p3/preprocess/Adrenocortical_Cancer/code/GSE67766.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE67766"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE67766"

# Output paths
out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE67766.csv"
out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE67766.csv"
out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE67766.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)

# First extract background info and subseries info
background_info, _ = get_background_and_clinical_data(matrix_file, 
                                                    prefixes_a=['!Series_title', '!Series_summary', 
                                                              '!Series_overall_design', '!Series_type',
                                                              '!Series_relation'],
                                                    prefixes_b=None)

print("Initial Dataset Information:")
print(background_info)
print("\nChecking for subseries...\n")

# If SuperSeries, get the constituent series accession
subseries = None
if 'SuperSeries' in background_info:
    for line in background_info.split('\n'):
        if '!Series_relation\t' in line:
            matches = re.finditer(r'GSE\d+', line)
            for match in matches:
                potential_subseries = match.group(0)
                if potential_subseries != cohort:  # Skip if it's the SuperSeries ID
                    subseries_dir = os.path.join(in_trait_dir, potential_subseries)
                    if os.path.exists(subseries_dir):
                        print(f"Found valid subseries: {potential_subseries}")
                        subseries = potential_subseries
                        break

# If subseries found, update directory path and get new files
if subseries:
    in_cohort_dir = os.path.join(in_trait_dir, subseries)
    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
    print(f"\nUsing subseries data from: {in_cohort_dir}\n")
else:
    print("\nNo valid subseries found, using original data\n")

# Extract background info and clinical data from final files
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 final dataset information
print("Final Dataset Information:")
print(f"{background_info}\n")

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_type, which includes "Expression profiling by array" and "Expression profiling by high throughput sequencing"
# this dataset likely contains gene expression data
is_gene_available = True

# 2.1 Data Availability
# Looking at sample characteristics:
# Row 0 shows "cell line: SW-13" - this indicates cell line data, not clinical samples
# No rows for trait, age or gender found
trait_row = None  
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
# Although not used since data is unavailable, define placeholder functions
def convert_trait(x):
    if x is None or pd.isna(x):
        return None
    value = str(x).split(':')[-1].strip()
    # Binary conversion would go here
    return None

def convert_age(x):
    if x is None or pd.isna(x):
        return None
    value = str(x).split(':')[-1].strip()
    # Numeric conversion would go here
    return None

def convert_gender(x):
    if x is None or pd.isna(x):
        return None
    value = str(x).split(':')[-1].strip().lower()
    # Gender binary conversion would go here
    return None

# 3. Save Metadata
# trait_row is None so is_trait_available is False
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=False)

# 4. Clinical Feature Extraction
# Skip since trait_row is None
# 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 identifiers starting with "ILMN_", these are Illumina probe IDs
# They need to be mapped to official gene symbols to be interpretable in analysis
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 'Symbol'):")
print("\nFirst 5 rows:")
print(gene_annotation[['ID', 'Symbol']].head().to_string())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'Symbol' column: Contains gene symbol information")
# 1. Based on previous output:
# Gene expression data uses 'ILMN_*' identifiers as index
# Gene annotation data has matching IDs in 'ID' column and gene symbols in 'Symbol' column 

# 2. Extract mapping between probe IDs and gene symbols
mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Apply gene mapping to convert probe-level measurements to gene expression
gene_data = apply_gene_mapping(gene_data, mapping)

# Print info about the mapping results
print("Shape of probe-level data:", gene_data.shape)
print("\nShape after mapping to genes:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(gene_data.head())
print("\nFirst few gene symbols:")
print(gene_data.index[:10])
# 1. Normalize and save gene expression data
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)

# 2-4. Skip clinical data linking and bias checking since no clinical data exists

# 5. Update cohort info to reflect dataset is not usable due to lack of trait data
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,  # Cell line data is considered biased for human trait analysis
    df=gene_data,  # Provide gene expression data
    note="Dataset contains only cell line data (SW-13) without clinical information"
)

# 6. Skip saving linked data since dataset is not usable for trait analysis