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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Osteoporosis"
cohort = "GSE152073"
# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE152073"
# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE152073.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE152073.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE152073.csv"
json_path = "./output/preprocess/3/Osteoporosis/cohort_info.json"
# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
print(f"\n{row}:")
print(values)
# 1. Gene Expression Data
# Yes - Based on background info mentioning Affymetrix microarrays and gene expression data
is_gene_available = True
# 2. Variable Availability and Conversion Functions
# 2.1 Row Identifiers
trait_row = 0 # Inferred from background info stating all subjects have osteoporosis
age_row = 1 # Age data in row 1
gender_row = 0 # Gender data in row 0
# 2.2 Conversion Functions
def convert_trait(x):
# All subjects have osteoporosis based on study design
return 1
def convert_age(x):
try:
# Extract numeric age value after colon
age = int(x.split(': ')[1])
return age
except:
return None
def convert_gender(x):
try:
gender = x.split(': ')[1].lower()
if gender == 'female':
return 0
elif gender == 'male':
return 1
return None
except:
return None
# 3. Save Metadata
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=trait_row is not None
)
# 4. Clinical Feature Extraction
if trait_row is not None:
clinical_features = geo_select_clinical_features(
clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
age_row=age_row,
convert_age=convert_age,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the extracted features
preview = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview)
# Save to CSV
clinical_features.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
def get_genetic_data_modified(file_path: str, marker: str = "!series_matrix_table_begin") -> pd.DataFrame:
with gzip.open(file_path, 'rt') as file:
for i, line in enumerate(file):
if marker in line:
skip_rows = i + 1
break
else:
raise ValueError(f"Marker '{marker}' not found in the file.")
genetic_data = pd.read_csv(file_path, compression='gzip', skiprows=skip_rows, comment='!',
delimiter='\t', on_bad_lines='skip').T
genetic_data.columns = genetic_data.iloc[0] # Set first row as column names
genetic_data = genetic_data.iloc[1:] # Remove the first row
return genetic_data
genetic_data = get_genetic_data_modified(matrix_file_path)
# Print outputs to examine structure
print("Data structure and head:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
print("\nFirst 20 column names (probe identifiers):")
print(list(genetic_data.columns)[:20])
print("\nFirst 5 row names (sample IDs):")
print(list(genetic_data.index)[:5])
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