p3/preprocess/Sickle_Cell_Anemia/code/GSE84632.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sickle_Cell_Anemia"
cohort = "GSE84632"

# Input paths
in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84632"

# Output paths
out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE84632.csv"
out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE84632.csv"
out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE84632.csv"
json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
is_gene_available = True  # The title and summary indicate this is gene expression data from PBMCs

# 2.1 Data Availability
trait_row = 2  # Disease status in row 2
age_row = None  # Age not provided
gender_row = None  # Gender not provided

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert trait information to binary (0: Control, 1: Case)"""
    if value is None or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'sickle cell disease' in value:
        return 1
    return None

def convert_age(value):
    """Convert age to float"""
    return None  # Not used since age data not available

def convert_gender(value): 
    """Convert gender to binary (0: Female, 1: Male)"""
    return None  # Not used since gender data not available

# 3. Save 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:
    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
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical))
    
    # Save to CSV
    selected_clinical.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# The gene identifiers appear to be array probe IDs (16650001, etc) rather than human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview the dataframe by looking at column names and first few values
print("Gene annotation columns and first few rows:")
for col in gene_annotation.columns:
    print(f"\n{col}:")
    print(list(gene_annotation[col])[:5])
# Step 1: Extract platform table data from SOFT file - look between !Platform_table_begin and !Platform_table_end
platform_table = filter_content_by_prefix(
    soft_file_path,
    prefixes_a=['!Platform_table_begin', '!Platform_table_end'],
    unselect=True,
    source_type='file',
    return_df_a=True
)[0]

# Look at column names to identify probe ID and gene symbol columns
print("Platform table columns:")
print(platform_table.columns)

# Review some sample rows to confirm data format
print("\nSample rows:")
print(platform_table.head())

# Create mapping using probe ID and gene symbol columns
mapping_df = get_gene_mapping(platform_table, 'ID', 'gene_assignment')

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

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

print("\nShape of gene expression data after mapping:")
print(gene_data.shape)
print("\nFirst few gene symbols:")
print(list(gene_data.index)[:10])
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))