p3/preprocess/Schizophrenia/code/GSE193818.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Schizophrenia"
cohort = "GSE193818"

# Input paths
in_trait_dir = "../DATA/GEO/Schizophrenia"
in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE193818"

# Output paths
out_data_file = "./output/preprocess/3/Schizophrenia/GSE193818.csv"
out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE193818.csv"
out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE193818.csv"
json_path = "./output/preprocess/3/Schizophrenia/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
# From the Series description, this is RNA data from blood samples
is_gene_available = True

# 2.1 Data Availability
# All patients have schizophrenia according to Series title/summary
trait_row = None
# Age available in row 1
age_row = 1  
# Gender available in row 0
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # All samples have schizophrenia, but this row is not available
    return None

def convert_age(x):
    if pd.isna(x):
        return None
    try:
        # Extract numeric value after colon
        age = float(x.split(': ')[1])
        return age
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    value = x.split(': ')[1].lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# 3. Save Metadata 
# is_trait_available is False since all samples have schizophrenia (constant)
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=False)

# 4. Clinical Feature Extraction skipped since trait_row is None
# 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 IDs look like Affymetrix probe IDs (e.g., 'AFFX-BkGr-GC03_st')
# These are not standard human gene symbols and will need to be mapped
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))
# The gene expression data uses probe IDs similar to 'AFFX-BkGr-GC03_st'
# The gene annotation contains detailed information about genes in 'SPOT_ID.1'
# We need to extract gene symbols from this field and pair them with IDs

# Get mapping dataframe containing probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID.1')

# Apply mapping to convert probe-level 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)

print("Gene data shape:", gene_data.shape)
print("\nFirst few genes and samples:")
print(preview_df(gene_data))
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
print("Gene data shape after normalization:", gene_data.shape)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
gene_data.to_csv(out_gene_data_file)

# Mark dataset as unusable due to constant trait
note = "All samples have schizophrenia (constant trait), making the dataset unsuitable for associational studies."
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,  # Since constant trait = 100% bias
    df=gene_data,    # Gene data serves as the dataset
    note=note
)