p3/preprocess/Prostate_Cancer/code/GSE201805.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Prostate_Cancer"
cohort = "GSE201805"

# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE201805"

# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE201805.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE201805.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE201805.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")
# Check gene expression data availability
# Based on series title and summary, this is a gene expression study
is_gene_available = True

# Track trait data availability
# Treatment arm can indicate prostate cancer severity/response
trait_row = 5  # randomization arm row
def convert_trait(value):
    if pd.isna(value):
        return None
    value = value.split(": ")[-1].strip()
    # Treatment arm indicates disease severity/intervention status
    return 1 if value == "Treatment" else 0

# Track age data availability
age_row = 3  # age row
def convert_age(value):
    if pd.isna(value):
        return None
    try:
        # Extract numeric age value after colon
        age = int(value.split(": ")[-1].strip())
        return age
    except:
        return None

# Track gender data availability
# This is a prostate cancer study - all subjects are male
gender_row = None  # gender not needed since all male
def convert_gender(value):
    return 1  # all male

# Save metadata about data availability
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
)

# Extract clinical features using library function
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
)

# Preview the extracted features
preview = preview_df(clinical_features)
print("Preview of extracted clinical features:")
print(preview)

# Save 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)
# The identifiers appear to be numeric IDs (e.g. 2315554, 2315633, etc.)
# These are not standard human gene symbols which are typically alphanumeric (e.g. BRCA1, TP53)
# Therefore gene mapping will be required

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))
# From the preview, 'ID' column matches numeric identifiers in gene expression data
# 'gene_assignment' contains gene symbols between '//' delimiters

# Get probe-to-gene mapping
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

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

# Preview results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few gene symbols and their expression values:")
print(gene_data.head())
# Save probe-level gene data
gene_data.to_csv(out_gene_data_file)

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

# Link clinical and probe-level gene 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 probe-level gene expression data and clinical features. Gene symbol mapping was not successful."
)

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