p3/preprocess/Ovarian_Cancer/code/GSE201525.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Ovarian_Cancer"
cohort = "GSE201525"

# Input paths
in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE201525"

# Output paths
out_data_file = "./output/preprocess/3/Ovarian_Cancer/GSE201525.csv"
out_gene_data_file = "./output/preprocess/3/Ovarian_Cancer/gene_data/GSE201525.csv"
out_clinical_data_file = "./output/preprocess/3/Ovarian_Cancer/clinical_data/GSE201525.csv"
json_path = "./output/preprocess/3/Ovarian_Cancer/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# From the background info, this appears to be a gene expression dataset studying interferon effects
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability 
# All samples are ovarian cancer - trait info can be inferred from study design
trait_row = 0  # Treatment info row can be used to identify samples
age_row = None  # No age info available
gender_row = None  # No gender info available

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.split(": ")[-1].lower()
    # All samples are cancer cases
    return 1

def convert_age(value):
    # Not needed since age data unavailable
    return None

def convert_gender(value):
    # Not needed since gender data unavailable 
    return None

# 3. Save Metadata
is_usable = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True  # All samples are cancer cases
)

# 4. Clinical Feature Extraction
# Extract clinical features (trait data) since trait_row is available
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
)

# Preview the processed clinical data
preview_dict = preview_df(selected_clinical_df)
print("\nPreview of processed clinical data:")
print(preview_dict)

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
is_gene_available = True

# Save updated 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)
)

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
# Looking at the gene identifiers in the index, they are numerical IDs (1,2,3,4,5)
# These are not standard human gene symbols and will need to be mapped
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# Extract mapping between probe IDs and gene symbols from annotation data
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

# Convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the gene data
print("Gene expression data preview:")
print("\nFirst 5 genes:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)

# Debug print to check data structure
print("Linked data preview:")
print(linked_data.head())
print("\nColumns:", linked_data.columns)

# 3. Transpose data to get samples as rows and genes/features as columns
linked_data = linked_data.T

# Debug print after transpose
print("\nAfter transpose:")
print(linked_data.head())
print("\nColumns:", linked_data.columns[:5], "...", len(linked_data.columns), "total columns")
print(f"\nTrait values:\n{linked_data[trait].value_counts()}")

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

# 5. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Gene expression data from ovarian cancer studying interferon treatment effects."
)

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