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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Osteoporosis"
cohort = "GSE84500"
# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE84500"
# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE84500.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE84500.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE84500.csv"
json_path = "./output/preprocess/3/Osteoporosis/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)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
print(f"\n{row}:")
print(values)
# 1. Gene Expression Data Availability
# Yes, this is gene expression microarray data studying differentiation and gene regulation
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# Osteoporosis trait can be inferred from treatment condition in row 2
# BMP2+TGFB+IBMX treatment promotes osteogenic differentiation while others don't
def convert_trait(value: str) -> int:
if not value or ':' not in value:
return None
treatment = value.split(': ')[1].strip().lower()
# Treatment with BMP2+TGFB+IBMX promotes osteogenic differentiation
return 1 if treatment == 'bmp2+tgfb+ibmx' else 0
trait_row = 2
# Age and gender not available - these are cell line samples
age_row = None
gender_row = None
convert_age = None
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
# Since trait_row is not None, we extract clinical features
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 the processed clinical data
print(preview_df(clinical_features))
# Save clinical data
clinical_features.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Examine data structure
print("Data structure and head:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])
# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Based on the format of gene IDs like '1007_s_at', these appear to be Affymetrix probe IDs
# rather than human gene symbols, which would look like 'BRCA1', 'TP53', etc.
# Therefore, these IDs need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# 1. Identify mapping columns from gene annotation data
# Gene identifiers are in 'ID' column as probe IDs (e.g., '1007_s_at')
# Gene symbols are in 'Gene Symbol' column (e.g., 'DDR1')
# 2. Extract mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
# 3. Convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Print dimensions to verify the mapping
print("\nDimensions:")
print(f"Original probe data: {genetic_data.shape}")
print(f"After mapping to genes: {gene_data.shape}")
# Preview first few rows
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and information saving
note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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=trait_biased,
df=linked_data,
note=note
)
# 6. Save linked data only if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |