output/preprocess/Aniridia/code/GSE137996.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Aniridia"
cohort = "GSE137996"

# Input paths
in_trait_dir = "../DATA/GEO/Aniridia"
in_cohort_dir = "../DATA/GEO/Aniridia/GSE137996"

# Output paths
out_data_file = "./output/z1/preprocess/Aniridia/GSE137996.csv"
out_gene_data_file = "./output/z1/preprocess/Aniridia/gene_data/GSE137996.csv"
out_clinical_data_file = "./output/z1/preprocess/Aniridia/clinical_data/GSE137996.csv"
json_path = "./output/z1/preprocess/Aniridia/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
import os
import re

# 1. Gene Expression Data Availability
is_gene_available = True  # mRNA profiling present (not purely miRNA/methylation)

# 2. Variable Availability and Converters
# Identified rows from the Sample Characteristics Dictionary
trait_row = 2   # 'disease: AAK' vs 'disease: healthy control'
age_row = 0     # 'age: ...'
gender_row = 1  # 'gender: F', 'gender: W', 'gender: M'

def _extract_value(x):
    if x is None:
        return ""
    s = str(x)
    return s.split(":", 1)[1].strip() if ":" in s else s.strip()

def convert_trait(x):
    val = _extract_value(x).lower()
    case_set = {
        'aak',
        'aniridia',
        'congenital aniridia',
        'aniridia-associated keratopathy',
        'aniridia associated keratopathy'
    }
    control_set = {'healthy control', 'healthy', 'control', 'normal'}
    if val in case_set:
        return 1
    if val in control_set:
        return 0
    return None

def convert_age(x):
    val = _extract_value(x)
    m = re.search(r'[-+]?\d+\.?\d*', val)
    if m:
        try:
            v = float(m.group())
            return v
        except Exception:
            return None
    return None

def convert_gender(x):
    val = _extract_value(x).lower()
    # Heuristics: M/male/man -> 1; F/female/woman/W -> 0
    if val in {'m', 'male', 'man'} or val.startswith('m'):
        return 1
    if val in {'f', 'female', 'woman', 'w'} or val.startswith('f') or val.startswith('w'):
        return 0
    return None

# 3. Save Metadata (initial filtering)
is_trait_available = trait_row is not None
_ = 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 if clinical data available)
if trait_row is not None:
    selected_clinical_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 = preview_df(selected_clinical_df, n=5)
    print(preview)

    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

# 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
print("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
# Identify the appropriate columns for probe IDs and gene symbols based on the annotation preview
probe_col = 'ID'
gene_symbol_col = 'GENE_SYMBOL'

# 2. Create the mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

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

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save gene expression data
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. Link the clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# Flags for availability before stringent missing handling (ensure native Python bool)
is_gene_available_flag = bool(normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
is_trait_available_flag = bool((trait in list(linked_data.columns)) and linked_data[trait].notna().any())

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

# 4. Bias evaluation and removal of biased demographics
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# Ensure columns are a Python list to avoid numpy.bool_ in membership checks inside the validator
unbiased_linked_data.columns = list(unbiased_linked_data.columns)
is_trait_biased = bool(is_trait_biased)

# 5. Final validation and metadata saving
note = (
    f"INFO: Normalized gene symbols and linked with clinical data. "
    f"Gene matrix shape (normalized): {normalized_gene_data.shape}. "
    f"Linked data shape after QC: {unbiased_linked_data.shape}."
)
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available_flag,
    is_trait_available=is_trait_available_flag,
    is_biased=is_trait_biased,
    df=unbiased_linked_data,
    note=note
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)