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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Breast_Cancer"
cohort = "GSE207847"
# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE207847"
# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE207847.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE207847.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE207847.csv"
json_path = "./output/preprocess/3/Breast_Cancer/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 mentioning "gene expression profile using Clariom D platform",
# this dataset contains gene expression data
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability & 2.2 Conversion Functions
# Trait (loco-regional recurrence time)
trait_row = 3
def convert_trait(val):
if not isinstance(val, str):
return None
val = val.split(': ')[-1].strip().upper()
if val == 'EARLY':
return 0 # < 2 years
elif val == 'INTERMEDIATE':
return 0.5 # 2-5 years
elif val == 'LATE':
return 1 # > 5 years
return None
# Age - Not available in characteristics
age_row = None
convert_age = None
# Gender - Constant value "female" for all samples
gender_row = None # Although present in row 1, it's constant
convert_gender = 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:
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
)
print("Preview of selected clinical features:")
print(preview_df(selected_clinical))
# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# The identifiers appear to be using TC (transcript cluster) format from Affymetrix Clariom arrays
# These are not standard gene symbols and will need to be mapped
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# Get gene mapping from annotation data
# 'ID' column contains probe IDs matching genetic_data
# 'gene_assignment' contains gene symbols in format "ID // SYMBOL // ..."
prob_col = 'ID'
gene_col = 'gene_assignment'
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
# Extract valid gene symbols
def extract_gene_symbol(text):
if not isinstance(text, str):
return None
parts = text.split('//')
if len(parts) >= 2:
symbol = parts[1].strip()
# Validate that it looks like a proper gene symbol
if len(symbol) > 0 and not symbol.startswith('---'):
return symbol
return None
# Update mapping before applying
mapping_data['Gene'] = mapping_data['Gene'].apply(extract_gene_symbol)
mapping_data = mapping_data.dropna()
# Apply mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview first few gene symbols
print("\nFirst few genes in mapped expression data:")
print(list(gene_data.index[:5]))
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_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
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=is_trait_available,
is_biased=trait_biased,
df=linked_data,
note="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."
)
# 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) |