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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Gaucher_Disease"
cohort = "GSE124283"
# Input paths
in_trait_dir = "../DATA/GEO/Gaucher_Disease"
in_cohort_dir = "../DATA/GEO/Gaucher_Disease/GSE124283"
# Output paths
out_data_file = "./output/preprocess/3/Gaucher_Disease/GSE124283.csv"
out_gene_data_file = "./output/preprocess/3/Gaucher_Disease/gene_data/GSE124283.csv"
out_clinical_data_file = "./output/preprocess/3/Gaucher_Disease/clinical_data/GSE124283.csv"
json_path = "./output/preprocess/3/Gaucher_Disease/cohort_info.json"
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")
# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
print(f"{row}:")
print(f" {values}")
print()
# 1. Gene Expression Data
# Since this is a microarray study of gene expression changes in skin fibroblasts,
# gene expression data should be available
is_gene_available = True
# 2. Variable Availability and Conversion Functions
# 2.1 Data rows
trait_row = 2 # 'condition' field contains disease status
gender_row = 3 # 'gender' field is available
age_row = None # Age information not available
# 2.2 Conversion Functions
def convert_trait(value: str) -> int:
"""Convert trait values to binary (0: control, 1: Gaucher)"""
if not value or "N/A" in value:
return None
value = value.split(": ")[1] if ": " in value else value
if "Control" in value:
return 0
elif "Gaucher" in value:
return 1
return None # Other conditions like NPC are not relevant
def convert_gender(value: str) -> int:
"""Convert gender values to binary (0: female, 1: male)"""
if not value or "N/A" in value:
return None
value = value.split(": ")[1] if ": " in value else value
if value == "K": # K likely means "kobieta" (female in Polish)
return 0
elif value == "M": # M likely means male
return 1
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. Clinical Feature Extraction
# Since trait_row is not None, we proceed with feature extraction
selected_clinical_df = geo_select_clinical_features(
clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the selected features
print("Preview of selected clinical features:")
print(preview_df(selected_clinical_df))
# Save to CSV
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These gene IDs look like human gene symbols (e.g., A1BG, A2M, AAAS, AACS etc.)
# Therefore no mapping is required
requires_gene_mapping = False
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
normalized_gene_data.to_csv(out_gene_data_file)
# Read the processed clinical data file
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# Validate data quality and save cohort info
note = "Gene expression data from fibroblasts of Gaucher disease patients and healthy controls. Also contains samples from NPC disease patients which were excluded from analysis."
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
)
# Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file)
else:
print(f"Dataset {cohort} did not pass quality validation and will not be saved.") |