p3/preprocess/Prostate_Cancer/code/GSE192817.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Prostate_Cancer"
cohort = "GSE192817"

# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE192817"

# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE192817.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE192817.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE192817.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 
is_gene_available = True  # Based on title and summary, this is gene expression data studying cellular mechanisms

# 2.1 Data Availability
trait_row = None # No prostate cancer status - all samples are cancer cell lines
age_row = None # No age data available
gender_row = None # No gender data - these are cell lines

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return None # Not used since trait_row is None

def convert_age(x):
    return None # Not used since age_row is None 

def convert_gender(x):
    return None # Not used since gender_row is 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=False # No trait data available since these are cell lines
)

# 4. Clinical Feature Extraction
# Skip this step since trait_row is None
# 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 gene identifiers appear to be simple numerical indices (1, 2, 3, etc.)
# rather than standard human gene symbols or probe IDs
# This indicates we need to map these indices to actual 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))
# 1. Identify mapping keys:
# 'ID' in gene_metadata matches the numeric indices in gene_data
# 'GENE_SYMBOL' contains the target gene symbols

# 2. Get gene mapping dataframe 
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')

# 3. Apply mapping to convert probe measurements to gene expression values
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview the resulting gene expression data
print("\nShape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
print("\nFirst few gene symbols:")
print(gene_data.index[:20])
# 1. Normalize gene symbols
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)

# Since we determined in step 2 that no clinical features are available
# (all samples are cell lines with no trait data), we cannot construct a valid linked dataset.
# Set is_biased=True since the dataset cannot be used for trait association analysis.

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,  # Dataset is biased since it lacks trait data
    df=normalized_gene_data,
    note="Contains normalized gene expression data from cell lines but lacks clinical trait data required for association analysis."
)

# Do not save linked data since trait information is not available and dataset is not usable