p3/preprocess/Essential_Thrombocythemia/code/GSE12295.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE12295"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE12295"

# Output paths
out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE12295.csv"
out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE12295.csv"
out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE12295.csv"
json_path = "./output/preprocess/3/Essential_Thrombocythemia/cohort_info.json"

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

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# From background info, this is an oligonucleotide array study comparing platelet samples
# Contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# From characteristics dict:
# Key 0 contains disease status including ET, RT and Normal
trait_row = 0  
# Age and gender not available
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(val):
    if not isinstance(val, str):
        return None
    val = val.lower()
    if "essential thrombocythemia" in val:
        return 1  # Case
    elif "normal" in val:
        return 0  # Control
    else:
        return None  # RT samples excluded

def convert_age(val):
    # Not available
    return None

def convert_gender(val):
    # Not available 
    return None

# 3. Save Metadata 
# Initial filtering - trait row exists, so trait data is available
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
# Since trait_row exists, extract clinical features
clinical_df = 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_result = preview_df(clinical_df)
print("Preview of extracted clinical features:")
print(preview_result)

# Save to CSV
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These look like probe IDs from a microarray, not gene symbols
# They are numerical values that need to be mapped to actual gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# 1. Identify mapping columns: 'ID' for probe identifiers, 'Gene Symbol' for gene names
# in gene annotation data matches the indices in gene expression data

# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Preview the mapped gene data
preview_result = preview_df(gene_data)
print("\nPreview of gene expression data after mapping:")
print(preview_result)
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data) 
normalized_gene_data.to_csv(out_gene_data_file)

# Read the processed clinical data file
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = "Expression data comparing patients with Essential Thrombocythemia to controls with other myeloproliferative disorders (PMF, PV). No age or gender data available."
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=note
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")