output/preprocess/Endometrioid_Cancer/code/GSE73614.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE73614"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73614"

# Output paths
out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE73614.csv"
out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE73614.csv"
out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE73614.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

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
trait_row = None  # No trait information available in sample characteristics
age_row = None    # No age information available in sample characteristics  
gender_row = None # No gender information available in sample characteristics

# 2.2 Data Type Conversion
def convert_trait(value):
    """Convert trait values to binary (0 for non-endometrioid, 1 for endometrioid)"""
    if value is None:
        return None
    value_str = str(value).lower()
    if 'endometrioid' in value_str:
        return 1
    else:
        return 0

def convert_age(value):
    """Convert age to continuous values"""
    if value is None:
        return None
    try:
        if ':' in str(value):
            age_str = str(value).split(':')[1].strip()
        else:
            age_str = str(value).strip()
        return float(age_str)
    except (ValueError, IndexError):
        return None

def convert_gender(value):
    """Convert gender to binary (0 for female, 1 for male)"""
    if value is None:
        return None
    value_str = str(value).lower()
    if ':' in value_str:
        value_str = value_str.split(':')[1].strip()
    
    if 'female' in value_str or 'f' == value_str:
        return 0
    elif 'male' in value_str or 'm' == value_str:
        return 1
    else:
        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
# Skip this step since trait_row is None (clinical data not available)

# 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 from the previous step
gene_identifiers_sample = ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056',
                          'A_23_P100074', 'A_23_P100092', 'A_23_P100103', 'A_23_P100111',
                          'A_23_P100127', 'A_23_P100133', 'A_23_P100141', 'A_23_P100156',
                          'A_23_P100177', 'A_23_P100189', 'A_23_P100196', 'A_23_P100203',
                          'A_23_P100220', 'A_23_P100240', 'A_23_P10025', 'A_23_P100263']

print("Sample gene identifiers:")
for i, identifier in enumerate(gene_identifiers_sample[:5]):
    print(f"  {identifier}")

# Analysis: These identifiers follow the pattern "A_23_P" + numbers
# This is the standard format for Agilent microarray probe IDs
# The "A_23_P" prefix indicates Agilent platform probe identifiers
# These are not human gene symbols (which would be like BRCA1, TP53, etc.)
# Therefore, they need to be mapped to gene symbols for meaningful analysis

print("\nAnalysis: These are Agilent microarray probe IDs (A_23_P prefix)")
print("They are not human gene symbols and 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 the mapping columns
# Gene identifiers in expression data match 'ID' column in annotation
# Gene symbols are in 'GENE_SYMBOL' column
prob_col = 'ID'
gene_col = 'GENE_SYMBOL'

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

# 3. Apply gene mapping to convert probe-level 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 5 gene symbols: {list(gene_data.index[:5])}")

# Step 7: Data Normalization and Linking
import os

# 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)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Since no clinical data is available (trait_row was None), create empty clinical dataframe
empty_clinical_data = pd.DataFrame()
linked_data = geo_link_clinical_genetic_data(empty_clinical_data, normalized_gene_data)

# 5. Conduct quality check and save the cohort information
os.makedirs(os.path.dirname(json_path), exist_ok=True)
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort, 
    info_path=json_path, 
    is_gene_available=True, 
    is_trait_available=False, 
    is_biased=False,  # Placeholder value since no trait data available
    df=linked_data, 
    note="INFO: Dataset contains gene expression data but no trait information available for analysis"
)

# 6. Since no trait data is available, the dataset is not usable - do not save linked data
print("Dataset not saved - no trait information available for associational study")