p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE113842.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autism_spectrum_disorder_(ASD)"
cohort = "GSE113842"

# Input paths
in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE113842"

# Output paths
out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE113842.csv"
out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE113842.csv"
out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE113842.csv"
json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/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
is_gene_available = True  # RNA expression data present based on series title and characteristics

# 2. Variable Availability and Data Type Conversion
# Trait data available in group field (row 0)
trait_row = 0
def convert_trait(value: str) -> int:
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if 'CTRL' in value:
        return 0
    elif 'ASD' in value:
        return 1
    return None

# Age data available (row 2)
age_row = 2 
def convert_age(value: str) -> float:
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if '/' in value:  # Handle ranges like "20/21"
        ages = value.split('/')
        return sum(float(age) for age in ages) / len(ages)
    return float(value)

# Gender data not available
gender_row = None
def convert_gender(value: str) -> int:
    return None  # Not used but defined for consistency

# 3. Save initial 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. Extract clinical features
clinical_df = 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 and save clinical data
print("Clinical data preview:")
print(preview_df(clinical_df))
clinical_df.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 and some data preview to verify structure
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))

print("\nData preview:")
preview_subset = genetic_data.iloc[:5, :5]
print(preview_subset)
# These gene identifiers appear to be Affymetrix probe IDs rather than human gene symbols
# They follow the format of Affymetrix _at, _s_at, _x_at probe identifiers
# Will need to map these probe IDs to gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Preview the gene annotation dataframe
print("\nGene annotation preview showing ID and Gene Symbol columns:")
preview = preview_df(gene_metadata[['ID', 'Gene Symbol']])
print(preview)
# Print all column names in gene_metadata to find correct identifier columns
print("Available columns in gene_metadata:")
print(gene_metadata.columns.tolist())

# Print sample from genetic_data to verify probe format
print("\nSample probe IDs from genetic data:")
print(genetic_data.index[:5].tolist())

# Create gene mapping dataframe with appropriate probe ID and gene symbol columns
mapping_data = gene_metadata.loc[:, ['ID_REF', 'Gene Symbol']]
mapping_data = mapping_data.rename(columns={'ID_REF': 'ID'})
mapping_data = mapping_data.dropna()

# Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview result
print("\nGene expression data preview:")
print(preview_df(gene_data))

# Save gene expression data if successful mapping
if len(gene_data) > 0:
    gene_data.to_csv(out_gene_data_file)
# Print sample IDs from both datasets to understand the format
print("Expression data probe IDs:")
print(list(genetic_data.index[:5]))
print("\nAnnotation data probe IDs:")
print(gene_metadata['ID'].head().tolist())

# Create gene mapping dataframe with appropriate probe ID and gene symbol columns
# First get relevant columns
mapping_data = gene_metadata[['ID', 'Gene Symbol']].copy()

# Print unique patterns in probe IDs to understand the structure
print("\nUnique patterns in expression data probes:")
print(set([x.split('_', 1)[1] for x in genetic_data.index[:20]]))
print("\nUnique patterns in annotation probes:")
print(set([x.split('_', 1)[1] for x in mapping_data['ID'].head(20)]))

# Transform annotation IDs to match expression data format
# Remove "_PM" and add "117" prefix to match expression data format
mapping_data['ID'] = mapping_data['ID'].str.replace('_PM', '')
mapping_data['ID'] = '117' + mapping_data['ID'].str.split('_').str[0].str[2:] + '_' + mapping_data['ID'].str.split('_').str[1]

# Create mapping using transformed IDs
mapping_data = get_gene_mapping(mapping_data, 'ID', 'Gene Symbol')

# Print sample of transformed mapping to verify
print("\nTransformed mapping sample:")
print(mapping_data.head().to_dict('records'))

# Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview result
print("\nGene expression data preview:")
print(preview_df(gene_data))

# Save gene expression data if successful mapping
if len(gene_data) > 0:
    gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
genetic_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_data)

# Add verification step to debug linking
print("\nSample verification:")
print("Clinical data columns:", clinical_df.index.tolist())
print("Clinical data shape:", clinical_df.shape)
print("Genetic data shape:", genetic_data.shape)
print("Linked data shape:", linked_data.shape)

# 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 brain tissue. Sample size adequate for analysis."
)

# 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)