p3/preprocess/Sarcoma/code/GSE162785.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE162785"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE162785"

# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE162785.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE162785.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE162785.csv"
json_path = "./output/preprocess/3/Sarcoma/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 is likely available since this is a microarray analysis
is_gene_available = True

# 2. Variable availability and data type conversion
trait_row = 0 # cell line field contains information about Ewing Sarcoma cell lines
age_row = None # Age data not available 
gender_row = None # Gender data not available

def convert_trait(x):
    # Ewing Sarcoma (ES) cell lines indicate positive trait status
    # Extract cell line name after colon
    if not x or ':' not in x:
        return None
    cell_line = x.split(': ')[1].strip().upper()
    if cell_line in ['A673', 'CHLA-10', 'EW7', 'SK-N-MC']: 
        return 1 # ES cell line
    return 0 # Not ES cell line

def convert_age(x):
    return None # Not used since age data unavailable

def convert_gender(x):
    return None # Not used since gender data unavailable

# 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. Extract clinical features since trait_row is not None
clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait)

# Preview the extracted features
print(preview_df(clinical_features))

# Save clinical data
clinical_features.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 row IDs (7892501, 7892502, etc) appear to be probe IDs from a microarray platform
# rather than human gene symbols like BRCA1, TP53, etc.
# These numeric IDs need to be mapped to their corresponding gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation data
print("Column names and first few values:")
print(preview_df(gene_annotation))

# Based on the presence of "Homo sapiens" in the annotations, this is human data
# The gene_assignment column appears to contain probe-to-gene mappings we need
print("\nVerified human gene expression data with probe-to-gene mappings available.")
# The 'ID' column in gene annotations contains probe IDs matching the gene expression data indices
# The 'gene_assignment' column contains gene symbol information
# Extract mapping relationship between probe IDs and gene symbols, extracting gene symbols from the complex annotations
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# Apply the mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Normalize/standardize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print shape to verify the conversion
print("\nGene expression data shape after mapping:", gene_data.shape)
print("\nFirst few gene symbols:", list(gene_data.index)[:10])
# 1. Save normalized gene data (already normalized in previous step)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("Gene data shape:", 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
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 = "This cohort consists entirely of Ewing sarcoma cell lines according to the background 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=trait_biased,
        df=linked_data,
        note=note
    )

    # 6. Save linked data only if usable 
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)
else:
    # Handle case where clinical features were not properly extracted
    note = "Failed to extract clinical trait information from sample characteristics."
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path, 
        is_gene_available=True,
        is_trait_available=False,
        is_biased=None,
        df=None,
        note=note
    )