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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Depression"
cohort = "GSE208668"
# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE208668"
# Output paths
out_data_file = "./output/preprocess/3/Depression/GSE208668.csv"
out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE208668.csv"
out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE208668.csv"
json_path = "./output/preprocess/3/Depression/cohort_info.json"
# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values for each feature (row) in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# From the background info, this dataset contains transcriptome data from PBMCs
# However, it mentions raw data was lost, so gene expression data is not available
is_gene_available = False
# 2.1 Data Availability
# Depression trait can be inferred from "history of depression" field (key 9)
trait_row = 9
# Age is available in key 1
age_row = 1
# Gender is available in key 2
gender_row = 2
# 2.2 Data Type Conversion Functions
def convert_trait(x):
if not isinstance(x, str):
return None
x = x.lower().strip()
if 'history of depression:' not in x:
return None
value = x.split(':')[1].strip()
if value == 'yes':
return 1
elif value == 'no':
return 0
return None
def convert_age(x):
if not isinstance(x, str):
return None
if 'age:' not in x:
return None
try:
return float(x.split(':')[1].strip())
except:
return None
def convert_gender(x):
if not isinstance(x, str):
return None
if 'gender:' not in x:
return None
value = x.split(':')[1].strip().lower()
if value == 'female':
return 0
elif value == 'male':
return 1
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
# Since trait_row is not None, extract clinical features
clinical_df = geo_select_clinical_features(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 and save clinical data
print("Preview of clinical data:")
print(preview_df(clinical_df))
clinical_df.to_csv(out_clinical_data_file) |