p3/preprocess/Retinoblastoma/code/GSE208143.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Retinoblastoma"
cohort = "GSE208143"

# Input paths
in_trait_dir = "../DATA/GEO/Retinoblastoma"
in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE208143"

# Output paths
out_data_file = "./output/preprocess/3/Retinoblastoma/GSE208143.csv"
out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE208143.csv"
out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE208143.csv"
json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background 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
# From series title and summary, this is mRNA expression data
is_gene_available = True

# 2.1 Data Availability
# Tissue type indicates tumor vs control, maps to trait 
trait_row = 0
# Age not available
age_row = None  
# Gender is available
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Convert "tissue: X" to binary (1 for tumor, 0 for control)
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1]
    if 'tumor' in x:
        return 1
    elif 'retina' in x or 'control' in x:
        return 0
    return None

def convert_gender(x):
    # Convert "gender: X" to binary (1 for male, 0 for female) 
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1]
    if 'male' in x:
        return 1
    elif 'female' in x:
        return 0
    return None

# No convert_age function needed since age data not available

# 3. Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(False, cohort, json_path, is_gene_available, is_trait_available)

# 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,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview the extracted features
preview_dict = preview_df(selected_clinical_df)
print("Preview of selected clinical features:")
print(preview_dict)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.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])
# The identifiers starting with "A_19_P" appear to be Agilent microarray probe IDs
# rather than standard human gene symbols (which usually look like "BRCA1", "TP53", etc.)
# These probe IDs will need to be mapped to their corresponding gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_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))
# 1. Identify relevant columns - 'ID' matches probe IDs in expression data, 'GENE_SYMBOL' has gene symbols
prob_col = 'ID'
gene_col = 'GENE_SYMBOL'

# 2. Create mapping dataframe with just ID and gene symbol columns
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Convert probe-level measurements to gene-level expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview results
print("\nShape of gene expression data after mapping:")
print(gene_data.shape)
print("\nFirst few gene symbols:")
print(list(gene_data.index)[:10])

# Save genetic data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
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)

# 1. Normalize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
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
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Dataset contains gene expression data from primary human retinoblastoma samples profiled with Affymetrix microarray."
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
)

# 6. 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)