p3/preprocess/Schizophrenia/code/GSE119288.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Schizophrenia"
cohort = "GSE119288"

# Input paths
in_trait_dir = "../DATA/GEO/Schizophrenia"
in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE119288"

# Output paths
out_data_file = "./output/preprocess/3/Schizophrenia/GSE119288.csv"
out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE119288.csv"
out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE119288.csv"
json_path = "./output/preprocess/3/Schizophrenia/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
# Based on the background info, this dataset contains gene expression data from hiPSC-derived NPCs
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Looking at cell IDs in row 1, we can infer patient/control status
trait_row = 1
age_row = None  # No age information available
gender_row = None  # No gender information available

def convert_trait(value: str) -> Optional[int]:
    """Convert cell ID to binary trait status"""
    if not isinstance(value, str):
        return None
    # Extract value after colon and strip whitespace
    value = value.split(':')[1].strip()
    # VCAP appears to be a cancer cell line control
    # Numbered IDs are patient-derived cells
    if value == 'VCAP':
        return 0  # Control
    elif value.replace('-', '').replace('.', '').replace('A', '').isdigit():
        return 1  # Patient
    return None

def convert_age(value: str) -> Optional[float]:
    """Convert age value"""
    # No age data available
    return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender value"""
    # No gender data available
    return None

# 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
if trait_row is not None:
    selected_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 the data
    preview = preview_df(selected_clinical_df)
    print("Preview of clinical data:")
    print(preview)
    
    # Save to CSV
    selected_clinical_df.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])
requires_gene_mapping = True
# Extract gene annotation data with additional prefixes to catch platform annotations
prefixes = ['^', '!', '#', '!platform_table_begin']
gene_annotation = get_gene_annotation(soft_file_path)

# Look for platform annotation section
platform_annotation = None
with gzip.open(soft_file_path, 'rt') as f:
    content = f.read()
    platform_sections = re.split('!platform_table_begin|!platform_table_end', content)
    if len(platform_sections) > 1:
        platform_data = platform_sections[1].strip()
        platform_annotation = pd.read_csv(io.StringIO(platform_data), sep='\t')

if platform_annotation is not None:
    print("Platform annotation DataFrame preview:")
    print(preview_df(platform_annotation))
else:
    print("Platform annotation not found in SOFT file.")
    print("\nBasic gene annotation DataFrame preview:")
    print(preview_df(gene_annotation))
# Since proper gene mapping data is not available, preserve probe-level data for now
gene_data = genetic_data

# Save the probe expression data
gene_data.to_csv(out_gene_data_file)

# Preview the output data
print("Preview of gene expression data:")
print(preview_df(gene_data))
# 1. Skip gene symbol normalization since we're working with probe IDs
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape (probe-level):", gene_data.shape)

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data using probe-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    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 = "This dataset contains probe-level expression data since platform-specific gene mapping was not available. The data was preprocessed successfully but remains at probe level rather than gene level."
    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 and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)