p3/preprocess/Lung_Cancer/code/GSE244645.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lung_Cancer"
cohort = "GSE244645"

# Input paths
in_trait_dir = "../DATA/GEO/Lung_Cancer"
in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE244645"

# Output paths
out_data_file = "./output/preprocess/3/Lung_Cancer/GSE244645.csv"
out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE244645.csv"
out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE244645.csv"
json_path = "./output/preprocess/3/Lung_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on background info, this is platelet gene expression data from microarray
is_gene_available = True

# 2. Variable Availability and Row Detection
# trait (cancer state) is in Feature 1 - tumour presence/absence 
trait_row = 1
# age is in Feature 5
age_row = 5 
# gender is in Feature 4
gender_row = 4

# 2. Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert tumor status to binary: 1 for tumor presence, 0 for tumor free"""
    if not value or value == '-':
        return None
    value = value.split(': ')[1].lower()
    if 'tumour presence' in value:
        return 1
    elif 'tumour free' in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age string to float"""
    if not value or value == '-':
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 1 for male, 0 for female"""
    if not value or value == '-':
        return None
    value = value.split(': ')[1].lower()
    if value == 'male':
        return 1
    elif value == 'female':
        return 0
    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
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,
        age_row=age_row,
        convert_age=convert_age, 
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical data
    clinical_features.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# The identifiers (e.g. TC0100006437.hg.1) appear to be probe IDs from a microarray platform
# rather than standard human gene symbols like BRCA1, TP53 etc.
# They will need to be mapped to official gene symbols.
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Try searching for ID patterns in all columns
print("All column names:", gene_metadata.columns.tolist())
print("\nPreview first few rows of each column to locate numeric IDs:")
for col in gene_metadata.columns:
    sample_values = gene_metadata[col].dropna().head().tolist()
    print(f"\n{col}:")
    print(sample_values)

# Inspect raw file to see unfiltered annotation format
import gzip
print("\nRaw SOFT file preview:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    header = []
    for i, line in enumerate(f):
        header.append(line.strip())
        if i >= 10:  # Preview first 10 lines
            break
print('\n'.join(header))
# Create function to extract gene symbols from annotation text
def extract_gene_symbols(text):
    if not isinstance(text, str):
        return []
    symbols = []
    # Get symbols from parentheses after "Homo sapiens"
    matches = re.findall(r'Homo sapiens.*?\((\w+)\)', text)
    symbols.extend(matches)
    # Get symbols from HGNC tags 
    hgnc_matches = re.findall(r'\[Source:HGNC Symbol;Acc:HGNC:\d+\].*?(\w+)', text)
    symbols.extend(hgnc_matches)
    return list(set(symbols))

# Create mapping dataframe by extracting gene symbols from SPOT_ID.1 column
gene_metadata['Gene'] = gene_metadata['SPOT_ID.1'].apply(extract_gene_symbols)
mapping_data = gene_metadata[['ID', 'Gene']].copy()

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

# Save genetic data 
gene_data.to_csv(out_gene_data_file)

print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows and columns of mapped data:")
print(gene_data.head().iloc[:, :5])
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation data and load gene data from file
gene_metadata = get_gene_annotation(soft_file)

# Refine extraction of gene symbols
def extract_gene_symbols_from_annotation(text):
    if not isinstance(text, str):
        return []
    # Focus on RefSeq entries which typically have cleaner gene names
    refseq_match = re.search(r'NM_\d+ // RefSeq // Homo sapiens .*? \((\w+)\)', text)
    if refseq_match:
        return [refseq_match.group(1)]  # Return the symbol in parentheses
    return []

# Create mapping dataframe
gene_metadata['Gene'] = gene_metadata['SPOT_ID.1'].apply(extract_gene_symbols_from_annotation)
mapping_data = gene_metadata[['ID', 'Gene']].copy()

# Re-apply mapping with refined gene symbol extraction
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene expression data 
gene_data.to_csv(out_gene_data_file)

# Link clinical and genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

# Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Record cohort information
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="Gene expression and clinical data processed and linked using refined gene symbol extraction."
)

# Save data if usable
if is_usable:
    linked_data.to_csv(out_data_file)