output/preprocess/Endometrioid_Cancer/code/GSE94523.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE94523"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94523"

# Output paths
out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE94523.csv"
out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE94523.csv"
out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE94523.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  # Series mentions "Microarray Expression" and "Gene expression profiling"

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
trait_row = None  # Only one unique value 'tissue: endometrioid adenocarcinoma', which is constant
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 (0/1)"""
    if value is None:
        return None
    value = str(value).split(':')[-1].strip().lower()
    if 'endometrioid' in value or 'adenocarcinoma' in value:
        return 1
    else:
        return 0

def convert_age(value):
    """Convert age to continuous numeric values"""
    if value is None:
        return None
    try:
        value = str(value).split(':')[-1].strip()
        return float(value)
    except:
        return None

def convert_gender(value):
    """Convert gender to binary (0=female, 1=male)"""
    if value is None:
        return None
    value = str(value).split(':')[-1].strip().lower()
    if 'female' in value or 'f' in value:
        return 0
    elif 'male' in value or 'm' in value:
        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
# Skipping this step since trait_row is None (no clinical data 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 output
gene_identifiers = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20']

# Check if these look like human gene symbols
# Gene symbols are typically alphanumeric strings with letters, not just numbers
# Examples: TP53, BRCA1, EGFR, MYC, etc.
print("Sample gene identifiers:", gene_identifiers[:10])

# These are clearly numeric identifiers (as strings), not gene symbols
# Gene symbols would contain letters and be more descriptive
# These appear to be 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 mapping columns: 'ID' matches gene expression identifiers, 'HUGO' contains gene symbols
prob_col = 'ID'
gene_col = 'HUGO'

# 2. Get gene mapping dataframe using the library function
gene_mapping = get_gene_mapping(gene_annotation, prob_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(f"First few gene symbols: {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. Since no clinical data is available (trait_row = None from Step 2), create empty clinical dataframe
# and link only genetic data
clinical_data_empty = pd.DataFrame()
linked_data = normalized_gene_data.T  # Transpose to have samples as rows

# 3. Since no trait data is available, skip missing value handling for clinical features
# Only handle missing values in genetic data
linked_data = linked_data.fillna(linked_data.mean())

# 4. Since no trait data exists, the dataset is biased/unusable for associative studies
# All samples have the same constant trait value (endometrioid adenocarcinoma)
is_trait_biased = True
unbiased_linked_data = linked_data

# 5. Conduct quality check and save the cohort information
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort, 
    info_path=json_path, 
    is_gene_available=True, 
    is_trait_available=False,  # No usable trait data available
    is_biased=is_trait_biased, 
    df=unbiased_linked_data,
    note="INFO: Dataset contains only constant trait values (all endometrioid adenocarcinoma), not suitable for associative studies"
)

# 6. Since dataset is not usable, do not save the linked data file
if is_usable:
    unbiased_linked_data.to_csv(out_data_file)