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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Melanoma"
cohort = "GSE215868"
# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE215868"
# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE215868.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE215868.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE215868.csv"
json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
print(f"\n{feature}:")
print(values)
# 1. Gene Expression Data Availability
# Based on background info, this is a gene expression dataset studying melanoma outcomes
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Trait data is available from "long-term benefit" field in key 4
trait_row = 4
# Age data is available in key 0
age_row = 0
# Gender data not available
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
"""Convert long-term benefit to binary (0=NO, 1=YES)"""
if pd.isna(value):
return None
value = value.split(": ")[-1]
if value == "YES":
return 1
elif value == "NO":
return 0
return None
def convert_age(value: str) -> Optional[float]:
"""Convert age to continuous numeric value"""
if pd.isna(value):
return None
try:
return float(value.split(": ")[-1])
except:
return None
def convert_gender(value: str) -> Optional[int]:
"""Placeholder function since gender data not available"""
return None
# 3. Save metadata
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
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 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)
# List all files to check and get matrix file content
print("All files in directory:")
files = os.listdir(in_cohort_dir)
for f in files:
print(f)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs (gene/probe identifiers)
print("\nFirst 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# Since we found standard gene symbols in the matrix file,
# we need to revise the earlier conclusion about methylation data
is_gene_available = True
# Save updated metadata with corrected gene availability info
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)
)
# These appear to be standard human gene symbols (HGNC symbols)
# Examples like A2M, ABCF1, AKT1 are well-known human gene symbols
# No mapping needed as they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=True,
is_biased=trait_biased,
df=linked_data,
note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."
)
# 6. Save linked data if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |