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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Eczema"
cohort = "GSE32924"
# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE32924"
# Output paths
out_data_file = "./output/preprocess/3/Eczema/GSE32924.csv"
out_gene_data_file = "./output/preprocess/3/Eczema/gene_data/GSE32924.csv"
out_clinical_data_file = "./output/preprocess/3/Eczema/clinical_data/GSE32924.csv"
json_path = "./output/preprocess/3/Eczema/cohort_info.json"
# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values for each feature (row) in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on Series_title and Series_summary mentioning genomic profiling, this is likely a gene expression dataset
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Identify rows containing clinical data
trait_row = 2 # condition: ANL/AL/Normal indicates eczema status
age_row = None # No age data available
gender_row = None # No gender data available
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
"""Convert trait value to binary (0: No eczema, 1: Has eczema)"""
if not value or ":" not in value:
return None
value = value.split(":")[1].strip()
if value == "Normal":
return 0
elif value in ["ANL", "AL"]: # Both are eczema cases
return 1
return None
def convert_age(value: str) -> Optional[float]:
return None # Not used since age data is not available
def convert_gender(value: str) -> Optional[int]:
return None # Not used since gender data is not available
# 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:
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 extracted features
print("Preview of clinical features:")
print(preview_df(clinical_features))
# Save to CSV
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)
# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])
print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# These identifiers appear to be Affymetrix probe IDs (e.g. '1007_s_at', '1053_at')
# rather than human gene symbols. They need to be mapped to standard gene symbols.
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file)
# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Identify columns for mapping
# The 'ID' column in annotation contains probe IDs matching gene_data's index
# The 'Gene Symbol' column contains the human gene symbols we want to map to
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# 2&3. Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_df, mapping_df)
# Print preview of mapped gene data
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nPreview of gene expression data after mapping:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and metadata saving
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="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
)
# 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) |