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"Base Dataset class ..."
import math
import collections
from redis import Redis
from base import species
from utility import chunks
from .datasetgroup import DatasetGroup
from wqflask.database import database_connection
from utility.db_tools import escape, mescape, create_in_clause
from .utils import fetch_cached_results, cache_dataset_results
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, redis_conn=Redis()):
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.accession_id = None
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(redis_conn)
self.species = species.TheSpecies(self)
def as_dict(self):
return {
'name': self.name,
'shortname': self.shortname,
'fullname': self.fullname,
'type': self.type,
'data_scale': self.data_scale,
'group': self.group.name,
'accession_id': self.accession_id
}
def get_accession_id(self):
results = None
with database_connection() as conn, conn.cursor() as cursor:
if self.type == "Publish":
cursor.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,)
)
results = cursor.fetchone()
elif self.type == "Geno":
cursor.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,)
)
results = cursor.fetchone()
if results:
return str(results[0])
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.
"""
with database_connection() as conn, conn.cursor() as cursor:
try:
if self.type == "ProbeSet":
cursor.execute(
"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)",
(self.name,)*3)
(self.id, self.name, self.fullname, self.shortname,
self.data_scale, self.tissue) = cursor.fetchone()
else:
self.tissue = "N/A"
cursor.execute(
"SELECT Id, Name, FullName, ShortName "
f"FROM {self.type}Freeze "
"WHERE (Name = %s OR FullName = "
"%s OR ShortName = %s)",
(self.name,)*3)
(self.id, self.name, self.fullname,
self.shortname) = cursor.fetchone()
except TypeError:
pass
def chunk_dataset(self, dataset, n):
results = {}
traits_name_dict = ()
with database_connection() as conn, conn.cursor() as cursor:
cursor.execute(
"SELECT ProbeSetXRef.DataId,ProbeSet.Name "
"FROM ProbeSet, ProbeSetXRef, ProbeSetFreeze "
"WHERE ProbeSetFreeze.Name = %s AND "
"ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id "
"AND ProbeSetXRef.ProbeSetId = ProbeSet.Id",
(self.name,))
# should cache this
traits_name_dict = dict(cursor.fetchall())
for i in range(0, len(dataset), n):
matrix = list(dataset[i:i + n])
trait_name = traits_name_dict[matrix[0][0]]
my_values = [value for (trait_name, strain, value) in matrix]
results[trait_name] = my_values
return results
def get_probeset_data(self, sample_list=None, trait_ids=None):
# improvement of get trait data--->>>
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
with database_connection() as conn, conn.cursor() as cursor:
cursor.execute(
"SELECT Strain.Name, Strain.Id FROM "
"Strain, Species WHERE Strain.Name IN "
f"{create_in_clause(self.samplelist)} "
"AND Strain.SpeciesId=Species.Id AND "
"Species.name = %s", (self.group.species,)
)
results = dict(cursor.fetchall())
sample_ids = [results[item] for item in self.samplelist]
sorted_samplelist = [strain_name for strain_name, strain_id in sorted(
results.items(), key=lambda item: item[1])]
cursor.execute(
"SELECT * from ProbeSetData WHERE StrainID IN "
f"{create_in_clause(sample_ids)} AND id IN "
"(SELECT ProbeSetXRef.DataId FROM "
"(ProbeSet, ProbeSetXRef, ProbeSetFreeze) "
"WHERE ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id "
"AND ProbeSetFreeze.Name = %s AND "
"ProbeSet.Id = ProbeSetXRef.ProbeSetId)",
(self.name,)
)
query_results = list(cursor.fetchall())
data_results = self.chunk_dataset(query_results, len(sample_ids))
self.samplelist = sorted_samplelist
self.trait_data = data_results
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
with database_connection() as conn, conn.cursor() as cursor:
cursor.execute(
"SELECT Strain.Name, Strain.Id FROM Strain, Species "
f"WHERE Strain.Name IN {create_in_clause(self.samplelist)} "
"AND Strain.SpeciesId=Species.Id "
"AND Species.name = %s",
(self.group.species,)
)
results = dict(cursor.fetchall())
sample_ids = [
sample_id for sample_id in
(results.get(item) for item in self.samplelist
if item is not None)
if sample_id is not None
]
# 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))
cached_results = fetch_cached_results(self.name, self.type, self.samplelist)
if cached_results is None:
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
if len(temp) > 0:
query = 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))
cursor.execute(query)
results = cursor.fetchall()
trait_sample_data.append([list(result) for result in results])
trait_count = len(trait_sample_data[0])
self.trait_data = collections.defaultdict(list)
data_start_pos = 1
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:])
cache_dataset_results(
self.name, self.type, self.samplelist, self.trait_data)
else:
self.trait_data = cached_results
|
