p3/preprocess/Intellectual_Disability/code/GSE273850.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Intellectual_Disability"
cohort = "GSE273850"

# Input paths
in_trait_dir = "../DATA/GEO/Intellectual_Disability"
in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE273850"

# Output paths
out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE273850.csv"
out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE273850.csv"
out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE273850.csv"
json_path = "./output/preprocess/3/Intellectual_Disability/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)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Affymetrix array data indicates gene expression data is available
is_gene_available = True

# 2. Variable and Data Type Analysis

# 2.1 Row identifiers
trait_row = 0  # Genotype info in row 0 indicates T21 vs control 
gender_row = 1  # Sex info in row 1
age_row = None  # Age not available

# 2.2 Conversion functions
def convert_trait(value: str) -> int:
    """Convert T21 status to binary: 1 for T21, 0 for control"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 't21' in value:
        return 1
    elif 'euploid' in value:
        return 0
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 0 for female, 1 for male"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

def convert_age(value: str) -> float:
    """Placeholder function since age is not available"""
    return 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:
    clinical_features = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    preview = preview_df(clinical_features)
    print("Clinical features preview:", preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# The transcript IDs are in the format "TCxxxxxxxx.hg.1" which are not standard human gene symbols
# They appear to be transcript cluster identifiers that need mapping to gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())

# Look at general data statistics 
print("\nData shape:", gene_metadata.shape)

# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# 1. Identify columns for gene mapping
# Based on observation:
# - 'ID' column in gene annotation contains probe IDs like 'TC0100006437.hg.1'
# - Gene symbols are contained in 'SPOT_ID.1' column within RefSeq/ENSEMBL descriptions

# 2. Create gene mapping dataframe from the annotation data
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='SPOT_ID.1')

# 3. Apply gene mapping to convert probe-level data to gene expression data 
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the gene data
print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Get clinical features 
clinical_features = geo_select_clinical_features(
    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
)

# 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)

# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
    is_biased = True
    linked_data = None
else:
    # 4. Judge whether features are biased and remove biased demographic features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
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
)

# 6. Save the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)