p3/preprocess/Sjögrens_Syndrome/code/GSE84844.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sjögrens_Syndrome"
cohort = "GSE84844"

# Input paths
in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE84844"

# Output paths
out_data_file = "./output/preprocess/3/Sjögrens_Syndrome/GSE84844.csv"
out_gene_data_file = "./output/preprocess/3/Sjögrens_Syndrome/gene_data/GSE84844.csv"
out_clinical_data_file = "./output/preprocess/3/Sjögrens_Syndrome/clinical_data/GSE84844.csv"
json_path = "./output/preprocess/3/Sjögrens_Syndrome/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 Availability
# Yes, this is whole blood transcriptome data based on the background information
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Find row indices 
trait_row = 0    # Disease status in row 0
age_row = 2      # Age in row 2  
gender_row = 3   # Gender in row 3

# 2.2 Conversion functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1]
    if 'sjogren' in x or 'ss' in x:
        return 1
    elif 'healthy' in x or 'control' in x:
        return 0
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        return float(x.split(': ')[-1])
    except:
        return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1]
    if x == 'female':
        return 0
    elif x == 'male':
        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=True)

# 4. Extract clinical features
clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                         age_row, convert_age, 
                                         gender_row, convert_gender)

# Preview the extracted features
print("Preview of extracted clinical features:")
print(preview_df(clinical_df))

# Save clinical data
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])
# The probe IDs are in Affymetrix format (e.g. '1007_s_at', '1053_at') which need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# Get probe-to-gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Save gene expression data 
gene_data.to_csv(out_gene_data_file)

# Preview the mapped gene data
print("\nGene expression data shape after mapping:", gene_data.shape)
print("\nFirst few gene symbols:")
print(list(gene_data.index)[:20])
# 1. Normalize gene symbols in gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", gene_data.shape) 

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    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 = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
    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 and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)