p3/preprocess/Adrenocortical_Cancer/code/GSE19776.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE19776"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE19776"

# Output paths
out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE19776.csv"
out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE19776.csv"
out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE19776.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")
# 1. Gene Expression Data Availability 
# Based on series title and extensive disease/tumor grade info, this appears to be a gene expression study
is_gene_available = True

# 2.1 Data Availability
# Trait (cancer stage) available in Feature 1 - extent of disease
trait_row = 1  

# Age available in Feature 5
age_row = 5

# Gender available in Feature 4
gender_row = 4

# 2.2 Data Type Conversion Functions
def convert_trait(val: str) -> int:
    """Convert extent of disease to binary (0=localized, 1=advanced)"""
    if not val or 'Unknown' in val:
        return None
    val = val.split(': ')[1].strip()
    if val == 'Localized':
        return 0
    elif val in ['Regional', 'Metastatic']:
        return 1
    return None

def convert_age(val: str) -> float:
    """Convert age to float"""
    if not val or 'Unknown' in val:
        return None
    try:
        return float(val.split(': ')[1])
    except:
        return None

def convert_gender(val: str) -> int:
    """Convert gender to binary (0=F, 1=M)"""
    if not val:
        return None
    val = val.split(': ')[1].strip()
    if val == 'F':
        return 0
    elif val == 'M':
        return 1
    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. Extract Clinical Features
selected_clinical = 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
)

print("Preview of selected clinical features:")
print(preview_df(selected_clinical))

# Save clinical data
selected_clinical.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)
# Looking at the data, we see numeric IDs (3,4,5,8,9 etc) being used as identifiers
# These are not standard human gene symbols, which are typically alphanumeric (e.g. TP53, BRCA1)
# Therefore mapping will be required to convert these IDs to 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(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 probe-to-gene mapping from annotation data
mapping_df = gene_annotation[['ID', 'Gene Symbol']].copy()
mapping_df = mapping_df.rename(columns={'Gene Symbol': 'Gene'})

# Convert IDs to string type and remove any leading/trailing whitespace 
mapping_df['ID'] = mapping_df['ID'].astype(str).str.strip()
gene_data.index = gene_data.index.str.strip()

# Filter annotation data to match numeric probe IDs only
mapping_df = mapping_df[mapping_df['ID'].str.match(r'^\d+$')]

# Apply mapping to convert probe-level data to gene expression data
# Note: Each probe's expression will be divided among its target genes, then summed per gene
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Normalize gene symbols before saving 
gene_data = normalize_gene_symbols_in_index(gene_data)

print("Shape after mapping probes to genes:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(gene_data.head())

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Skip data linking since gene mapping failed
linked_data = pd.DataFrame()  # Empty dataframe since no valid gene data

# Validate and save cohort info indicating the data is not usable
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=False,  # Set to False since gene mapping failed
    is_trait_available=True,  # Clinical data was successfully extracted
    is_biased=True,  # No valid data to analyze
    df=linked_data,
    note="Gene mapping failed - numeric probe IDs in expression data did not match Affymetrix IDs in annotation"
)