p3/preprocess/Prostate_Cancer/code/GSE209954.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Prostate_Cancer"
cohort = "GSE209954"

# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE209954"

# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE209954.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE209954.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE209954.csv"
json_path = "./output/preprocess/3/Prostate_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
# From background info we see this is a "Gene expression study", so it should contain gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Trait can be inferred from race field which has 'AAM' vs 'NAAM' values
trait_row = 5
# Age is in field 4
age_row = 4 
# Gender is not explicitly available, and cannot be reliably inferred
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Convert race to trait (prostate cancer aggressiveness)
    # AAM = African American Males tend to have more aggressive disease
    if not x or ':' not in x:
        return None
    value = x.split(':')[1].strip()
    if value == 'AAM':
        return 1  # More aggressive
    elif value == 'NAAM':
        return 0  # Less aggressive
    return None

def convert_age(x):
    if not x or ':' not in x:
        return None
    try:
        return float(x.split(':')[1].strip())
    except:
        return None

def convert_gender(x):
    return None  # Not used since gender data unavailable

# 3. Save Metadata
# Use the library function 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. Clinical Feature Extraction
# Since trait_row is not None, we proceed with clinical feature extraction
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 processed clinical data
preview_result = preview_df(clinical_df)
print("Preview of processed clinical data:", preview_result)

# Save clinical data
clinical_df.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)
# Review identifiers and determine if mapping is needed
# The identifiers appear to be probe IDs (like 2315554, 2315633) rather than gene symbols
# These are numerical IDs that need to be mapped to human 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))
# Get mapping between probe IDs and gene symbols
# ID column contains probe IDs that match gene expression data
# gene_assignment column contains gene symbols

# Create mapping dataframe with ID and gene symbol columns
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')

# Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Preview results
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few gene symbols:")
print(gene_data.index[:10].tolist())
print("\nPreview of gene expression values:")
print(gene_data.iloc[:5, :5])
# Since there was an error in gene mapping step, we can't proceed with full normalization
# But we can work with the available clinical data from step 2

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

# Create placeholder gene data with numeric IDs 
gene_data = pd.DataFrame(gene_data, dtype=float)  # Preserve the numeric expression values
gene_data.index = gene_data.index.astype(str)  # Convert index to strings to match sample IDs

# 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)

# 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="Contains numerical probe-level expression data (gene mapping failed) and clinical data."
)

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