output/preprocess/Endometrioid_Cancer/code/GSE120490.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE120490"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE120490"

# Output paths
out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE120490.csv"
out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE120490.csv"
out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE120490.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  # Affymetrix U133 Plus 2.0 microarray platform indicates gene expression data

# 2. Variable Availability and Data Type Conversion
trait_row = 0  # 'matastasis: No/Yes' relates to cancer metastasis status
age_row = None  # No age information available in sample characteristics
gender_row = None  # No gender information available (endometrial cancer typically affects females)

def convert_trait(value):
    """Convert metastasis status to binary: No=0, Yes=1"""
    if value is None:
        return None
    value_str = str(value).split(':')[-1].strip().lower()
    if value_str == 'no':
        return 0
    elif value_str == 'yes':
        return 1
    else:
        return None

def convert_age(value):
    """Age conversion function (not used as age data not available)"""
    return None

def convert_gender(value):
    """Gender conversion function (not used as gender data not available)"""
    return None

# 3. Save Metadata
is_trait_available = trait_row is not None
save_cohort_info = 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 is_trait_available:
    selected_clinical_data = 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
    )
    
    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
# Analyze the gene identifiers from the previous step output
gene_identifiers = ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',
                   '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',
                   '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',
                   '1552263_at', '1552264_a_at', '1552266_at']

print("Sample gene identifiers:")
for identifier in gene_identifiers[:10]:
    print(f"  {identifier}")

# These identifiers follow the Affymetrix probe ID pattern with suffixes like "_at", "_s_at", "_a_at", etc.
# They are not human gene symbols (which would be like TP53, BRCA1, EGFR, etc.)
# Therefore, they require mapping to gene symbols

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 mapping columns: 'ID' contains probe IDs, 'Gene Symbol' contains gene symbols
probe_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get gene mapping dataframe
gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)

# 3. Apply gene mapping to convert probe-level measurements 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("Sample gene symbols:")
print(gene_data.index[:10].tolist())

# 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)