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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Hypertrophic_Cardiomyopathy"
cohort = "GSE36961"
# Input paths
in_trait_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy"
in_cohort_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy/GSE36961"
# Output paths
out_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/GSE36961.csv"
out_gene_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv"
out_clinical_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv"
json_path = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/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)
# Get unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Series title and summary indicate this is transcriptome profiling data
is_gene_available = True
# 2.1 Data Availability
# Trait is in row 3, gender in row 0, age in row 1
trait_row = 3 # disease state / sample type shows HCM vs control
gender_row = 0 # Sex field
age_row = 1 # age (yrs) field
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert trait value to binary (1=case, 0=control)"""
if pd.isna(value):
return None
value = value.split(": ")[-1].lower()
if "hypertrophic cardiomyopathy" in value or "hcm" in value or "case" in value:
return 1
elif "control" in value:
return 0
return None
def convert_age(value: str) -> float:
"""Convert age value to continuous numeric"""
if pd.isna(value):
return None
try:
return float(value.split(": ")[-1])
except:
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary (0=female, 1=male)"""
if pd.isna(value):
return None
value = value.split(": ")[-1].lower()
if value == "female":
return 0
elif value == "male":
return 1
return None
# 3. Save Metadata
# trait_row is not None, so trait data is available
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=is_trait_available)
# 4. Extract Clinical Features
selected_clinical = 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 clinical data
preview_result = preview_df(selected_clinical)
# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
requires_gene_mapping = False
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)
# Get clinical features
clinical_features = geo_select_clinical_features(
clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
gender_row=gender_row,
convert_gender=convert_gender
)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
note = "Dataset contains gene expression data comparing cardiac tissue from patients with hypertrophic cardiomyopathy (HCM) versus control donor cardiac tissues."
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=True,
is_biased=is_biased,
df=linked_data,
note=note
)
# 6. Save the linked data only if it's usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |