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authorAlexander Kabui2021-05-24 16:35:11 +0300
committerBonfaceKilz2021-06-17 08:55:17 +0300
commit7cbf9f75ce3e7655f93dd5c3b975ae4430d567fb (patch)
tree5685bffb79336f98d7f83a1d4884611f3cd7a046
parenta7f2e8dea0a84a70aec29159a9ae35bd55ba047d (diff)
downloadgenenetwork2-7cbf9f75ce3e7655f93dd5c3b975ae4430d567fb.tar.gz
add query for fetching probeset data
-rw-r--r--wqflask/base/data_set.py258
-rw-r--r--wqflask/wqflask/correlation/correlation_gn3_api.py144
2 files changed, 303 insertions, 99 deletions
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index 75ddf278..10f0e110 100644
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -166,7 +166,6 @@ class DatasetType:
if t in ['pheno', 'other_pheno']:
group_name = name.replace("Publish", "")
-
results = g.db.execute(sql_query_mapping[t] % group_name).fetchone()
if results:
self.datasets[name] = dataset_name_mapping[t]
@@ -278,7 +277,7 @@ class Markers:
filtered_markers = []
for marker in self.markers:
if marker['name'] in p_values:
- #logger.debug("marker {} IS in p_values".format(i))
+ # logger.debug("marker {} IS in p_values".format(i))
marker['p_value'] = p_values[marker['name']]
if math.isnan(marker['p_value']) or (marker['p_value'] <= 0):
marker['lod_score'] = 0
@@ -299,7 +298,7 @@ class HumanMarkers(Markers):
self.markers = []
for line in marker_data_fh:
splat = line.strip().split()
- #logger.debug("splat:", splat)
+ # logger.debug("splat:", splat)
if len(specified_markers) > 0:
if splat[1] in specified_markers:
marker = {}
@@ -441,7 +440,7 @@ class DatasetGroup:
# genotype_1 is Dataset Object without parents and f1
# genotype_2 is Dataset Object with parents and f1 (not for intercross)
- #genotype_1 = reaper.Dataset()
+ # genotype_1 = reaper.Dataset()
# reaper barfs on unicode filenames, so here we ensure it's a string
if self.genofile:
@@ -650,9 +649,39 @@ class DataSet:
- def get_trait_data(self, sample_list=None):
+
+ def chunk_dataset(self, dataset, n):
+
+
+ results = {}
+
+ query = """
+ SELECT ProbeSetXRef.DataId,ProbeSet.Name
+ FROM ProbeSet, ProbeSetXRef, ProbeSetFreeze
+ WHERE ProbeSetFreeze.Name = '{}' AND
+ ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND
+ ProbeSetXRef.ProbeSetId = ProbeSet.Id
+ """.format(self.name)
+
+ # should cache this
+
+ traits_name_dict= dict(g.db.execute(query).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):
if sample_list:
self.samplelist = sample_list
+
else:
self.samplelist = self.group.samplelist
@@ -666,27 +695,59 @@ class DataSet:
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]
+ query = """SELECT * from ProbeSetData
+ where StrainID in {}
+ and id in (SELECT ProbeSetXRef.DataId
+ FROM (ProbeSet, ProbeSetXRef, ProbeSetFreeze)
+ WHERE ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id
+ and ProbeSetFreeze.Name = '{}'
+ and ProbeSet.Id = ProbeSetXRef.ProbeSetId)""".format(create_in_clause(sample_ids),self.name)
+
+ query_results=list(g.db.execute(query).fetchall())
+
+ data_results=self.chunk_dataset(query_results, len(sample_ids))
+ 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
+
+ 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 = []
+ 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"
+ dataset_type="Phenotype"
else:
- dataset_type = self.type
- temp = ['T%s.value' % item for item in sample_ids_step]
+ 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))
+ query="SELECT {}XRef.Id,".format(escape(self.type))
else:
- query = "SELECT {}.Name,".format(escape(dataset_type))
- data_start_pos = 1
+ query="SELECT {}.Name,".format(escape(dataset_type))
+ data_start_pos=1
query += ', '.join(temp)
query += ' FROM ({}, {}XRef, {}Freeze) '.format(*mescape(dataset_type,
self.type,
@@ -715,27 +776,27 @@ class DataSet:
""".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()
+ results=g.db.execute(query).fetchall()
trait_sample_data.append(results)
- trait_count = len(trait_sample_data[0])
- self.trait_data = collections.defaultdict(list)
+ 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]
+ trait_name=trait_sample_data[0][trait_counter][0]
for chunk_counter in range(int(number_chunks)):
self.trait_data[trait_name] += (
trait_sample_data[chunk_counter][trait_counter][data_start_pos:])
class PhenotypeDataSet(DataSet):
- DS_NAME_MAP['Publish'] = 'PhenotypeDataSet'
+ DS_NAME_MAP['Publish']='PhenotypeDataSet'
def setup(self):
# Fields in the database table
- self.search_fields = ['Phenotype.Post_publication_description',
+ self.search_fields=['Phenotype.Post_publication_description',
'Phenotype.Pre_publication_description',
'Phenotype.Pre_publication_abbreviation',
'Phenotype.Post_publication_abbreviation',
@@ -748,7 +809,7 @@ class PhenotypeDataSet(DataSet):
'PublishXRef.Id']
# Figure out what display_fields is
- self.display_fields = ['name', 'group_code',
+ self.display_fields=['name', 'group_code',
'pubmed_id',
'pre_publication_description',
'post_publication_description',
@@ -766,7 +827,7 @@ class PhenotypeDataSet(DataSet):
'sequence', 'units', 'comments']
# Fields displayed in the search results table header
- self.header_fields = ['Index',
+ self.header_fields=['Index',
'Record',
'Description',
'Authors',
@@ -775,9 +836,9 @@ class PhenotypeDataSet(DataSet):
'Max LRS Location',
'Additive Effect']
- self.type = 'Publish'
+ self.type='Publish'
- self.query_for_group = '''
+ self.query_for_group='''
SELECT
InbredSet.Name, InbredSet.Id, InbredSet.GeneticType
FROM
@@ -797,13 +858,13 @@ class PhenotypeDataSet(DataSet):
if not this_trait.haveinfo:
this_trait.retrieve_info(get_qtl_info=True)
- description = this_trait.post_publication_description
+ 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 = ""
+ this_trait.description_display=""
continue # for now, because no authorization features
if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(
@@ -811,46 +872,46 @@ class PhenotypeDataSet(DataSet):
userName=self.userName,
authorized_users=this_trait.authorized_users):
- description = this_trait.pre_publication_description
+ description=this_trait.pre_publication_description
if len(description) > 0:
- this_trait.description_display = description.strip()
+ this_trait.description_display=description.strip()
else:
- this_trait.description_display = ""
+ this_trait.description_display=""
if not this_trait.year.isdigit():
- this_trait.pubmed_text = "N/A"
+ this_trait.pubmed_text="N/A"
else:
- this_trait.pubmed_text = this_trait.year
+ this_trait.pubmed_text=this_trait.year
if this_trait.pubmed_id:
- this_trait.pubmed_link = webqtlConfig.PUBMEDLINK_URL % 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"
+ this_trait.LRS_score_repr="N/A"
+ this_trait.LRS_location_repr="N/A"
if this_trait.lrs:
- query = """
+ 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)
logger.sql(query)
- result = g.db.execute(query).fetchone()
+ result=g.db.execute(query).fetchone()
if result:
if result[0] and result[1]:
- LRS_Chr = result[0]
- LRS_Mb = 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' % (
+ 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 = """
+ query="""
SELECT
Strain.Name, PublishData.value, PublishSE.error, NStrain.count, Strain.Name2
FROM
@@ -868,34 +929,34 @@ class PhenotypeDataSet(DataSet):
Strain.Name
"""
logger.sql(query)
- results = g.db.execute(query, (trait, self.id)).fetchall()
+ results=g.db.execute(query, (trait, self.id)).fetchall()
return results
class GenotypeDataSet(DataSet):
- DS_NAME_MAP['Geno'] = 'GenotypeDataSet'
+ DS_NAME_MAP['Geno']='GenotypeDataSet'
def setup(self):
# Fields in the database table
- self.search_fields = ['Name',
+ self.search_fields=['Name',
'Chr']
# Find out what display_fields is
- self.display_fields = ['name',
+ self.display_fields=['name',
'chr',
'mb',
'source2',
'sequence']
# Fields displayed in the search results table header
- self.header_fields = ['Index',
+ self.header_fields=['Index',
'ID',
'Location']
# Todo: Obsolete or rename this field
- self.type = 'Geno'
+ self.type='Geno'
- self.query_for_group = '''
+ self.query_for_group='''
SELECT
InbredSet.Name, InbredSet.Id, InbredSet.GeneticType
FROM
@@ -914,11 +975,11 @@ class GenotypeDataSet(DataSet):
this_trait.retrieveInfo()
if this_trait.chr and this_trait.mb:
- this_trait.location_repr = 'Chr%s: %.6f' % (
+ this_trait.location_repr='Chr%s: %.6f' % (
this_trait.chr, float(this_trait.mb))
def retrieve_sample_data(self, trait):
- query = """
+ query="""
SELECT
Strain.Name, GenoData.value, GenoSE.error, "N/A", Strain.Name2
FROM
@@ -935,7 +996,7 @@ class GenotypeDataSet(DataSet):
Strain.Name
"""
logger.sql(query)
- results = g.db.execute(query,
+ results=g.db.execute(query,
(webqtlDatabaseFunction.retrieve_species_id(self.group.name),
trait, self.name)).fetchall()
return results
@@ -949,11 +1010,11 @@ class MrnaAssayDataSet(DataSet):
platform and is far too specific.
'''
- DS_NAME_MAP['ProbeSet'] = 'MrnaAssayDataSet'
+ DS_NAME_MAP['ProbeSet']='MrnaAssayDataSet'
def setup(self):
# Fields in the database table
- self.search_fields = ['Name',
+ self.search_fields=['Name',
'Description',
'Probe_Target_Description',
'Symbol',
@@ -963,7 +1024,7 @@ class MrnaAssayDataSet(DataSet):
'RefSeq_TranscriptId']
# Find out what display_fields is
- self.display_fields = ['name', 'symbol',
+ self.display_fields=['name', 'symbol',
'description', 'probe_target_description',
'chr', 'mb',
'alias', 'geneid',
@@ -983,7 +1044,7 @@ class MrnaAssayDataSet(DataSet):
'flag']
# Fields displayed in the search results table header
- self.header_fields = ['Index',
+ self.header_fields=['Index',
'Record',
'Symbol',
'Description',
@@ -994,9 +1055,9 @@ class MrnaAssayDataSet(DataSet):
'Additive Effect']
# Todo: Obsolete or rename this field
- self.type = 'ProbeSet'
+ self.type='ProbeSet'
- self.query_for_group = '''
+ self.query_for_group='''
SELECT
InbredSet.Name, InbredSet.Id, InbredSet.GeneticType
FROM
@@ -1014,7 +1075,7 @@ class MrnaAssayDataSet(DataSet):
# Note: setting trait_list to [] is probably not a great idea.
if not trait_list:
- trait_list = []
+ trait_list=[]
for this_trait in trait_list:
@@ -1022,33 +1083,33 @@ class MrnaAssayDataSet(DataSet):
this_trait.retrieveInfo(QTL=1)
if not this_trait.symbol:
- this_trait.symbol = "N/A"
+ this_trait.symbol="N/A"
# XZ, 12/08/2008: description
# XZ, 06/05/2009: Rob asked to add probe target description
- description_string = str(
+ description_string=str(
str(this_trait.description).strip(codecs.BOM_UTF8), 'utf-8')
- target_string = str(
+ 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
+ description_display=description_string
else:
- description_display = this_trait.symbol
+ 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()
+ description_display=description_display + '; ' + target_string.strip()
# Save it for the jinja2 template
- this_trait.description_display = description_display
+ this_trait.description_display=description_display
if this_trait.chr and this_trait.mb:
- this_trait.location_repr = 'Chr%s: %.6f' % (
+ this_trait.location_repr='Chr%s: %.6f' % (
this_trait.chr, float(this_trait.mb))
# Get mean expression value
- query = (
+ query=(
"""select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet
where ProbeSetXRef.ProbeSetFreezeId = %s and
ProbeSet.Id = ProbeSetXRef.ProbeSetId and
@@ -1056,44 +1117,45 @@ class MrnaAssayDataSet(DataSet):
""" % (escape(str(this_trait.dataset.id)),
escape(this_trait.name)))
- #logger.debug("query is:", pf(query))
+ # logger.debug("query is:", pf(query))
logger.sql(query)
- result = g.db.execute(query).fetchone()
+ result=g.db.execute(query).fetchone()
- mean = result[0] if result else 0
+ mean=result[0] if result else 0
if mean:
- this_trait.mean = "%2.3f" % 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'
+ 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 = """
+ 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()
+ 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, 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 = """
+ query="""
SELECT
Strain.Name, ProbeSetData.value, ProbeSetSE.error, NStrain.count, Strain.Name2
FROM
- (ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef)
+ (ProbeSetData, ProbeSetFreeze,
+ Strain, ProbeSet, ProbeSetXRef)
left join ProbeSetSE on
(ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId)
left join NStrain on
@@ -1109,19 +1171,19 @@ class MrnaAssayDataSet(DataSet):
Strain.Name
""" % (escape(trait), escape(self.name))
logger.sql(query)
- results = g.db.execute(query).fetchall()
- #logger.debug("RETRIEVED RESULTS HERE:", results)
+ results=g.db.execute(query).fetchall()
+ # logger.debug("RETRIEVED RESULTS HERE:", results)
return results
def retrieve_genes(self, column_name):
- query = """
+ 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()
+ results=g.db.execute(query).fetchall()
return dict(results)
@@ -1129,40 +1191,40 @@ class MrnaAssayDataSet(DataSet):
class TempDataSet(DataSet):
'''Temporary user-generated data set'''
- DS_NAME_MAP['Temp'] = 'TempDataSet'
+ DS_NAME_MAP['Temp']='TempDataSet'
def setup(self):
- self.search_fields = ['name',
+ self.search_fields=['name',
'description']
- self.display_fields = ['name',
+ self.display_fields=['name',
'description']
- self.header_fields = ['Name',
+ self.header_fields=['Name',
'Description']
- self.type = 'Temp'
+ self.type='Temp'
# Need to double check later how these are used
- self.id = 1
- self.fullname = 'Temporary Storage'
- self.shortname = 'Temp'
+ self.id=1
+ self.fullname='Temporary Storage'
+ self.shortname='Temp'
def geno_mrna_confidentiality(ob):
- dataset_table = ob.type + "Freeze"
- #logger.debug("dataset_table [%s]: %s" % (type(dataset_table), dataset_table))
+ dataset_table=ob.type + "Freeze"
+ # logger.debug("dataset_table [%s]: %s" % (type(dataset_table), dataset_table))
- query = '''SELECT Id, Name, FullName, confidentiality,
+ query='''SELECT Id, Name, FullName, confidentiality,
AuthorisedUsers FROM %s WHERE Name = "%s"''' % (dataset_table, ob.name)
logger.sql(query)
- result = g.db.execute(query)
+ result=g.db.execute(query)
(dataset_id,
name,
full_name,
confidential,
- authorized_users) = result.fetchall()[0]
+ authorized_users)=result.fetchall()[0]
if confidential:
return True
diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py
index 6974dbd5..3e1ce1dc 100644
--- a/wqflask/wqflask/correlation/correlation_gn3_api.py
+++ b/wqflask/wqflask/correlation/correlation_gn3_api.py
@@ -27,6 +27,34 @@ def create_target_this_trait(start_vars):
return (this_dataset, this_trait, target_dataset, sample_data)
+
+def test_process_data(this_trait,dataset,start_vars):
+ """test function for bxd,all and other sample data"""
+
+ corr_samples_group = start_vars["corr_samples_group"]
+
+
+ primary_samples = dataset.group.samplelist
+ if dataset.group.parlist != None:
+ primary_samples += dataset.group.parlist
+ if dataset.group.f1list != None:
+ primary_samples += dataset.group.f1list
+
+ # If either BXD/whatever Only or All Samples, append all of that group's samplelist
+ if corr_samples_group != 'samples_other':
+ sample_data = process_samples(start_vars, primary_samples)
+
+ # If either Non-BXD/whatever or All Samples, get all samples from this_trait.data and
+ # exclude the primary samples (because they would have been added in the previous
+ # if statement if the user selected All 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 (
+ dataset.group.parlist + dataset.group.f1list)]
+ sample_data = process_samples(start_vars, list(this_trait.data.keys()), primary_samples)
+
+ return sample_data
+
def process_samples(start_vars, sample_names, excluded_samples=None):
"""process samples"""
sample_data = {}
@@ -118,13 +146,22 @@ def fetch_sample_data(start_vars, this_trait, this_dataset, target_dataset):
sample_data = process_samples(
start_vars, this_dataset.group.samplelist)
- target_dataset.get_trait_data(list(sample_data.keys()))
+
+ # sample_data = test_process_data(this_trait,this_dataset,start_vars)
+
+ if target_dataset.type =="ProbeSet":
+ # pass
+ target_dataset.get_probeset_data(list(sample_data.keys()))
+ else:
+ target_dataset.get_trait_data(list(sample_data.keys()))
this_trait = retrieve_sample_data(this_trait, this_dataset)
this_trait_data = {
"trait_sample_data": sample_data,
"trait_id": start_vars["trait_id"]
}
+ # should remove this len(samplelist) == len(strain_values)
+
results = map_shared_keys_to_values(
target_dataset.samplelist, target_dataset.trait_data)
@@ -201,6 +238,7 @@ def compute_correlation(start_vars, method="pearson"):
"target_dataset": start_vars['corr_dataset'],
"return_results": corr_return_results}
+
return correlation_data
@@ -261,3 +299,107 @@ def get_tissue_correlation_input(this_trait, trait_symbol_dict):
}
return (primary_tissue_data, target_tissue_data)
return None
+
+
+def generate_corr_data(corr_results, target_dataset):
+ counter = 0
+ results_list = []
+ for (index, trait_corr) in enumerate(corr_results):
+ trait_name = list(trait_corr.keys())[0]
+ trait = create_trait(dataset=target_dataset,
+ name=trait_name)
+
+ trait_corr_data = trait_corr[trait_name]
+
+ if trait.view == False:
+ continue
+ results_dict = {}
+ results_dict['index'] = index + 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_corr_data.get('sample_r',0)):.3f}"
+ results_dict['num_overlap'] = trait.num_overlap
+ results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}"
+ results_dict['lit_corr'] = "--"
+ results_dict['tissue_corr'] = "--"
+ results_dict['tissue_pvalue'] = "--"
+ tissue_corr = trait_corr_data.get('tissue_corr',0)
+ lit_corr = trait_corr_data.get('lit_corr',0)
+ if bool(lit_corr):
+ results_dict['lit_corr'] = f"{float(trait_corr_data.get('lit_corr',0)):.3f}"
+ if bool(tissue_corr):
+ results_dict['tissue_corr'] = f"{float(trait_corr_data.get('tissue_corr',0)):.3f}"
+ results_dict['tissue_pvalue'] = f"{float(trait_corr_data.get('tissue_pvalue',0)):.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_corr_data.get('sample_r',0)):.3f}"
+ results_dict['num_overlap'] = trait.num_overlap
+ results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}"
+ else:
+ results_dict['location'] = trait.location_repr
+ results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}"
+ results_dict['num_overlap'] = trait.num_overlap
+ results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}"
+
+ results_list.append(results_dict)
+
+ return results_list
+
+