output/preprocess/Autoinflammatory_Disorders/code/GSE80060.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autoinflammatory_Disorders"
cohort = "GSE80060"

# Input paths
in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders"
in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE80060"

# Output paths
out_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/GSE80060.csv"
out_gene_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/gene_data/GSE80060.csv"
out_clinical_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/clinical_data/GSE80060.csv"
json_path = "./output/z1/preprocess/Autoinflammatory_Disorders/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
import pandas as pd

# 1) Gene expression data availability
is_gene_available = True  # Title indicates "Gene expression data"; not miRNA/methylation

# 2) Variable availability
# From Sample Characteristics Dictionary:
# 1: ['disease status: SJIA', 'disease status: Healthy'] -> maps to our trait (Autoinflammatory_Disorders)
trait_row = 1
age_row = None         # No age field found
gender_row = None      # No gender field found

# 2.2) Converters
def _after_colon(value: str) -> str:
    s = str(value)
    if ':' in s:
        s = s.split(':', 1)[1]
    return s.strip()

def convert_trait(value):
    if pd.isna(value):
        return None
    v = _after_colon(value).lower()
    # Heuristics: SJIA is an autoinflammatory disease -> 1; Healthy/Control -> 0
    if 'sjia' in v or ('patient' in v and 'healthy' not in v):
        return 1
    if 'healthy' in v or 'control' in v or v == 'normal':
        return 0
    return None

def convert_age(value):
    if pd.isna(value):
        return None
    v = _after_colon(value).strip()
    if v == '' or v.lower() in {'na', 'n/a', 'nan', 'none', 'unknown'}:
        return None
    # Try direct float
    try:
        return float(v)
    except Exception:
        pass
    low = v.lower()
    m = re.search(r'(\d+(\.\d+)?)', low)
    if not m:
        return None
    num = float(m.group(1))
    # Convert to years if units provided
    if 'month' in low or re.search(r'\bmo\b', low):
        return round(num / 12.0, 3)
    if 'week' in low or re.search(r'\bwk\b', low):
        return round(num / 52.0, 3)
    if 'day' in low or re.search(r'\bd\b', low):
        return round(num / 365.0, 3)
    return num  # assume years

def convert_gender(value):
    if pd.isna(value):
        return None
    v = _after_colon(value).strip().lower()
    if v in {'m', 'male'} or 'male' in v or 'man' in v or 'boy' in v:
        return 1
    if v in {'f', 'female'} or 'female' in v or 'woman' in v or 'girl' in v:
        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 trait_row is 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)
    print("Preview of selected clinical features:", preview)

    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
# Identify the columns for probe IDs and gene symbols based on the annotation preview
probe_col = 'ID'            # matches probe identifiers like '1007_s_at'
gene_symbol_col = 'Gene Symbol'  # contains gene symbols (may include multiple per probe)

# Build mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# 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)

# 2. Link clinical and genetic data (use the correct variable from Step 2)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# Derive availability flags based on current data state and cast to built-in bool
is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
is_trait_available = bool((trait in linked_data.columns) and (linked_data[trait].notna().sum() > 0))

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

# 4. Determine biases and remove biased demographic features
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
is_trait_biased = bool(is_trait_biased)

# 5. Final quality validation and save cohort info
covariate_cols = [trait, 'Age', 'Gender']
gene_cols_in_final = [c for c in unbiased_linked_data.columns if c not in covariate_cols]
sample_count = int(len(unbiased_linked_data))
gene_count = int(len(gene_cols_in_final))
note = (
    f"INFO: Normalized Affymetrix probe data to gene symbols using NCBI synonyms. "
    f"Clinical features available: trait only; Age/Gender not provided. "
    f"Post-QC samples: {sample_count}; genes: {gene_count}."
)
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=bool(is_gene_available),
    is_trait_available=bool(is_trait_available),
    is_biased=bool(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)
    print(f"Saved processed cohort to {out_data_file}")
    print(f"Saved gene data to {out_gene_data_file}")