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Downloaded data from API, formatted code
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@KajusC
KajusC committed Aug 28, 2024
1 parent 5a23fea commit 4d3575a
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7 changes: 6 additions & 1 deletion api/data/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,9 @@
LOVD_TABLES_DATA_TYPES,

# Paths for database downloads
DATABASES_DOWNLOAD_PATHS
DATABASES_DOWNLOAD_PATHS,

GNOMAD_PATH,
)

# DATA COLLECTION IMPORT
Expand Down Expand Up @@ -57,4 +59,7 @@
from_clinvar_name_to_cdna_position,
save_lovd_as_vcf,
request_gnomad_api_data,
merge_gnomad_lovd,
parse_gnomad,
set_gnomad_dtypes,
)
110 changes: 56 additions & 54 deletions api/data/refactoring.py
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Original file line number Diff line number Diff line change
Expand Up @@ -247,7 +247,18 @@ def save_lovd_as_vcf(data, save_to="./lovd.vcf"):
f.write("\n")


def request_gnomad_api_data(gene_name, to_file=True):
def process_population_data(df, pop_data, name, pop_ids, index):
for pop_id in pop_ids:
df.loc[index, f'{name}_ac_{pop_id}'] = 0
df.loc[index, f'{name}_an_{pop_id}'] = 0
if isinstance(pop_data, list):
for pop in pop_data:
variant_id = pop['id']
df.loc[index, f'{name}_ac_{variant_id}'] = pop['ac']
df.loc[index, f'{name}_an_{variant_id}'] = pop['an']


def request_gnomad_api_data(gene_name):
"""
Requests gnomAD API for data about a specific gene containing:
- variant_id
Expand Down Expand Up @@ -305,44 +316,38 @@ def request_gnomad_api_data(gene_name, to_file=True):
}}
"""

response = requests.post(url, json={'query': query})
if response.status_code == 200:
data = response.json()['data']['gene']['variants']

df = pd.json_normalize(data)

df['total_ac'] = df['exome.ac'].fillna(0) + df['genome.ac'].fillna(0)
df['total_an'] = df['exome.an'].fillna(0) + df['genome.an'].fillna(0)

df['cDNA change'] = df['hgvsc'].fillna(0)
df['Protein change'] = df['hgvsp'].fillna(0)

df['Allele Frequency'] = df['total_ac'] / df['total_an']
df['Homozygote Count'] = df['exome.ac_hom'].fillna(0) + df['genome.ac_hom'].fillna(0)
exome_populations = df['exome.populations']
genome_populations = df['genome.populations']
ids = ['afr', 'eas', 'asj', 'sas', 'nfe', 'fin', 'mid', 'amr', 'ami', 'remaining']

def process_population_data(pop_data, name, pop_ids, index):
for pop_id in pop_ids:
df.loc[index, f'{name}_ac_{pop_id}'] = 0
df.loc[index, f'{name}_an_{pop_id}'] = 0
if type(pop_data) == list:
for pop in pop_data:
id = pop['id']
df.loc[index, f'{name}_ac_{id}'] = pop['ac']
df.loc[index, f'{name}_an_{id}'] = pop['an']

for i in range(len(exome_populations)):
exome_pop = exome_populations[i]
process_population_data(exome_pop, 'exome', ids, i)
genome_pop = genome_populations[i]
process_population_data(genome_pop, 'genome', ids, i)

for id in ids:
df[f'Allele_Frequency_{id}'] = (df[f'exome_ac_{id}'].fillna(0) + df[f'genome_ac_{id}'].fillna(0)) / (
df[f'exome_an_{id}'].fillna(0) + df[f'genome_an_{id}'].fillna(0))
population_mapping = {
response = requests.post(url, json={'query': query}, timeout=300)# timeout set to 5 minutes

if response.status_code != 200:
print('Error:', response.status_code)
return None

data = response.json()['data']['gene']['variants']

df = pd.json_normalize(data)

df['total_ac'] = df['exome.ac'].fillna(0) + df['genome.ac'].fillna(0)
df['total_an'] = df['exome.an'].fillna(0) + df['genome.an'].fillna(0)

df['HGVS Consequence'] = df['hgvsc'].fillna(0) # cDNA change
df['Protein Consequence'] = df['hgvsp'].fillna(0) # Protein change

df['Allele Frequency'] = df['total_ac'] / df['total_an']
df['Homozygote Count'] = df['exome.ac_hom'].fillna(0) + df['genome.ac_hom'].fillna(0)
exome_populations = df['exome.populations']
genome_populations = df['genome.populations']
ids = ['afr', 'eas', 'asj', 'sas', 'nfe', 'fin', 'mid', 'amr', 'ami', 'remaining']

for i in range(len(exome_populations)):
exome_pop = exome_populations[i]
process_population_data(df, exome_pop, 'exome', ids, i)
genome_pop = genome_populations[i]
process_population_data(df, genome_pop, 'genome', ids, i)

for variant_id in ids:
df[f'Allele_Frequency_{variant_id}'] = (df[f'exome_ac_{variant_id}'].fillna(0) + df[f'genome_ac_{variant_id}'].fillna(0)) / (
df[f'exome_an_{variant_id}'].fillna(0) + df[f'genome_an_{variant_id}'].fillna(0))
population_mapping = {
'afr': 'African/African American',
'eas': 'East Asian',
'asj': 'Ashkenazi Jew',
Expand All @@ -355,22 +360,19 @@ def process_population_data(pop_data, name, pop_ids, index):
'remaining': 'Remaining',
'': ''
}
for i in range(len(df)):
max = 0
maxid = ''
for id in ids:
if df.loc[i, f'Allele_Frequency_{id}'] > max:
max = df.loc[i, f'Allele_Frequency_{id}']
maxid = id
df.loc[i, 'Popmax'] = max
df.loc[i, 'Popmax population'] = population_mapping[maxid]
not_to_drop = ['Popmax', 'Popmax population', 'Homozygote Count', 'Allele Frequency', 'variant_id',
for i in range(len(df)):
max_pop = 0
maxid = ''
for variant_id in ids:
if df.loc[i, f'Allele_Frequency_{variant_id}'] > max_pop:
max_pop = df.loc[i, f'Allele_Frequency_{variant_id}']
maxid = variant_id
df.loc[i, 'Popmax'] = max_pop
df.loc[i, 'Popmax population'] = population_mapping[maxid]
not_to_drop = ['Popmax', 'Popmax population', 'Homozygote Count', 'Allele Frequency', 'variant_id',
'cDNA change', 'Protein change']
df = df.drop([col for col in df.columns if col not in not_to_drop], axis=1)
if to_file:
df.to_csv('variants.csv', index=True)
df = df.drop([col for col in df.columns if col not in not_to_drop], axis=1)

else:
print('Error:', response.status_code)
df.rename(columns={'variant_id': 'gnomAD ID'}, inplace=True)

return df
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