p3/preprocess/Lung_Cancer/code/GSE280643.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lung_Cancer"
cohort = "GSE280643"

# Input paths
in_trait_dir = "../DATA/GEO/Lung_Cancer"
in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE280643"

# Output paths
out_data_file = "./output/preprocess/3/Lung_Cancer/GSE280643.csv"
out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE280643.csv"
out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE280643.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
is_gene_available = True  # Study of KEAP1/NRF2 signaling indicates gene expression data

# 2.1 Data Availability
trait_row = 0  # Can infer lung cancer status from tissue type
age_row = None  # Age data not available 
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert tissue type to binary lung cancer status"""
    if pd.isna(value):
        return None
    value = value.lower().split(': ')[1] if ': ' in value else value.lower()
    if 'small cell lung cancer' in value:
        return 1
    elif 'normal lung' in value:
        return 0
    return None

def convert_age(value):
    return None  # Not used since age data unavailable

def convert_gender(value):
    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. 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,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    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)
# In this data, identifiers like '23064070' are numeric IDs
# These are not human gene symbols and need to be mapped to get gene names
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Create a mapping with placeholder gene symbols since we can't find direct mapping
mapping_df = pd.DataFrame({'ID': gene_data.index, 'Gene': 'Gene_' + gene_data.index.astype(str)})

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

print("\nShape after mapping:", gene_data.shape)
print("\nPreview of converted gene data:")
print(preview_df(gene_data))

# Save the mapped gene expression data
gene_data.to_csv(out_gene_data_file)
# Since gene mapping wasn't successful earlier, we'll work with probe-level data
gene_data = pd.DataFrame(gene_data, dtype=float)  # Keep numeric expression values
gene_data.index = gene_data.index.astype(str)  # Convert index to strings to match sample IDs

# Load clinical data from previous steps
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data 
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)

# Save both gene expression data and cohort info
gene_data.to_csv(out_gene_data_file)

# Record cohort information and save if usable
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="Contains numerical probe-level expression data and clinical data. Gene symbol mapping was not successful."
)

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