p3/preprocess/Eczema/code/GSE123086.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE123086"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE123086"

# Output paths
out_data_file = "./output/preprocess/3/Eczema/GSE123086.csv"
out_gene_data_file = "./output/preprocess/3/Eczema/gene_data/GSE123086.csv"
out_clinical_data_file = "./output/preprocess/3/Eczema/clinical_data/GSE123086.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 background info, this is a microarray gene expression study
is_gene_available = True

# 2. Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    # Extract value after colon and convert to binary
    value = x.split(": ")[1].strip()
    if value == "ATOPIC_ECZEMA":
        return 1
    elif value == "HEALTHY_CONTROL":
        return 0
    return None

def convert_age(x):
    if pd.isna(x):
        return None
    try:
        # Extract numeric age value after colon
        age = int(x.split(": ")[1])
        return age
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    # Extract value after colon and convert to binary
    value = x.split(": ")[1].strip()
    if value.upper() == "FEMALE":
        return 0
    elif value.upper() == "MALE":
        return 1
    return None

# Find data rows in sample characteristics
trait_row = 1  # Primary diagnosis in row 1
age_row = 3    # Age appears in rows 3 and 4, but row 3 has more entries
gender_row = 2  # Sex information in row 2

# 3. Save metadata for initial filtering
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 = pd.DataFrame(clinical_data)
selected_clinical_df = geo_select_clinical_features(
    clinical_df,
    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("Preview of extracted clinical features:")
print(preview_df(selected_clinical_df))
selected_clinical_df.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])
# Examine gene identifiers - these appear to be numbers rather than standard gene symbols
# Numbers indicate probe or probe set IDs from a microarray platform
# Will need to be mapped to gene symbols
requires_gene_mapping = True
# Extract probe IDs and gene name mapping from SOFT file
gene_metadata = get_gene_annotation(soft_file)

# Print field information to check available gene identifiers
print("Sample of probe ID field:")
print(gene_metadata['ID'].head())
print("\nAll column names:")
print(list(gene_metadata.columns))
print("\nSample of gene metadata rows:")
pd.set_option('display.max_columns', None)
print(gene_metadata.head())
# Looking at the gene metadata, let's try to extract more information from the SOFT file
# Extract probe IDs and gene name mapping from SOFT file again, but this time don't filter out comment lines
gene_metadata = pd.read_csv(soft_file, compression='gzip', sep='\t', comment=None, on_bad_lines='skip')

# Get relevant columns for mapping
id_col = [col for col in gene_metadata.columns if 'ID_REF' in col or 'ID' in col][0]
gene_col = [col for col in gene_metadata.columns if 'GENE_SYMBOL' in col][0]

# Create mapping dataframe
mapping_df = gene_metadata[[id_col, gene_col]].copy()
mapping_df.columns = ['ID', 'Gene']  
mapping_df = mapping_df.dropna()

# Apply the mapping to convert probe-level data to gene-level data 
gene_data = apply_gene_mapping(genetic_df, mapping_df)

print("Gene data shape after mapping:", gene_data.shape)
print("\nPreview of first few genes and samples:")
print(gene_data.head().iloc[:, :5])

# Save the gene expression data 
gene_data.to_csv(out_gene_data_file)
# Extract gene annotation data using the library function
gene_metadata = get_gene_annotation(soft_file)

# Get column with ENTREZ_GENE_IDs to map to NCBI gene symbols
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'ENTREZ_GENE_ID') 

# Apply the mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

print("Gene data shape after mapping:", gene_data.shape)
print("\nPreview of first few genes and samples:")
print(gene_data.head().iloc[:, :5])

# Save the gene expression data
gene_data.to_csv(out_gene_data_file)
# Skip gene symbol normalization since we have no valid gene data
gene_data = pd.read_csv(out_gene_data_file, index_col=0)

if len(gene_data) == 0:
    # Load clinical data for validation
    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    # Check for biased features in clinical data
    trait_biased, clinical_df = judge_and_remove_biased_features(clinical_df, trait)
    
    validate_and_save_cohort_info(
        is_final=True, 
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,  # Genes exist but mapping failed
        is_trait_available=True,
        is_biased=trait_biased,
        df=clinical_df,
        note="Gene mapping failed - no valid gene expression data produced"
    )
else:
    # Original processing steps
    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)

    # 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)

    # Handle missing values
    linked_data = handle_missing_values(linked_data, trait)

    # Check for biased features
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # Final validation
    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 Eczema patient vs healthy control gene expression in CD4+ T cells"
    )

    # 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)
# 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 the series description mentioning gene expression microarray analysis, RNA extraction,
# and Agilent microarray processing, this dataset contains gene expression data
is_gene_available = True

# 2.1 Data Availability
# Trait (primary diagnosis) is in row 1
trait_row = 1

# Gender is in row 3 (and partly in row 2)
gender_row = 3  

# Age appears in rows 3 and 4
age_row = 3

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait values to binary (0: control, 1: case)"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip().upper()
    if "ATOPIC_ECZEMA" in value:
        return 1
    elif "HEALTHY_CONTROL" in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age values to continuous numbers"""
    if not isinstance(value, str) or not value.startswith('age: '):
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender values to binary (0: female, 1: male)"""
    if not isinstance(value, str) or not value.startswith('Sex: '):
        return None
    value = value.split(': ')[1].strip().upper()
    if value == 'FEMALE':
        return 0
    elif value == 'MALE':
        return 1
    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. Extract Clinical Features
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 and save clinical data
print("Preview of extracted clinical features:")
print(preview_df(selected_clinical_df))
selected_clinical_df.to_csv(out_clinical_data_file)
# Based on the preview data, we can see this dataset has trait data (0s and 1s), age data (numeric age values), and gender data (0s and 1s)
is_gene_available = True  # This is a GEO dataset so likely contains gene expression data

# Define conversion functions
def convert_trait(value):
    if pd.isna(value):
        return None
    try:
        val = float(value.split(":")[-1].strip() if ":" in value else value)
        return val  # Already binary (0/1) format 
    except:
        return None

def convert_age(value):
    if pd.isna(value):
        return None
    try:
        val = float(value.split(":")[-1].strip() if ":" in value else value)
        return val
    except:
        return None

def convert_gender(value): 
    if pd.isna(value):
        return None
    try:
        val = float(value.split(":")[-1].strip() if ":" in value else value)
        return val  # Already in binary format where 1=male, 0=female
    except:
        return None

# Identify row indices for each variable based on the data preview
trait_row = 0 
age_row = 1
gender_row = 2

# Initial filtering and 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)

# Extract clinical features since trait data is available
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 extracted features
    print("\nPreview of extracted clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save clinical data
    selected_clinical_df.to_csv(out_clinical_data_file)