p3/preprocess/Anxiety_disorder/code/GSE78104.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"
cohort = "GSE78104"

# Input paths
in_trait_dir = "../DATA/GEO/Anxiety_disorder"
in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE78104"

# Output paths
out_data_file = "./output/preprocess/3/Anxiety_disorder/GSE78104.csv"
out_gene_data_file = "./output/preprocess/3/Anxiety_disorder/gene_data/GSE78104.csv"
out_clinical_data_file = "./output/preprocess/3/Anxiety_disorder/clinical_data/GSE78104.csv"
json_path = "./output/preprocess/3/Anxiety_disorder/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
# This dataset contains both mRNA and lncRNA expression data, which is suitable
is_gene_available = True

# 2.1 Variable Availability
# Trait data is in row 1 - "disease state"
trait_row = 1  
# Age data is in row 3
age_row = 3
# Gender data is in row 2 
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Extract value after colon and trim whitespace
    val = value.split(":")[1].strip()
    # Convert to binary: OCD = 1, control = 0
    if "Obsessive-Compulsive Disorder" in val:
        return 1
    elif "normal control" in val:
        return 0
    return None

def convert_age(value):
    # Extract age value and convert to float
    try:
        # Extract number before 'y'
        age = float(value.split(":")[1].strip().replace("y",""))
        return age
    except:
        return None

def convert_gender(value):
    # Extract gender and convert to binary
    val = value.split(":")[1].strip()
    if "female" in val.lower():
        return 0
    elif "male" in val.lower():
        return 1
    return 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 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 data
    print("Clinical features preview:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# 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 gene identifiers shown, these appear to be microarray probe IDs 
# (e.g. '(+)E1A_r60_1', 'A_19_P00315459') rather than human gene symbols.
# These will need to be mapped to standard gene symbols.
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# 1. The 'ID' column in gene annotation matches the probe identifiers in gene expression data
# The 'GeneSymbol' column contains corresponding gene symbols

# 2. Create mapping dataframe with probe ID and gene symbol columns
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GeneSymbol')

# 3. Apply gene mapping to convert probe measurements to gene expression values
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Print preview of gene-level data
print("Mapped gene expression data preview:")
print("Shape:", gene_data.shape)
print("\nFirst few rows:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene data
gene_data.to_csv(out_gene_data_file)

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

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

    # 4. Determine if features are biased
    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. 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_trait_biased,
        df=linked_data,
        note="Gene expression data successfully mapped and linked with clinical features"
    )

    # 6. Save linked data only if usable AND trait is not biased
    if is_usable and not is_trait_biased:
        linked_data.to_csv(out_data_file)

except Exception as e:
    print(f"Error in data linking and processing: {str(e)}")
    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=True,
        df=pd.DataFrame(),
        note=f"Data processing failed: {str(e)}"
    )