p3/preprocess/Asthma/code/GSE123088.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE123088"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE123088"

# Output paths
out_data_file = "./output/preprocess/3/Asthma/GSE123088.csv"
out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE123088.csv"
out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE123088.csv"
json_path = "./output/preprocess/3/Asthma/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
# CD4+ T cells data likely contains gene expression, not just miRNA or methylation
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 1  # 'primary diagnosis' contains trait info
age_row = 3  # 'age' data starts at feature 3 and continues in feature 4
gender_row = 2  # 'Sex' info is in feature 2

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if pd.isna(value):
        return None
    value = value.split(': ')[1]
    # Convert values to binary (0: control, 1: asthma)
    if value in ['HEALTHY_CONTROL', 'Control']:
        return 0
    elif value in ['ASTHMA']:
        return 1
    return None

def convert_age(value):
    if pd.isna(value):
        return None
    try:
        # Extract numeric age value after colon
        age = int(value.split(': ')[1])
        return age
    except:
        return None

def convert_gender(value):
    if pd.isna(value):
        return None
    if not value.startswith('Sex:'):
        return None
    value = value.split(': ')[1]
    # Convert to binary (0: female, 1: male)
    if value.upper() == 'FEMALE':
        return 0
    elif value.upper() == 'MALE':
        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. Clinical Feature Extraction
if trait_row is not None:
    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
    )
    
    # Preview the processed clinical data
    preview = preview_df(selected_clinical)
    print("Preview of processed clinical data:")
    print(preview)
    
    # Save to CSV
    selected_clinical.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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)
# The identifiers appear to be numeric IDs from 1-24166
# These are not gene symbols and will need mapping
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract platform info from SOFT file line by line
import gzip
import re
platform_lines = []
gene_symbol_lines = []
with gzip.open(soft_file, 'rt') as f:
    in_platform_block = False
    for line in f:
        if line.startswith('!Platform_'):
            platform_lines.append(line.strip())
            if 'table_begin' in line:
                in_platform_block = True
                # Skip header line
                next(f)
                continue
            elif 'table_end' in line:
                in_platform_block = False
                continue
        elif in_platform_block:
            gene_symbol_lines.append(line.strip())

# Parse platform annotation table
import pandas as pd 
import io

platform_table = pd.read_csv(io.StringIO('\n'.join(gene_symbol_lines)), sep='\t')

# Preview annotation data
print("Platform Information:")
for line in platform_lines[:5]:
    print(line)

print("\nPlatform Annotation Table Preview:")
print("Column names:", platform_table.columns.tolist())
print("\nFirst few rows:")
print(preview_df(platform_table))
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Look through entire SOFT file for gene symbol information
import gzip
platform_lines = []
gene_table_lines = []
with gzip.open(soft_file, 'rt') as f:
    in_gene_table = False
    table_found = False
    for line in f:
        # Keep track of platform lines for debugging
        if line.startswith('!Platform_'):
            platform_lines.append(line.strip())
            if 'table_begin' in line:
                in_gene_table = True
                # Get header line
                header = next(f).strip()
                gene_table_lines.append(header)
                continue
            elif 'table_end' in line:
                in_gene_table = False
        elif in_gene_table and ('gene_symbol' in line.lower() or 'gene_name' in line.lower() or 
                               'symbol' in line.lower() or 'gene_assignment' in line.lower()):
            table_found = True
            gene_table_lines.append(line.strip())

print("Platform information:")
for line in platform_lines[:10]:
    print(line)

print("\nFirst few gene table lines (if gene symbols found):")
if gene_table_lines:
    for line in gene_table_lines[:5]:
        print(line)

print("\nSearching for alternative annotation fields...")
# Extract gene annotation trying both methods
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("\nGene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary (showing all columns):")
pd.set_option('display.max_columns', None)
print(gene_annotation.head().to_dict('records'))
# Create mapping dataframe using platform's annotation
mapping_data = gene_annotation.loc[:, ['ID', 'ENTREZ_GENE_ID']].dropna()

# Create a list of genes for the Gene column, single gene ID per row
mapping_data['Gene'] = mapping_data['ENTREZ_GENE_ID'].map(lambda x: [str(int(float(x)))] if pd.notnull(x) and float(x) > 0 else [])
mapping_data = mapping_data.drop(columns=['ENTREZ_GENE_ID'])

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

# Normalize gene symbols to consistent format
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Get file paths and read data
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get gene annotation and expression data
gene_annotation = get_gene_annotation(soft_file)
gene_data = get_genetic_data(matrix_file)

# Clean and create mapping dataframe 
gene_annotation = gene_annotation[gene_annotation['ENTREZ_GENE_ID'].str.isnumeric().fillna(False)]
mapping_data = gene_annotation.loc[:, ['ID', 'ENTREZ_GENE_ID']].dropna()
mapping_data['Gene'] = mapping_data['ENTREZ_GENE_ID'].map(lambda x: [str(int(float(x)))] if pd.notnull(x) and float(x) > 0 else [])
mapping_data = mapping_data.drop(columns=['ENTREZ_GENE_ID'])

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

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

# Link clinical and genetic data
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

# Evaluate bias
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="Dataset contains gene expression data from CD4+ T cells."
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)