p3/preprocess/Prostate_Cancer/code/GSE200879.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Prostate_Cancer"
cohort = "GSE200879"

# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE200879"

# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE200879.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE200879.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE200879.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
# Background info mentions "Transcriptomics" so gene expression data should be available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# Trait (tumor vs normal) is in row 0
trait_row = 0

# No age data available
age_row = None 

# No gender data available (typically all male in prostate cancer studies)
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x) or not isinstance(x, str):
        return None
    val = x.split(': ')[1].lower() if ': ' in x else x.lower()
    if 'tumor' in val:
        return 1
    elif 'normal' in val:
        return 0
    return None

def convert_age(x):
    # Not used since age data not available
    return None

def convert_gender(x):
    # Not used since gender data not available
    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
# Since trait_row is not None, extract clinical features
selected_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 extracted features
print("Preview of selected clinical features:")
print(preview_df(selected_clinical_df))

# Save clinical data
selected_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)
# These appear to be custom identifiers starting with "GSHG" rather than standard human gene symbols
# They will need to be mapped to proper 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. Determine mapping columns - 'ID' column matches gene identifiers in expression data,
# and 'Gene Symbol' contains the target gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get gene mapping from annotation data
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

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

# Preview result
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
# 1. Normalize gene symbols using NCBI synonym information and save
try:
    gene_data = normalize_gene_symbols_in_index(gene_data)
    gene_data.to_csv(out_gene_data_file)
except Exception as e:
    print(f"Warning: Gene symbol normalization failed, using original mapped gene symbols. Error: {e}")

# 2. Link clinical and gene expression data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) 
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

# 4. Check for bias in trait and demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort information
# If gene normalization failed but the data is otherwise usable, note this in metadata
note = "Contains gene expression data with custom probe-to-gene mapping." if 'GSHG' in str(gene_data.index[:5]) else "Contains normalized gene expression data."

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=note
)

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