output/preprocess/Endometrioid_Cancer/code/GSE73551.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE73551"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73551"

# Output paths
out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE73551.csv"
out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE73551.csv"
out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE73551.csv"
json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"


# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)

# Step 2: Dataset Analysis and Clinical Feature Extraction
# 1. Gene Expression Data Availability
is_gene_available = True  # This appears to be gene expression data for compositional analysis

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
trait_row = 0  # Cell type information is in row 0, includes ENDOMETRIOID
age_row = None  # No age information available
gender_row = None  # No gender information available

# 2.2 Data Type Conversion
def convert_trait(value):
    """Convert cell type to binary: 1 for ENDOMETRIOID, 0 for others"""
    if pd.isna(value):
        return None
    value_str = str(value).upper()
    if ':' in value_str:
        value_str = value_str.split(':', 1)[1].strip()
    return 1 if 'ENDOMETRIOID' in value_str else 0

def convert_age(value):
    """Convert age to continuous (not available in this dataset)"""
    return None

def convert_gender(value):
    """Convert gender to binary (not available in this dataset)"""
    return None

# 3. Save Metadata
is_trait_available = trait_row is not None
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. Clinical Feature Extraction
if trait_row is not None:
    selected_clinical_data = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait, 
                                                        age_row, convert_age, gender_row, convert_gender)
    print("Preview of selected clinical data:")
    print(preview_df(selected_clinical_data))
    
    # Save clinical data
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_data.to_csv(out_clinical_data_file)
    print(f"Clinical data saved to {out_clinical_data_file}")

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
# Examine the gene identifiers to determine if mapping is required
print("Gene identifiers observed:")
print("Index(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',")
print("       '14', '15', '16', '17', '18', '19', '20'],")
print("      dtype='object', name='ID')")
print("\nThese are numerical identifiers, not human gene symbols.")
print("Human gene symbols are typically alphanumeric (e.g., TP53, BRCA1, EGFR).")
print("These numerical IDs likely represent probe IDs or platform-specific identifiers.")

requires_gene_mapping = True

# Step 5: Gene Annotation
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))

# Step 6: Gene Identifier Mapping
# 1. Identify the correct columns for mapping
# The 'ID' column matches the gene identifiers in gene expression data
# The 'GeneSymbol' column contains the actual gene symbols
prob_col = 'ID'
gene_col = 'GeneSymbol'

# 2. Get gene mapping dataframe using the get_gene_mapping function
gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, gene_mapping)

print(f"Gene expression data shape after mapping: {gene_data.shape}")
print(f"First 10 genes: {list(gene_data.index[:10])}")

# Step 7: Data Normalization and Linking
# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

# 3. Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct quality check and save the cohort information.
is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)

# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
if is_usable:
    unbiased_linked_data.to_csv(out_data_file)