p1/preprocess/Endometriosis/code/GSE145701.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE145701"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE145701"

# Output paths
out_data_file = "./output/preprocess/1/Endometriosis/GSE145701.csv"
out_gene_data_file = "./output/preprocess/1/Endometriosis/gene_data/GSE145701.csv"
out_clinical_data_file = "./output/preprocess/1/Endometriosis/clinical_data/GSE145701.csv"
json_path = "./output/preprocess/1/Endometriosis/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)
# 1. Gene Expression Data Availability
# Based on the background information, this dataset uses an Affymetrix Human Gene 1.0 ST array
# (i.e., gene-expression microarray). Therefore, it likely contains gene expression data.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Identify the corresponding rows for each variable.
#    From the sample characteristics dictionary, disease state data is in row 2, 
#    which clearly differentiates Normal and Endometriosis (Stage I, Stage IV). 
#    Hence, trait_row = 2. Age and gender are either missing or constant, so set them to None.

trait_row = 2
age_row = None
gender_row = None

# 2.2 Choose data types and write conversion functions.
#     Trait data => binary (Normal -> 0, Endometriosis -> 1).
#     Age/gender are unavailable, so the conversion functions will simply return None.

def convert_trait(value: str) -> Optional[int]:
    """
    Convert disease state string to a binary integer:
    Normal -> 0, Endometriosis Stage I/IV -> 1.
    Unknown values -> None.
    """
    # Extract the part after colon, if present.
    parts = value.split(':', 1)
    if len(parts) == 2:
        val = parts[1].strip().lower()
    else:
        val = value.strip().lower()
    if "normal" in val:
        return 0
    elif "endo" in val:  # covers "Endometriosis stage I" and "Endometriosis stage IV"
        return 1
    return None

def convert_age(value: str) -> Optional[float]:
    # Age data is not provided or is constant, so we treat it as not available.
    return None

def convert_gender(value: str) -> Optional[int]:
    # Gender is all "Female", so it is constant and effectively unavailable.
    return None

# 3. Save Metadata (initial filtering)
#    Trait data is available if trait_row is not None.
#    So is_trait_available = True. Then we call validate_and_save_cohort_info with is_final=False.
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
#    This step is only performed if trait_row is not None (meaning clinical data is available).
if trait_row is not None:
    # Suppose the previously obtained clinical data is in a DataFrame called clinical_data.
    # Extract relevant features:
    selected_clinical_df = geo_select_clinical_features(
        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 data
    preview_dict = preview_df(selected_clinical_df, n=5, max_items=200)

    # Save the clinical features to CSV
    selected_clinical_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])
print("These IDs appear to be numeric probe identifiers rather than standard human gene symbols.")
print("requires_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 matching columns in the gene_annotation DataFrame:
#    - 'ID' contains the probe identifiers matching gene_data.index
#    - 'gene_assignment' contains gene symbol information

probe_col = "ID"
gene_symbol_col = "gene_assignment"

# 2. Create a mapping DataFrame from probe ID to gene symbol
mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)

# 3. Apply the mapping to convert probe-level expressions to gene-level expressions
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (Optional) Print summary for verification
print(f"Finished mapping probes to gene symbols. Gene expression matrix shape: {gene_data.shape}")
print("First 10 gene symbols in final gene_data index:")
print(gene_data.index[:10])
# STEP 7

import pandas as pd

# 1. Normalize the gene expression data to standard gene symbols.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print("Normalized gene expression data saved to:", out_gene_data_file)

# 2. Read back the clinical data, reassign its single row index to the trait name, and link with genetic data.
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
selected_clinical_df.index = [trait]  # Ensure the clinical row is labeled by the trait
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values systematically.
df = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
trait_biased, df = judge_and_remove_biased_features(df, trait)

# 5. Perform final validation with full dataset information.
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=df,
    note="Final step with linking, missing-value handling, bias checks."
)

# 6. If the data is usable, save the final linked data.
if is_usable:
    df.to_csv(out_data_file)
    print(f"Final linked data saved to: {out_data_file}")
else:
    print("Dataset is not usable or severely biased. No final data saved.")