p3/preprocess/Psoriasis/code/GSE162998.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE162998"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE162998"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE162998.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE162998.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE162998.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# 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 
# Yes - this is a gene expression profiling study using Illumina DASL BeadArray
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Trait - Available in Feature 3, lesional vs non-lesional skin indicates psoriasis status
trait_row = 3
# Age - Not available 
age_row = None
# Gender - Not available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert lesional/non-lesional status to binary psoriasis indicator"""
    if pd.isna(value):
        return None
    # Extract value after colon and strip whitespace
    value = value.split(':')[1].strip().lower()
    if 'lesional' in value:
        return 1 if value == 'lesional' else 0
    return None

# Age conversion not needed since age data not available
convert_age = None

# Gender conversion not needed since gender data not available  
convert_gender = None

# 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:
    # Extract clinical features
    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
    )
    
    # Preview the extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.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)
# Based on the identifiers (e.g. ILMN_1343291), these are Illumina probe IDs
# which need to be mapped to human gene symbols for proper analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract probe ID and gene symbol columns for mapping
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')

# Convert probe level data to gene expression data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)

# Preview the gene expression data
print("Shape of gene expression data after mapping:", gene_data.shape) 
print("\nFirst few rows of gene-level data:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Load clinical features and link with genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) 
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# 4. Check for feature bias
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata recording
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=trait_biased,
    df=linked_data,
    note="Gene expression data with lesional/non-lesional tissue trait information."
)

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