p3/preprocess/Pancreatic_Cancer/code/GSE236951.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Pancreatic_Cancer"
cohort = "GSE236951"

# Input paths
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE236951"

# Output paths
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE236951.csv"
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE236951.csv"
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE236951.csv"
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"

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

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# 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
# The series summary indicates nanostring gene expression analysis of ~700 immune related genes
is_gene_available = True

# 2.1 Data Availability 
# Disease status in row 0, gender in row 2, age in row 3
trait_row = 0
gender_row = 2 
age_row = 3

# 2.2 Data Type Conversion Functions
def convert_trait(x: str) -> Optional[int]:
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1]
    if 'pancreatic' in x:
        return 1
    elif 'colon' in x or 'benign' in x:
        return 0
    return None

def convert_gender(x: str) -> Optional[int]:
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1]
    if 'female' in x:
        return 0
    elif 'male' in x:
        return 1
    return None

def convert_age(x: str) -> Optional[float]:
    if not isinstance(x, str):
        return None
    try:
        return float(x.split(': ')[-1].split()[0])
    except:
        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. Clinical Feature Extraction
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))

clinical_features.to_csv(out_clinical_data_file)
# 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 gene identifiers are already human gene symbols (like A2M, ABCB1, ABL1, etc.)
# No mapping is needed
requires_gene_mapping = False
# 1. Normalize gene symbols and save normalized gene data
gene_data = normalize_gene_symbols_in_index(gene_data) 
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data and trait
# First get selected clinical features using the extraction function from previous step
selected_clinical = 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
)

linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)

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

# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate data quality and save metadata
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 data comparing cervical carcinoma vs normal tissue samples"
)

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