p1/preprocess/Bile_Duct_Cancer/code/GSE107754.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bile_Duct_Cancer"
cohort = "GSE107754"

# Input paths
in_trait_dir = "../DATA/GEO/Bile_Duct_Cancer"
in_cohort_dir = "../DATA/GEO/Bile_Duct_Cancer/GSE107754"

# Output paths
out_data_file = "./output/preprocess/1/Bile_Duct_Cancer/GSE107754.csv"
out_gene_data_file = "./output/preprocess/1/Bile_Duct_Cancer/gene_data/GSE107754.csv"
out_clinical_data_file = "./output/preprocess/1/Bile_Duct_Cancer/clinical_data/GSE107754.csv"
json_path = "./output/preprocess/1/Bile_Duct_Cancer/cohort_info.json"

# STEP1
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)
import pandas as pd

# 1) Determine gene expression data availability
is_gene_available = True  # The summary indicates "Whole human genome gene expression microarrays"

# 2) Variable Availability and Data Type Conversion
# After reviewing the sample characteristics:
#   - trait_row (for Bile_Duct_Cancer) is 2, because "tissue: Bile duct cancer" appears among various tissues.
#   - age_row is None, no age information found.
#   - gender_row is 0, as "gender: Male" and "gender: Female" appear there.

trait_row = 2
age_row = None
gender_row = 0

# Conversion functions
def convert_trait(x: str):
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    # Binary conversion: 1 if it's Bile duct cancer, 0 otherwise
    return 1 if val == 'bile duct cancer' else 0

def convert_age(x: str):
    # Age data not available, return None
    return None

def convert_gender(x: str):
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'male':
        return 1
    elif val == 'female':
        return 0
    return None

# 3) Save Metadata (initial filtering)
# trait data is available (trait_row is not None) => is_trait_available = True
is_trait_available = (trait_row is not None)

is_usable = 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 (only do this step if trait_row is not None)
if trait_row is not None:
    # Suppose the clinical_data dataframe is already loaded in the environment
    clinical_features_df = 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
    )

    # Preview the extracted clinical features
    preview_result = preview_df(clinical_features_df, n=5)
    print("Clinical Features Preview:", preview_result)

    # Save clinical data
    clinical_features_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 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])
# Based on review, these identifiers (e.g., A_23_P100001) appear to be microarray probe set IDs,
# not standard human gene symbols, hence gene mapping is required.
print("\nrequires_gene_mapping = True")
# STEP5
# 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: Gene Identifier Mapping

# 1) Identify the columns in the annotation dataframe that match the IDs in the gene expression data
#    and which store the human gene symbols. In this case, "ID" matches "A_23_P..." probe IDs,
#    and "GENE_SYMBOL" stores the actual gene symbols.

# 2) Extract the mapping between these columns into a separate dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# 3) Convert probe-level measurements to gene-level measurements
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (Optional demonstration) Print shape or a small snippet to verify
print("Mapped gene_data shape:", gene_data.shape)
print("First few gene symbols in the mapped gene_data index:")
print(gene_data.index[:10].tolist())
# STEP7

# 1. Normalize the obtained gene data using the NCBI Gene synonym database
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
#    Use the correct variable name from previous steps: "clinical_features_df"
linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)

# 3. Handle missing values systematically using the actual trait name
linked_data_processed = handle_missing_values(linked_data, trait_col=trait)

# 4. Check for biased trait and remove any biased demographic features
trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)

# 5. Final quality validation and metadata saving
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_final,
    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
)

# 6. If dataset is usable, save the final linked data
if is_usable:
    linked_data_final.to_csv(out_data_file)