p3/preprocess/Psoriatic_Arthritis/code/GSE61281.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriatic_Arthritis"
cohort = "GSE61281"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE61281"

# Output paths
out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE61281.csv"
out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE61281.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE61281.csv"
json_path = "./output/preprocess/3/Psoriatic_Arthritis/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
is_gene_available = True  # Study title indicates whole blood transcriptional profiling

# 2.1 Data Availability
trait_row = 1  # 'condition' field contains case/control status
gender_row = 2  # 'gender' field available
age_row = None  # Age data not directly available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait value to binary: 1 for PsA cases, 0 for controls and PsC"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'psoriatic arthritis' in value:
        return 1
    elif 'unaffected control' in value or 'cutaneous psoriasis without arthritis' in value:
        return 0
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 0 for female, 1 for male"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# 3. Save Initial 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. Extract Clinical Features
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the selected features
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save clinical data
    selected_clinical_df.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])
# Looking at the gene identifiers like 'A_23_P100001', these are Agilent array probe IDs
# They need to be mapped to standard human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Create mapping dataframe using ID and GENE_SYMBOL columns
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')

# Apply gene mapping to get gene expression data 
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the mapped gene expression data
print("Gene expression data after mapping:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
print("\nFirst 20 gene symbols:")
print(list(gene_data.index)[:20])

# Save the gene expression data
gene_data.to_csv(out_gene_data_file)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

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

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."
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=note
)

# 6. Save linked data only if usable 
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)