p3/preprocess/COVID-19/code/GSE213313.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "COVID-19"
cohort = "GSE213313"

# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE213313"

# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE213313.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE213313.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE213313.csv"
json_path = "./output/preprocess/3/COVID-19/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
# Yes, this is microarray analysis of whole blood RNA samples according to background info
is_gene_available = True

# 2.1 Row Identifiers
trait_row = 2  # severity info in row 2
age_row = None  # age not available in characteristics
gender_row = None  # gender not available in characteristics

# 2.2 Conversion Functions
def convert_trait(value: str) -> Optional[float]:
    if not value or ':' not in value:
        return None
    severity = value.split(':')[1].strip().lower()
    if severity == 'critical':
        return 1.0  # More severe
    elif severity == 'non-critical':
        return 0.0  # Less severe
    return None  # Healthy controls excluded

def convert_age(value: str) -> Optional[float]:
    return None  # Not used since age data unavailable

def convert_gender(value: str) -> Optional[float]:
    return None  # Not used since gender data unavailable

# 3. Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Extract Clinical Features
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,  # clinical_data from previous step
        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 processed data
    preview_df(selected_clinical_df)
    
    # Save to CSV
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
is_gene_available = True

# Save updated 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)
)

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
# Based on the gene identifiers like 'A_19_P00315452', these appear to be Agilent array probes
# rather than standard human gene symbols. They need to be mapped to gene symbols.
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)

# This is human gene data with proper annotations
is_gene_available = True

# Save updated 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)
)
# Inspect the gene annotation data and identify relevant columns
# 'ID' contains probe IDs matching gene expression data
# 'GENE_SYMBOL' contains the target gene symbols
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')

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

# Preview result
print("\nGene expression data preview:")
print(gene_data.head())
print("\nShape:", gene_data.shape)

# Save updated 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)
)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Print diagnostic information
print("\nDiagnostic Information:")
print("Clinical features shape:", clinical_features.shape) 
print("Normalized gene data shape:", normalized_gene_data.shape)
print("\nSample of clinical feature IDs:", clinical_features.columns[:5].tolist())
print("Sample of genetic data IDs:", normalized_gene_data.columns[:5].tolist())

# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)

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

# Print linked data info
print("\nLinked data shape before bias check:", linked_data.shape)
print("Columns in linked data:", linked_data.columns[:5].tolist())

# 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="Dataset contains whole blood transcriptomic data comparing critical vs non-critical COVID-19 patients."
)

# 6. Save linked data if usable
if is_usable:
    print("\nSaving linked data with shape:", linked_data.shape)
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
print("Please provide the output from the previous step containing sample characteristics and background information to proceed with data availability assessment and feature extraction.")
raise ValueError("Missing required input from previous step - cannot determine data availability without sample characteristics dictionary")
# 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
# From background info: "microarray analysis of serial whole blood RNA samples"
# This indicates gene expression data is available
is_gene_available = True

# 2.1 Data Availability
# From sample characteristics:
trait_row = 2  # 'severity' indicates COVID-19 severity status
age_row = None # Age data not available
gender_row = None # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert severity level to binary (0: Non-critical, 1: Critical)"""
    if value is None:
        return None
    value = value.split(": ")[-1].strip()
    if value == "Critical":
        return 1
    elif value == "Non-critical":
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Convert age to float - placeholder since age not available"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary - placeholder since gender not available"""
    return None

# 3. Save Metadata
# Trait data is available since trait_row is not None
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction 
# Extract clinical features since 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)

# Preview the processed clinical data
print("Preview of clinical features:")
print(preview_df(clinical_features))

# Save clinical features
clinical_features.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
is_gene_available = True

# Save updated 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)
)

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
# Given that the gene identifiers start with "A_19_P", these are Agilent probe IDs and not standard gene symbols
# They will need to be mapped to official human gene symbols for biological interpretation

requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)

# This is human gene data with proper annotations
is_gene_available = True

# Save updated 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)
)
# Inspect the gene annotation data and identify relevant columns
# 'ID' contains probe IDs matching gene expression data
# 'GENE_SYMBOL' contains the target gene symbols
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')

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

# Preview result
print("\nGene expression data preview:")
print(gene_data.head())
print("\nShape:", gene_data.shape)

# Save updated 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)
)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Load saved clinical features
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)

# 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="Dataset contains whole blood transcriptomic data comparing critical vs non-critical COVID-19 patients, with gene expression profiles from 19 critical and 15 non-critical patients."
)

# 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)