description": "39S ribosomal protein L27, mitochondrial; last three exons", - "location": "Chr11: 94.517922", - "mean": "12.569", - "additive": "0.063", - "lod_score": "1.9", - "lrs_location": "Chr2: 164.779024", - "sample_r": "0.266", - "num_overlap": 67, - "sample_p": "2.986e-02", - "lit_corr": "--", - "tissue_corr": "--", - "tissue_pvalue": "--" - }, - { - "index": 11, - "trait_id": "1415727_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415727_at:HC_M2_0606_P:cb40b8cba0eee75781a6", - "symbol": "Apoa1bp", - "description": "apolipoprotein A-I binding protein; exons 3 through 6", - "location": "Chr3: 87.860534", - "mean": "11.707", - "additive": "-0.076", - "lod_score": "2.8", - "lrs_location": "Chr3: 56.295375", - "sample_r": "0.263", - "num_overlap": 67, - "sample_p": "3.136e-02", - "lit_corr": "--", - "tissue_corr": "-0.535", - "tissue_pvalue": "4.841e-03" - }, - { - "index": 12, - "trait_id": "1415730_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415730_at:HC_M2_0606_P:a970e0610a56ac4aba27", - "symbol": "Cpsf7", - "description": "cleavage and polyadenylation specificity factor 7; distal 3' UTR (transQTL on Chr 4 in BXD eye data)", - "location": "Chr19: 10.621618", - "mean": "10.662", - "additive": "-0.048", - "lod_score": "2.1", - "lrs_location": "Chr1: 188.085707", - "sample_r": "-0.263", - "num_overlap": 67, - "sample_p": "3.164e-02", - "lit_corr": "--", - "tissue_corr": "--", - "tissue_pvalue": "--" - }, - { - "index": 13, - "trait_id": "1415741_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415741_at:HC_M2_0606_P:033752be361d32960c29", - "symbol": "Tmem165", - "description": "transmembrane protein 165; 3' UTR", - "location": "Chr5: 76.637708", - "mean": "10.974", - "additive": "0.048", - "lod_score": "2.0", - "lrs_location": "Chr4: 5.606394", - "sample_r": "-0.258", - "num_overlap": 67, - "sample_p": "3.489e-02", - "lit_corr": "--", - "tissue_corr": "0.271", - "tissue_pvalue": "1.812e-01" - }, - { - "index": 14, - "trait_id": "1415725_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415725_at:HC_M2_0606_P:fbd6458be4f8ccbf1dc0", - "symbol": "Rrn3", - "description": "RRN3 RNA polymerase I transcription factor homolog (yeast)", - "location": "Chr16: 13.814359", - "mean": "9.195", - "additive": "-0.085", - "lod_score": "2.8", - "lrs_location": "Chr1: 148.717644", - "sample_r": "0.256", - "num_overlap": 67, - "sample_p": "3.636e-02", - "lit_corr": "--", - "tissue_corr": "0.587", - "tissue_pvalue": "1.621e-03" - }, - { - "index": 15, - "trait_id": "1415717_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415717_at:HC_M2_0606_P:dd51438830e4033114f8", - "symbol": "Rnf220", - "description": "ring finger protein 220; mid 3' UTR", - "location": "Chr4: 116.944155", - "mean": "10.778", - "additive": "-0.084", - "lod_score": "2.4", - "lrs_location": "Chr4: 122.536808", - "sample_r": "0.242", - "num_overlap": 67, - "sample_p": "4.816e-02", - "lit_corr": "--", - "tissue_corr": "--", - "tissue_pvalue": "--" - }, - { - "index": 16, - "trait_id": "1415703_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415703_at:HC_M2_0606_P:51ee8e47654845a546f0", - "symbol": "Huwe1", - "description": "HECT, UBA and WWE domain containing 1; last 3 exons and proximal 3' UTR", - "location": "ChrX: 148.367136", - "mean": "11.335", - "additive": "-0.094", - "lod_score": "2.3", - "lrs_location": "Chr1: 135.891043", - "sample_r": "0.235", - "num_overlap": 67, - "sample_p": "5.541e-02", - "lit_corr": "--", - "tissue_corr": "0.528", - "tissue_pvalue": "5.576e-03" - }, - { - "index": 17, - "trait_id": "1415748_a_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415748_a_at:HC_M2_0606_P:749a2279081b54e89885", - "symbol": "Dctn5", - "description": "dynactin 5; last exon and proximal half of 3' UTR", - "location": "Chr7: 129.291923", - "mean": "11.250", - "additive": "0.071", - "lod_score": "3.4", - "lrs_location": "Chr5: 138.337847", - "sample_r": "0.230", - "num_overlap": 67, - "sample_p": "6.133e-02", - "lit_corr": "--", - "tissue_corr": "0.064", - "tissue_pvalue": "7.557e-01" - }, - { - "index": 18, - "trait_id": "1415706_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415706_at:HC_M2_0606_P:ddfffdb78d0ff84d6a1a", - "symbol": "Copa", - "description": "coatomer protein complex, subunit alpha; 3' UTR", - "location": "Chr1: 174.051912", - "mean": "12.577", - "additive": "-0.143", - "lod_score": "8.7", - "lrs_location": "Chr1: 172.981863", - "sample_r": "0.224", - "num_overlap": 67, - "sample_p": "6.829e-02", - "lit_corr": "--", - "tissue_corr": "-0.147", - "tissue_pvalue": "4.739e-01" - }, - { - "index": 19, - "trait_id": "1415696_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415696_at:HC_M2_0606_P:da00b2667d7c27dc76a2", - "symbol": "Sar1a", - "description": "SAR1 gene homolog A; 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last 3 exons and 3' UTR", - "location": "Chr2: 128.438499", - "mean": "9.180", - "additive": "-0.102", - "lod_score": "8.7", - "lrs_location": "Chr2: 125.304784", - "sample_r": "-0.210", - "num_overlap": 67, - "sample_p": "8.734e-02", - "lit_corr": "--", - "tissue_corr": "0.367", - "tissue_pvalue": "6.539e-02" - }, - { - "index": 23, - "trait_id": "1415712_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415712_at:HC_M2_0606_P:48e284402cb79a5fbede", - "symbol": "Zranb1", - "description": "zinc finger, RAN-binding domain containing 1 (ubiquitin thioesterase, TRAF-binding protein); far 3' UTR (M430AB control duplicate)", - "location": "Chr7: 140.175988", - "mean": "9.923", - "additive": "-0.079", - "lod_score": "2.8", - "lrs_location": "Chr5: 143.642242", - "sample_r": "-0.208", - "num_overlap": 67, - "sample_p": "9.125e-02", - "lit_corr": "--", - "tissue_corr": "-0.068", - "tissue_pvalue": "7.413e-01" - }, - { - "index": 24, - "trait_id": "1415674_a_at", - "dataset": "HC_M2_0606_P", - "hmac": "1415674_a_at:HC_M2_0606_P:c8e7fb1fcad21d73fcfd", - "symbol": "Trappc4", - "description": "trafficking protein particle complex 4; 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"mean": "7.105", - "additive": "0.070", - "lod_score": "3.9", - "lrs_location": "Chr5: 133.538653", - "sample_r": "-0.679", - "num_overlap": 67, - "sample_p": "2.771e-10", - "lit_corr": "--", - "tissue_corr": "-0.360", - "tissue_pvalue": "7.075e-02" - }, - { - "index": 7, - "trait_id": "1450540_x_at", - "dataset": "HC_M2_0606_P", - "hmac": "1450540_x_at:HC_M2_0606_P:0836630187705e0a98ed", - "symbol": "Krtap5-1", - "description": "keratin associated protein 5-1", - "location": "Chr7: 149.482282", - "mean": "7.584", - "additive": "0.059", - "lod_score": "2.2", - "lrs_location": "Chr5: 140.893042", - "sample_r": "-0.678", - "num_overlap": 67, - "sample_p": "2.828e-10", - "lit_corr": "--", - "tissue_corr": "-0.486", - "tissue_pvalue": "1.174e-02" - }, - { - "index": 8, - "trait_id": "1454403_at", - "dataset": "HC_M2_0606_P", - "hmac": "1454403_at:HC_M2_0606_P:d6625a44bb78d0c9a7bc", - "symbol": "Fgd5", - "description": "FYVE, RhoGEF and PH domain containing 5", - "location": "Chr6: 91.964079", - "mean": "6.447", - "additive": "-0.036", - "lod_score": "1.8", - "lrs_location": "Chr8: 7.701081", - "sample_r": "-0.669", - "num_overlap": 67, - "sample_p": "5.895e-10", - "lit_corr": "--", - "tissue_corr": "-0.209", - "tissue_pvalue": "3.062e-01" - }, - { - "index": 9, - "trait_id": "1444162_at", - "dataset": "HC_M2_0606_P", - "hmac": "1444162_at:HC_M2_0606_P:fea27946a4ed1ee2b47a", - "symbol": "Frs2", - "description": "fibroblast growth factor receptor substrate 2", - "location": "Chr10: 116.521472", - "mean": "5.677", - "additive": "-0.040", - "lod_score": "1.8", - "lrs_location": "Chr4: 66.843058", - "sample_r": "-0.666", - "num_overlap": 67, - "sample_p": "7.946e-10", - "lit_corr": "--", - "tissue_corr": "-0.241", - "tissue_pvalue": "2.352e-01" - }, - { - "index": 10, - "trait_id": "1451876_a_at", - "dataset": "HC_M2_0606_P", - "hmac": "1451876_a_at:HC_M2_0606_P:eb879785591c3c2addeb", - "symbol": "Trp63", - "description": "transformation related protein 63", - "location": "Chr16: 25.884897", - "mean": "6.207", - "additive": "0.059", - "lod_score": "2.0", - "lrs_location": "Chr9: 74.382952", - "sample_r": "-0.664", - "num_overlap": 67, - "sample_p": "8.743e-10", - "lit_corr": "--", - "tissue_corr": "-0.187", - "tissue_pvalue": "3.601e-01" - }, - { - "index": 11, - "trait_id": "1457795_at", - "dataset": "HC_M2_0606_P", - "hmac": "1457795_at:HC_M2_0606_P:617d62e702f1f04b065d", - "symbol": "Scamp4", - "description": "secretory carrier membrane protein 4", - "location": "Chr10: 80.076487", - "mean": "7.060", - "additive": "-0.042", - "lod_score": "2.4", - "lrs_location": "Chr11: 58.923978", - "sample_r": "-0.663", - "num_overlap": 67, - "sample_p": "9.806e-10", - "lit_corr": "--", - "tissue_corr": "-0.040", - "tissue_pvalue": "8.462e-01" - }, - { - "index": 12, - "trait_id": "1439472_at", - "dataset": "HC_M2_0606_P", - "hmac": "1439472_at:HC_M2_0606_P:f52f356bf5d00add1ba9", - "symbol": "Gcn1l1", - "description": "general control of amino-acid synthesis 1-like 1", - "location": "Chr5: 116.033483", - "mean": "7.325", - "additive": "0.058", - "lod_score": "3.1", - "lrs_location": "Chr1: 196.404284", - "sample_r": "-0.662", - "num_overlap": 67, - "sample_p": "1.075e-09", - "lit_corr": "--", - "tissue_corr": "-0.205", - "tissue_pvalue": "3.157e-01" - }, - { - "index": 13, - "trait_id": "1422074_at", - "dataset": "HC_M2_0606_P", - "hmac": "1422074_at:HC_M2_0606_P:4acd73cfd3d194327d79", - "symbol": "Cdx2", - "description": "caudal type homeo box 2", - "location": "Chr5: 148.113293", - "mean": "6.415", - "additive": "-0.037", - "lod_score": "1.8", - "lrs_location": "Chr2: 180.825581", - "sample_r": "-0.661", - "num_overlap": 67, - "sample_p": "1.140e-09", - "lit_corr": "--", - "tissue_corr": "0.002", - "tissue_pvalue": "9.926e-01" - }, - { - "index": 14, - "trait_id": "1429140_at", - "dataset": "HC_M2_0606_P", - "hmac": "1429140_at:HC_M2_0606_P:16116d150fd7a8d09687", - "symbol": "Spns3", - "description": "spinster homolog 3; exons 10, 12, and proximal 3' UTR", - "location": "Chr11: 72.311676", - "mean": "7.194", - "additive": "0.050", - "lod_score": "2.1", - "lrs_location": "Chr9: 69.810185", - "sample_r": "-0.661", - "num_overlap": 67, - "sample_p": "1.175e-09", - "lit_corr": "--", - "tissue_corr": "0.557", - "tissue_pvalue": "3.116e-03" - }, - { - "index": 15, - "trait_id": "1437477_at", - "dataset": "HC_M2_0606_P", - "hmac": "1437477_at:HC_M2_0606_P:990d16df933d7bb03428", - "symbol": "Lrrfip1", - "description": "leucine rich repeat (in FLII) interacting protein 1", - "location": "Chr1: 93.011523", - "mean": "7.597", - "additive": "0.068", - "lod_score": "2.3", - "lrs_location": "Chr5: 133.538653", - "sample_r": "-0.658", - "num_overlap": 67, - "sample_p": "1.393e-09", - "lit_corr": "--", - "tissue_corr": "0.132", - "tissue_pvalue": "5.204e-01" - }, - { - "index": 16, - "trait_id": "1440212_at", - "dataset": "HC_M2_0606_P", - "hmac": "1440212_at:HC_M2_0606_P:01ae82e4856177dd9d89", - "symbol": "Slc12a1", - "description": "solute carrier family 12, member 1", - "location": "Chr2: 124.990152", - "mean": "7.061", - "additive": "0.038", - "lod_score": "2.2", - "lrs_location": "Chr1: 193.731996", - "sample_r": "-0.655", - "num_overlap": 67, - "sample_p": "1.769e-09", - "lit_corr": "--", - "tissue_corr": "0.028", - "tissue_pvalue": "8.923e-01" - }, - { - "index": 17, - "trait_id": "1419755_at", - "dataset": "HC_M2_0606_P", - "hmac": "1419755_at:HC_M2_0606_P:15fea7c69b0d5faa1298", - "symbol": "Mfi2", - "description": "antigen p97 (melanoma associated) identified by monoclonal antibodies 133.2 and 96.5", - "location": "Chr16: 31.898518", - "mean": "6.697", - "additive": "-0.038", - "lod_score": "2.0", - "lrs_location": "Chr4: 50.881071", - "sample_r": "-0.654", - "num_overlap": 67, - "sample_p": "1.950e-09", - "lit_corr": "--", - "tissue_corr": "0.244", - "tissue_pvalue": "2.305e-01" - }, - { - "index": 18, - "trait_id": "1425457_a_at", - "dataset": "HC_M2_0606_P", - "hmac": "1425457_a_at:HC_M2_0606_P:669c485b158c0207026c", - "symbol": "Grb10", - "description": "growth factor receptor bound protein 10", - "location": "Chr11: 11.833500", - "mean": "6.515", - "additive": "0.081", - "lod_score": "3.9", - "lrs_location": "Chr5: 133.538653", - "sample_r": "-0.652", - "num_overlap": 67, - "sample_p": "2.295e-09", - "lit_corr": "--", - "tissue_corr": "-0.090", - "tissue_pvalue": "6.617e-01" - }, - { - "index": 19, - "trait_id": "1431329_at", - "dataset": "HC_M2_0606_P", - "hmac": "1431329_at:HC_M2_0606_P:a6df7ed818ea0042c550", - "symbol": "Nphp4", - "description": "nephronophthisis 4 (renal tubular development and function)", - "location": "Chr4: 151.863271", - "mean": "6.191", - "additive": "0.039", - "lod_score": "1.9", - "lrs_location": "ChrX: 112.637353", - "sample_r": "-0.652", - "num_overlap": 67, - "sample_p": "2.330e-09", - "lit_corr": "--", - "tissue_corr": "-0.104", - "tissue_pvalue": "6.144e-01" - }, - { - "index": 20, - "trait_id": "1443987_at", - "dataset": "HC_M2_0606_P", - "hmac": "1443987_at:HC_M2_0606_P:681e6c787b4d652d0c07", - "symbol": "Klhl18", - "description": "kelch-like 18 (Drosophila)", - "location": "Chr9: 110.330597", - "mean": "7.244", - "additive": "-0.070", - "lod_score": "2.1", - "lrs_location": "Chr15: 13.149248", - "sample_r": "-0.650", - "num_overlap": 67, - "sample_p": "2.561e-09", - "lit_corr": "--", - "tissue_corr": "-0.200", - "tissue_pvalue": "3.270e-01" - } -]
\ No newline at end of file diff --git a/tests/unit/correlation/test_correlation_computations.py b/tests/unit/correlation/test_correlation_computations.py deleted file mode 100644 index dbb2587..0000000 --- a/tests/unit/correlation/test_correlation_computations.py +++ /dev/null @@ -1,65 +0,0 @@ -"""module for testing correlation/correlation_computations""" - -import unittest -from gn3.correlation.correlation_computations import compute_correlation - - -# mock for calculating correlation function - -def mock_get_loading_page_data(initial_start_vars): - """function to mock filtering input""" - results = {'start_vars': - {'genofile': 'SAMPLE:X', 'dataset': 'HC_M2_0606_P', - 'sample_vals': '{"C57BL/6J":"7.197","DBA/2J":"7.148","B6D2F1":"6.999"}', - 'primary_samples': 'C57BL/6J,DBA/2J,B6D2F1', - 'n_samples': 3, - 'wanted_inputs': "sample_vals,dataset,genofile,primary_samples"}} - - return results - - -class MockCorrelationResults: - """mock class for CorrelationResults""" - - def __init__(self, start_vars): - for _key, value in start_vars.items(): - self.value = value - - self.assert_start_vars(start_vars) - - @staticmethod - def assert_start_vars(start_vars): - """assert data required is supplied""" - assert "wanted_inputs" in start_vars - - def do_correlation(self, start_vars): - """mock method for doing correlation""" - - return { - "results": "success" - } - - -class TestCorrelationUtility(unittest.TestCase): - """tests for correlation computations""" - - def test_compute_correlation(self): - """test function for doing correlation""" - - sample_vals = """{"C57BL/6J":"7.197","DBA/2J":"7.148","B6D2F1":"6.999"}""" - - correlation_input_data = { - "wanted_inputs": "sample_vals,dataset,genofile,primary_samples", - "genofile": "SAMPLE:X", - "dataset": "HC_M2_0606_P", - - "sample_vals": sample_vals, - "primary_samples": "C57BL/6J,DBA/2J,B6D2F1" - - } - correlation_results = compute_correlation( - correlation_input_data=correlation_input_data, - correlation_results=MockCorrelationResults) - results = {"results": "success"} - - self.assertEqual(results,correlation_results) diff --git a/tests/unit/correlation/test_show_corr_results.py b/tests/unit/correlation/test_show_corr_results.py deleted file mode 100644 index 4846f5e..0000000 --- a/tests/unit/correlation/test_show_corr_results.py +++ /dev/null @@ -1,226 +0,0 @@ -"""module contains code for testing creating show correlation object""" - -import unittest -import json -import os -from unittest import mock -from types import SimpleNamespace -from gn3.correlation.show_corr_results import CorrelationResults -from gn3.correlation.show_corr_results import get_header_fields -from gn3.correlation.show_corr_results import generate_corr_json -# pylint: disable=unused-argument - - - -class ObjectMixin: - """object for adding other methods""" - def __str__(self): - raise NotImplementedError - - def get_dict(self): - raise NotImplementedError - -class MockGroup(ObjectMixin): - """mock class for Group""" - - def __init__(self): - self.samplelist = "add a mock for this" - self.parlist = None - - self.filist = None - -class MockCreateTrait(ObjectMixin): - """mock class for create trait""" - - def __init__(self): - pass - - def get_dict(self): - """class for getting dict items""" - return self.__dict__ - - def __str__(self): - return self.__class__.__name__ - - -class MockCreateDataset: - """mock class for create dataset""" - - def __init__(self): - - self.group = MockGroup() - - def get_trait_data(self, sample_keys): - """method for getting trait data""" - raise NotImplementedError() - - def retrieve_genes(self, symbol): - """method for retrieving genes""" - raise NotImplementedError() - - -def file_path(relative_path): - """getting abs path for file """ - # adopted from github - dir_name = os.path.dirname(os.path.abspath(__file__)) - split_path = relative_path.split("/") - new_path = os.path.join(dir_name, *split_path) - return new_path - - -def create_trait(dataset="Temp", name=None, cellid=None): - """mock function for creating trait""" - return "trait results" - - -def create_dataset(dataset_name="Temp", dataset_type="Temp", group_name=None): - """mock function to create dataset """ - return "dataset results" - - -def get_species(self, start_vars): - """ - how this function works is that it sets the self.dataset and self.species and self.this_trait - """ - - with open(file_path("./dataset.json")) as dataset_file: - results = json.load(dataset_file) - self.dataset = SimpleNamespace(**results) - - with open(file_path("./group_data_test.json")) as group_file: - results = json.load(group_file) - self.group = SimpleNamespace(**results) - - self.dataset.group = self.group - - trait_dict = {'name': '1434568_at', 'dataset': self.dataset, 'cellid': None, - 'identification': 'un-named trait', 'haveinfo': True, 'sequence': None} - - trait_obj = SimpleNamespace(**trait_dict) - - self.this_trait = trait_obj - - self.species = "this species data" - - -class TestCorrelationResults(unittest.TestCase): - """unittests for Correlation Results""" - - def setUp(self): - - with open(file_path("./correlation_test_data.json")) as json_file: - self.correlation_data = json.load(json_file) - - def tearDown(self): - - self.correlation_data = "" - - def test_for_assertion(self): - """test for assertion failures""" - with self.assertRaises(AssertionError): - _corr_results_object = CorrelationResults(start_vars={}) - - @mock.patch("gn3.correlation.show_corr_results.CorrelationResults.process_samples") - def test_do_correlation(self, process_samples): - """test for doing correlation""" - process_samples.return_value = None - corr_object = CorrelationResults(start_vars=self.correlation_data) - - with self.assertRaises(Exception) as _error: - - # xtodo;to be completed - - _corr_results = corr_object.do_correlation(start_vars=self.correlation_data, - create_dataset=create_dataset, - create_trait=None, - get_species_dataset_trait=get_species) - - - - def test_get_header_fields(self): - expected = [ - ['Index', - 'Record', - 'Symbol', - 'Description', - 'Location', - 'Mean', - 'Sample rho', - 'N', - 'Sample p(rho)', - 'Lit rho', - 'Tissue rho', - 'Tissue p(rho)', - 'Max LRS', - 'Max LRS Location', - 'Additive Effect'], - - ['Index', - 'ID', - 'Location', - 'Sample r', - 'N', - 'Sample p(r)'] - - ] - result1 = get_header_fields("ProbeSet", "spearman") - result2 = get_header_fields("Other", "Other") - self.assertEqual(result1, expected[0]) - self.assertEqual(result2, expected[1]) - - - - @mock.patch("gn3.utility.hmac.data_hmac") - def test_generate_corr_json(self, mock_data_hmac): - mock_data_hmac.return_value = "hajsdiau" - - dataset = SimpleNamespace(**{"name": "the_name"}) - this_trait = SimpleNamespace(**{"name": "trait_test", "dataset": dataset}) - target_dataset = SimpleNamespace(**{"type": "Publish"}) - corr_trait_1 = SimpleNamespace(**{ - "name": "trait_1", - "dataset": SimpleNamespace(**{"name": "dataset_1"}), - "view": True, - "abbreviation": "T1", - "description_display": "Trait I description", - "authors": "JM J,JYEW", - "pubmed_id": "34n4nn31hn43", - "pubmed_text": "2016", - "pubmed_link": "https://www.load", - "lod_score": "", - "mean": "", - "LRS_location_repr": "BXBS", - "additive": "", - "sample_r": 10.5, - "num_overlap": 2, - "sample_p": 5 - - - - - }) - corr_results = [corr_trait_1] - - dataset_type_other = { - "location": "cx-3-4", - "sample_4": 12.32, - "num_overlap": 3, - "sample_p": 10.34 - } - - expected_results = '[{"index": 1, "trait_id": "trait_1", "dataset": "dataset_1", "hmac": "hajsdiau", "abbreviation_display": "T1", "description": "Trait I description", "mean": "N/A", "authors_display": "JM J,JYEW", "additive": "N/A", "pubmed_id": "34n4nn31hn43", "year": "2016", "lod_score": "N/A", "lrs_location": "BXBS", "sample_r": "10.500", "num_overlap": 2, "sample_p": "5.000e+00"}]' - - results1 = generate_corr_json(corr_results=corr_results, this_trait=this_trait, - dataset=dataset, target_dataset=target_dataset, for_api=True) - self.assertEqual(expected_results, results1) - - - def test_generate_corr_json_view_false(self): - trait = SimpleNamespace(**{"view": False}) - corr_results = [trait] - this_trait = SimpleNamespace(**{"name": "trait_test"}) - dataset = SimpleNamespace(**{"name": "the_name"}) - - results_where_view_is_false = generate_corr_json( - corr_results=corr_results, this_trait=this_trait, dataset={}, target_dataset={}, for_api=False) - self.assertEqual(results_where_view_is_false, "[]")
\ No newline at end of file diff --git a/tests/unit/utility/__init__.py b/tests/unit/utility/__init__.py deleted file mode 100644 index e69de29..0000000 --- a/tests/unit/utility/__init__.py +++ /dev/null diff --git a/tests/unit/utility/test_chunks.py b/tests/unit/utility/test_chunks.py deleted file mode 100644 index 7c42b44..0000000 --- a/tests/unit/utility/test_chunks.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Test chunking""" - -import unittest - -from gn3.utility.chunks import divide_into_chunks - - -class TestChunks(unittest.TestCase): - "Test Utility method for chunking" - def test_divide_into_chunks(self): - "Check that a list is chunked correctly" - self.assertEqual(divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 3), - [[1, 2, 7], [3, 22, 8], [5, 22, 333]]) - self.assertEqual(divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 4), - [[1, 2, 7], [3, 22, 8], [5, 22, 333]]) - self.assertEqual(divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 5), - [[1, 2], [7, 3], [22, 8], [5, 22], [333]]) - self.assertEqual(divide_into_chunks([], 5), - [[]]) diff --git a/tests/unit/utility/test_corr_result_helpers.py b/tests/unit/utility/test_corr_result_helpers.py deleted file mode 100644 index ce5891f..0000000 --- a/tests/unit/utility/test_corr_result_helpers.py +++ /dev/null @@ -1,35 +0,0 @@ -""" Test correlation helper methods """ - -import unittest -from gn3.utility.corr_result_helpers import normalize_values -from gn3.utility.corr_result_helpers import common_keys -from gn3.utility.corr_result_helpers import normalize_values_with_samples - - -class TestCorrelationHelpers(unittest.TestCase): - """Test methods for normalising lists""" - - def test_normalize_values(self): - """Test that a list is normalised correctly""" - self.assertEqual( - normalize_values([2.3, None, None, 3.2, 4.1, 5],\ - [3.4, 7.2, 1.3, None, 6.2, 4.1]), - ([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3) - ) - - def test_common_keys(self): - """Test that common keys are returned as a list""" - test_a = dict(BXD1=9.113, BXD2=9.825, BXD14=8.985, BXD15=9.300) - test_b = dict(BXD1=9.723, BXD3=9.825, BXD14=9.124, BXD16=9.300) - self.assertEqual(sorted(common_keys(test_a, test_b)), - ['BXD1', 'BXD14']) - - def test_normalize_values_with_samples(self): - """Test that a sample(dict) is normalised correctly""" - self.assertEqual( - normalize_values_with_samples( - dict(BXD1=9.113, BXD2=9.825, BXD14=8.985, - BXD15=9.300, BXD20=9.300), - dict(BXD1=9.723, BXD3=9.825, BXD14=9.124, BXD16=9.300)), - (({'BXD1': 9.113, 'BXD14': 8.985}, {'BXD1': 9.723, 'BXD14': 9.124}, 2)) - ) diff --git a/tests/unit/utility/test_hmac.py b/tests/unit/utility/test_hmac.py deleted file mode 100644 index eba25a3..0000000 --- a/tests/unit/utility/test_hmac.py +++ /dev/null @@ -1,51 +0,0 @@ -"""Test hmac utility functions""" -# pylint: disable-all -import unittest -from unittest import mock - -from gn3.utility.hmac import data_hmac -from gn3.utility.hmac import url_for_hmac -from gn3.utility.hmac import hmac_creation - - -class TestHmacUtil(): - """Test Utility method for hmac creation""" - - @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) - def test_hmac_creation(self): - """Test hmac creation with a utf-8 string""" - self.assertEqual(hmac_creation("ファイ"), "7410466338cfe109e946") - - @mock.patch("utility.hmac.app.config", - {'SECRET_HMAC_CODE': ('\x08\xdf\xfa\x93N\x80' - '\xd9\\H@\\\x9f`\x98d^' - '\xb4a;\xc6OM\x946a\xbc' - '\xfc\x80:*\xebc')}) - def test_hmac_creation_with_cookie(self): - """Test hmac creation with a cookie""" - cookie = "3f4c1dbf-5b56-4260-87d6-f35445bda37e:af4fcf5eace9e7c864ce" - uuid_, _, signature = cookie.partition(":") - self.assertEqual( - hmac_creation(uuid_), - "af4fcf5eace9e7c864ce") - - @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) - def test_data_hmac(self): - """Test data_hmac fn with a utf-8 string""" - self.assertEqual(data_hmac("ファイ"), "ファイ:7410466338cfe109e946") - - @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) - @mock.patch("utility.hmac.url_for") - def test_url_for_hmac_with_plain_url(self, mock_url): - """Test url_for_hmac without params""" - mock_url.return_value = "https://mock_url.com/ファイ/" - self.assertEqual(url_for_hmac("ファイ"), - "https://mock_url.com/ファイ/?hm=05bc39e659b1948f41e7") - - @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) - @mock.patch("utility.hmac.url_for") - def test_url_for_hmac_with_param_in_url(self, mock_url): - """Test url_for_hmac with params""" - mock_url.return_value = "https://mock_url.com/?ファイ=1" - self.assertEqual(url_for_hmac("ファイ"), - "https://mock_url.com/?ファイ=1&hm=4709c1708270644aed79") |