output/preprocess/Cystic_Fibrosis/code/GSE71799.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE71799"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE71799"

# Output paths
out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE71799.csv"
out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE71799.csv"
out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE71799.csv"
json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
from typing import Any, Optional
import pandas as pd

# 1) Gene expression availability
is_gene_available = True  # Gene expression analysis was performed (not miRNA/methylation only)

# 2) Variable availability and converters
# Based on the provided sample characteristics dictionary, no usable keys for trait/age/gender were found.
trait_row = None
age_row = None
gender_row = None

def _extract_after_colon(x: Any) -> str:
    if x is None or (isinstance(x, float) and pd.isna(x)):
        return ''
    s = str(x).strip()
    # Take the part after the last colon if present
    if ':' in s:
        s = s.split(':')[-1].strip()
    return s

def convert_trait(x: Any) -> Optional[int]:
    """
    Binary: 1 = cystic fibrosis, 0 = healthy control.
    Heuristics map common labels (e.g., 'CF', 'cystic fibrosis', 'uHC', 'control', 'healthy').
    """
    s = _extract_after_colon(x).lower()
    if not s:
        return None
    # Positive (CF) indicators
    if any(k in s for k in ['cystic fibrosis', ' cf ', ' cf', 'cf ', 'c.f.', 'cystic-fibrosis']):
        return 1
    if any(k in s for k in ['patient', 'case']) and 'control' not in s:
        return 1
    # Negative (control) indicators
    if any(k in s for k in ['healthy', 'control', 'uhc', 'unrelated healthy control']):
        return 0
    return None

def convert_age(x: Any) -> Optional[float]:
    """
    Continuous: extract numeric age in years if present.
    """
    s = _extract_after_colon(x).lower()
    if not s or s in {'na', 'n/a', 'nan', 'none', 'unknown', 'unk'}:
        return None
    m = re.search(r'[-+]?\d*\.?\d+', s)
    if m:
        try:
            return float(m.group())
        except ValueError:
            return None
    return None

def convert_gender(x: Any) -> Optional[int]:
    """
    Binary: female=0, male=1.
    """
    s = _extract_after_colon(x).lower()
    if not s:
        return None
    if s in {'male', 'm', 'man', 'boy'}:
        return 1
    if s in {'female', 'f', 'woman', 'girl'}:
        return 0
    if 'male' in s and 'fe' not in s:
        return 1
    if 'female' in s:
        return 0
    return None

# 3) Save metadata with 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 (skip because trait_row is None)
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_df(selected_clinical_df)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# 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
# 1-2. Identify relevant columns and create the mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 3. Apply 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)

# Determine availability of clinical trait data from previous steps
try:
    trait_row
except NameError:
    trait_row = None
try:
    age_row
except NameError:
    age_row = None
try:
    gender_row
except NameError:
    gender_row = None

# 2-6. Branch depending on clinical availability
linked_data = None
is_trait_available = trait_row is not None

if is_trait_available:
    # Recompute clinical features to ensure availability in this step
    selected_clinical_data = 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
    )
    # Optionally save clinical features for traceability
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_data.to_csv(out_clinical_data_file)

    # Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

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

    # 4. Assess bias and remove biased demographics
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Final 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=is_trait_biased,
        df=unbiased_linked_data,
        note="INFO: Linked clinical-genetic dataset generated."
    )

    # 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)
else:
    # Clinical trait unavailable: skip linking and downstream steps
    # Still record final metadata correctly with trait unavailable
    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,  # Ignored since is_available will be False
        df=normalized_gene_data,
        note="INFO: Trait/clinical features unavailable; saved normalized gene expression only."
    )