p3/preprocess/Adrenocortical_Cancer/code/GSE68950.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE68950"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68950"

# Output paths
out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE68950.csv"
out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE68950.csv"
out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE68950.csv"
json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# 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
# Since the background info shows this is Affymetrix gene expression array data
is_gene_available = True

# Variable availability
trait_row = 3  # Use 'organism part' field, which has 'Adrenal Gland' among values
age_row = None  # No age information available
gender_row = None  # No gender information available

# Data type conversion functions
def convert_trait(value: str) -> int:
    if value is None or ':' not in value:
        return None
    value = value.split(': ')[1].strip()
    # Binary: 1 for Adrenal Gland samples, 0 for others
    return 1 if value == 'Adrenal Gland' else 0

# Validate and save initial info
is_trait_available = trait_row is not None
_ = validate_and_save_cohort_info(is_final=False, 
                                cohort=cohort,
                                info_path=json_path,
                                is_gene_available=is_gene_available,
                                is_trait_available=is_trait_available)

# Extract clinical features since trait_row is not None
sample_characteristics = {
    3: ['organism part: Leukemia', 'organism part: Urinary tract', 'organism part: Prostate', 
        'organism part: Stomach', 'organism part: Kidney', 'organism part: Thyroid Gland', 
        'organism part: Brain', 'organism part: Skin', 'organism part: Muscle', 
        'organism part: Head and Neck', 'organism part: Ovary', 'organism part: Lung',
        'organism part: Autonomic Ganglion', 'organism part: Endometrium', 'organism part: Pancreas',
        'organism part: Cervix', 'organism part: Breast', 'organism part: Colorectal',
        'organism part: Liver', 'organism part: Vulva', 'organism part: Bone',
        'organism part: Oesophagus', 'organism part: BiliaryTract', 
        'organism part: Connective and Soft Tissue', 'organism part: Lymphoma',
        'organism part: Pleura', 'organism part: Testis', 'organism part: Placenta',
        'organism part: Adrenal Gland', 'organism part: Unknow']
}
clinical_data = pd.DataFrame(sample_characteristics)

selected_clinical_df = geo_select_clinical_features(
    clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    age_row=age_row, 
    convert_age=None,
    gender_row=gender_row,
    convert_gender=None
)

# Preview the extracted clinical data
preview = preview_df(selected_clinical_df)
print(preview)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)  
selected_clinical_df.to_csv(out_clinical_data_file)
# 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)
# From the identifiers like "1007_s_at", "117_at", etc., these appear to be probe IDs from Affymetrix microarray
# rather than human gene symbols. They will need to be mapped to official gene symbols.

requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

# Print example rows showing the mapping information columns
print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
print("\nFirst 5 rows:")
print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'Gene Symbol' column: Contains gene symbol information")
# Get gene mapping between probe IDs and gene symbols using identified columns
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')

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

# Preview result to confirm successful mapping
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few genes and their expression values:")
print(gene_data.head())
# 1. Load clinical data and save normalized gene data
selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)

# Check for invalid clinical data (all 0s)
if selected_clinical.shape[0] == 1 and selected_clinical.iloc[0,0] == 0:
    print("Error: Clinical data contains only negative samples (all 0s). Dataset not suitable for analysis.")
    _ = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=None,
        df=None,
        note="Clinical data contains only negative samples - not suitable for case-control analysis"
    )
else:
    # Proceed with gene data normalization and saving
    gene_data.index = gene_data.index.str.replace('-mRNA', '')
    gene_data = normalize_gene_symbols_in_index(gene_data)
    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
    gene_data.to_csv(out_gene_data_file)

    # 2. Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)

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

    # 4. Check for biased features and remove them if needed 
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Validate and save cohort info
    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="Data from Sanger cell line Affymetrix gene expression project examining cancer cell lines"
    )

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