p3/preprocess/Generalized_Anxiety_Disorder/code/GSE61672.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Generalized_Anxiety_Disorder"
cohort = "GSE61672"

# Input paths
in_trait_dir = "../DATA/GEO/Generalized_Anxiety_Disorder"
in_cohort_dir = "../DATA/GEO/Generalized_Anxiety_Disorder/GSE61672"

# Output paths
out_data_file = "./output/preprocess/3/Generalized_Anxiety_Disorder/GSE61672.csv"
out_gene_data_file = "./output/preprocess/3/Generalized_Anxiety_Disorder/gene_data/GSE61672.csv"
out_clinical_data_file = "./output/preprocess/3/Generalized_Anxiety_Disorder/clinical_data/GSE61672.csv"
json_path = "./output/preprocess/3/Generalized_Anxiety_Disorder/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 
is_gene_available = True  # Series description mentions genome-wide gene expression

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 4  # anxiety case/control is in row 4
age_row = 0  # age data in row 0
gender_row = 1  # Sex data in row 1

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not isinstance(value, str):
        return None
    if "anxiety case/control:" not in value:
        return None
    value = value.split("anxiety case/control:")[1].strip()
    if value == "case":
        return 1
    elif value == "control":
        return 0
    return None

def convert_age(value):
    if not isinstance(value, str):
        return None
    if "age:" not in value:
        return None
    try:
        age = int(value.split("age:")[1].strip())
        return age
    except:
        return None

def convert_gender(value):
    if not isinstance(value, str):
        return None
    if "Sex:" not in value:
        return None
    sex = value.split("Sex:")[1].strip()
    if sex == "F":
        return 0
    elif sex == "M":
        return 1
    return None

# 3. Save 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. Clinical Feature Extraction
clinical_data = pd.DataFrame({'Sample': [], 'Value': []})
clinical_data.set_index('Sample', inplace=True)

selected_clinical = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                               age_row, convert_age, 
                                               gender_row, convert_gender)
print("Preview of selected clinical features:")
print(preview_df(selected_clinical))
selected_clinical.to_csv(out_clinical_data_file)
# 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()
# 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 are Illumina probe IDs (starting with "ILMN_"), not human gene symbols
# They need to be mapped to official gene symbols for analysis
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)
# Extract ID and Symbol columns for mapping
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Symbol')

# Convert probe expression values to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Normalize gene symbols in resulting dataframe
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save normalized gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Extract clinical features from clinical_data and save
selected_clinical = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                               age_row, convert_age, 
                                               gender_row, convert_gender)
selected_clinical.to_csv(out_clinical_data_file)

# 3. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)

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

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

# 6. Validate data quality and save cohort info
note = "Blood gene expression data from Generalized Anxiety Disorder patients and healthy controls, with good sample size and complete trait information."
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
)

# 7. 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.")
# 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])
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Extract ID and Symbol columns for mapping
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Symbol')

# Convert probe expression values to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Get gene expression data and mapping
genetic_data = get_genetic_data(matrix_file_path)
gene_annotation = get_gene_annotation(soft_file_path)
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Symbol')
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# 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)

# 2. Extract and save clinical features 
selected_clinical = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                               age_row, convert_age, 
                                               gender_row, convert_gender)
selected_clinical.to_csv(out_clinical_data_file)

# 3. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)

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

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

# 6. Validate data quality and save cohort info
note = "Blood gene expression data from Generalized Anxiety Disorder patients and healthy controls, with good sample size and complete trait information."
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
)

# 7. 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.")
# 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, we can see this is a blood gene expression study
is_gene_available = True

# 2.1 Identify row numbers for clinical features
trait_row = 4  # 'anxiety case/control' appears in row 4
age_row = 0    # 'age' appears in row 0 
gender_row = 1  # 'Sex' appears in row 1

# 2.2 Define conversion functions
def convert_trait(value: Any) -> Optional[float]:
    """Convert anxiety case/control status to binary"""
    if pd.isna(value):
        return None
    if isinstance(value, (int, float)):
        return float(value)
    if not value or value == '.':
        return None
    value = str(value).split(': ')[-1].lower()
    if 'case' in value:
        return 1.0
    elif 'control' in value:
        return 0.0
    return None

def convert_age(value: Any) -> Optional[float]:
    """Convert age to float"""
    if pd.isna(value):
        return None
    if isinstance(value, (int, float)):
        return float(value)
    if not value or value == '.':
        return None
    try:
        return float(str(value).split(': ')[-1])
    except:
        return None

def convert_gender(value: Any) -> Optional[float]:
    """Convert gender to binary (F=0, M=1)"""
    if pd.isna(value):
        return None
    if isinstance(value, (int, float)):
        return float(value)
    if not value or value == '.':
        return None
    value = str(value).split(': ')[-1].upper()
    if value == 'F':
        return 0.0
    elif value == 'M':
        return 1.0
    return None

# 3. Save metadata
is_trait_available = trait_row is not None  # True in this case
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. Extract clinical features and save
if clinical_data is not None and len(clinical_data) > 0:
    selected_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)

    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))

    # Save clinical data
    selected_clinical_df.to_csv(out_clinical_data_file)
else:
    print("Warning: clinical_data is empty or not initialized")
# 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])