p3/preprocess/Hypertension/code/GSE74144.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypertension"
cohort = "GSE74144"

# Input paths
in_trait_dir = "../DATA/GEO/Hypertension"
in_cohort_dir = "../DATA/GEO/Hypertension/GSE74144"

# Output paths
out_data_file = "./output/preprocess/3/Hypertension/GSE74144.csv"
out_gene_data_file = "./output/preprocess/3/Hypertension/gene_data/GSE74144.csv"
out_clinical_data_file = "./output/preprocess/3/Hypertension/clinical_data/GSE74144.csv"
json_path = "./output/preprocess/3/Hypertension/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 the series title and overall design mentioning transcriptomic analysis
# and gene expression profiling of leukocytes, this dataset contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion 
# 2.1 Data Availability

# Trait (Hypertension) can be determined from Feature 0 status
trait_row = 0

# Age and gender are not explicitly mentioned in characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert hypertension status to binary"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower()
    if 'hypertensive patient' in value:
        return 1 
    elif 'control' in value:
        return 0
    return None

# No age conversion function needed
convert_age = None

# No gender conversion function needed  
convert_gender = None

# 3. Save initial metadata
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
)

# 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
    )
    
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features
    clinical_features.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)
requires_gene_mapping = True
# Get file paths
import gzip 

# First inspect raw file content to find where platform annotation begins
platform_start = False
data_preview = []
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    for line in f:
        if line.startswith('^PLATFORM'):
            platform_start = True
            continue
        if platform_start and len(data_preview) < 20:
            data_preview.append(line.strip())

print("Platform annotation preview:")
print("\n".join(data_preview))
print("\n" + "="*80 + "\n")

# Extract gene annotation with focus on platform section
prefixes = ['!Platform_table_begin']  # Changed to target platform table
gene_annotation = get_gene_annotation(soft_file, prefixes)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation columns and first few rows:")
print(preview_df(gene_annotation))

# Additional inspection of columns that might contain probe-gene mapping
columns = gene_annotation.columns.tolist()
print("\nAll columns:", columns)
for col in columns:
    non_null = gene_annotation[col].notna().sum()
    if non_null > 0:
        print(f"\nColumn '{col}' has {non_null} non-null values")
        print("Sample values:", gene_annotation[col].dropna().head().tolist())
# Extract gene annotation data targeting the platform table 
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    content = f.read()
    table_start = content.find('!Platform_table_begin')
    table_end = content.find('!Platform_table_end')
    table_content = content[table_start:table_end]

# Convert table content to DataFrame
gene_annotation = pd.read_csv(io.StringIO(table_content), sep='\t', skiprows=1)

# Print column info to verify extraction
print("Column names in gene annotation:")
print(gene_annotation.columns.tolist())
print("\nPreview of gene annotation data:")
print(gene_annotation.head())

# Use ID and GENE_SYMBOL columns for mapping
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Preview the mapped gene expression data
print("\nShape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Extract gene annotation targeting the platform table
with gzip.open(soft_file, 'rt') as f:
    content = f.read()
    # Find platform section start
    platform_start = content.find('^PLATFORM')
    # Find table markers within platform section
    table_start = content.find('!Platform_table_begin', platform_start)
    table_end = content.find('!Platform_table_end', table_start)
    
    if table_start != -1 and table_end != -1:
        # Extract content between markers and skip the header line
        table_content = content[content.find('\n', table_start):table_end]
        gene_annotation = pd.read_csv(io.StringIO(table_content), sep='\t')
        
        print("Gene annotation shape:", gene_annotation.shape)
        print("\nGene annotation columns and first few rows:")
        print(gene_annotation.head())
        print("\nColumn names:")
        print(gene_annotation.columns.tolist())
        
        # Preview non-empty values in relevant columns
        for col in gene_annotation.columns:
            non_null = gene_annotation[col].notna().sum()
            if non_null > 0:
                print(f"\nColumn '{col}' has {non_null} non-null values")
                print("Sample values:", gene_annotation[col].dropna().head().tolist())
    else:
        print("Platform table markers not found in file")
# 1. Extract gene annotation from SOFT file
platform_section = ''
table_content = ''
inside_platform = False
inside_table = False

with gzip.open(soft_file, 'rt') as f:
    for line in f:
        if line.startswith('^PLATFORM'):
            inside_platform = True
        elif line.startswith('!Platform_table_begin') and inside_platform:
            inside_table = True
            continue
        elif line.startswith('!Platform_table_end'):
            break
        elif inside_table:
            table_content += line
        elif inside_platform:
            platform_section += line

# Parse table content into DataFrame
gene_annotation = pd.read_csv(io.StringIO(table_content), sep='\t')

# Create mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# Apply mapping to convert probe data to gene data
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Normalize gene symbols using NCBI database
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)

# Load clinical data
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)

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

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

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

# 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="Gene expression study comparing hypertensive patients with/without left ventricular remodeling"
)

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

# Print a sample of rows that have non-null gene symbols
print("\nSample rows with non-null gene symbols:")
non_null_genes = gene_annotation[gene_annotation['GENE_SYMBOL'].notna()]
print(preview_df(non_null_genes))

# Count unique IDs and gene symbols
print("\nNumber of unique values:")
print("Unique IDs:", gene_annotation['ID'].nunique())
if 'GENE_SYMBOL' in gene_annotation.columns:
    print("Unique gene symbols:", gene_annotation['GENE_SYMBOL'].dropna().nunique())
# Based on the gene identifiers preview, we need ID and GENE_SYMBOL columns
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Preview the mapped gene expression data
print("\nShape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
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)
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="Study comparing transcriptional profiles between idiopathic non-cirrhotic portal hypertension patients, cirrhosis patients, and normal controls"
)

# 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)