p3/preprocess/Retinoblastoma/code/GSE29683.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Retinoblastoma"
cohort = "GSE29683"

# Input paths
in_trait_dir = "../DATA/GEO/Retinoblastoma"
in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE29683"

# Output paths
out_data_file = "./output/preprocess/3/Retinoblastoma/GSE29683.csv"
out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE29683.csv"
out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE29683.csv"
json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"

# Debug: Print directory contents
print("Directory contents:", os.listdir(in_cohort_dir))

# Get matrix file path directly
matrix_file_path = os.path.join(in_cohort_dir, "GSE29683_series_matrix.txt.gz")

# Get background info and clinical data from compressed gz file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("\nBackground Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Yes, this dataset contains gene expression data comparing tumor samples with cell lines
is_gene_available = True

# 2.1 Data Availability
# For trait: Can be inferred from cell type field
trait_row = 0
# Age and gender not available in sample characteristics
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert sample type to binary: 1 for tumor, 0 for cell line/xenograft"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower()
    if 'primary tumor' in value:
        return 1
    elif any(x in value for x in ['cell line', 'xenograft']):
        return 0
    return None

def convert_age(value: str) -> float:
    return None

def convert_gender(value: str) -> int:
    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
if trait_row is not None:
    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
    )
    
    # Preview the extracted features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Based on observing the gene identifiers, they appear to be Affymetrix probe IDs
# (e.g. "1007_s_at", "1053_at", "117_at") rather than standard human gene symbols.
# We will need to map these probe IDs to gene symbols for biological interpretation.

requires_gene_mapping = True
# Use the matrix file we already have
matrix_file_path = os.path.join(in_cohort_dir, "GSE29683_series_matrix.txt.gz")

# Extract gene annotation data from matrix file
gene_annotation = get_gene_annotation(matrix_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Extract platform data using modified approach
prefixes_platform = ["^ID", "^Gene", "^GB_ACC", "^SPOT_ID"]  # Common platform header prefixes
gene_annotation = get_gene_annotation(matrix_file_path, prefixes=prefixes_platform)

# Print column names to see what we got
print("Platform annotation columns:")
print(gene_annotation.columns)

# We can use "GB_ACC" column which often contains gene symbols 
prob_col = 'ID'
gene_col = 'GB_ACC'
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# Apply mapping to get gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)

# Preview the first few rows
print("\nPreview of mapped gene expression data:")
print(preview_df(gene_data))
# We only have the matrix file available
matrix_file_path = os.path.join(in_cohort_dir, "GSE29683_series_matrix.txt.gz")

# Modify the prefixes to extract platform and gene annotation info from matrix file
# Common platform-related prefixes in GEO matrix files
prefixes = ['!Platform_title', '!Platform_organism', '!Platform_technology',
            '!Platform_data_row_count', '!Platform_sample_id',
            '!Platform_target_source', '!Platform_distribution', 
            '!Platform_manufacturer', '!Platform_coating',
            '!Platform_description', '!Platform_web_link', 
            '!Platform_table_begin', '!Platform_table_end']

# Extract gene annotation data from matrix file
gene_annotation = get_gene_annotation(matrix_file_path, prefixes)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Load previously processed genetic data
genetic_data = get_genetic_data(matrix_file_path)

# Try to normalize probe IDs to gene symbols using NCBI synonym information
# Even though these are probe IDs, some may match gene symbols
gene_data = pd.DataFrame(genetic_data.values,
                        columns=genetic_data.columns, 
                        index=genetic_data.index)
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene data
gene_data.to_csv(out_gene_data_file)

# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

# Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Final validation and information saving
note = "Dataset contains gene expression profiles from human retinoblastoma samples. Limited gene symbol mapping possible due to probe IDs in matrix file lacking platform annotations. Used NCBI synonym mapping on probe IDs where possible."
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=trait_biased,
    df=linked_data,
    note=note
)

# Save linked data only if usable 
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)