output/preprocess/Atrial_Fibrillation/code/GSE115574.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE115574"

# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE115574"

# Output paths
out_data_file = "./output/z1/preprocess/Atrial_Fibrillation/GSE115574.csv"
out_gene_data_file = "./output/z1/preprocess/Atrial_Fibrillation/gene_data/GSE115574.csv"
out_clinical_data_file = "./output/z1/preprocess/Atrial_Fibrillation/clinical_data/GSE115574.csv"
json_path = "./output/z1/preprocess/Atrial_Fibrillation/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 availability (Affymetrix human gene expression microarrays)
is_gene_available = True

# 2) Variable availability
trait_row = 0  # disease state: sinus rhythm vs atrial fibrillation
age_row = None
gender_row = None

# 2) Conversion functions
def convert_trait(x):
    if x is None:
        return None
    val = str(x).split(":", 1)[-1].strip().lower()
    # AF cases
    if ("atrial fibrillation" in val) or ("afib" in val) or re.search(r'\baf\b', val) or ("fibrillation" in val):
        return 1
    # Sinus rhythm cases
    if ("sinus rhythm" in val) or re.search(r'\bsr\b', val):
        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 (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=None,
        gender_row=gender_row,
        convert_gender=None
    )
    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)

# 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
# Map probe identifiers to gene symbols and convert to gene-level expression

# 1-2) Decide columns and create mapping dataframe
# Probe IDs in expression data match 'ID' in annotation; gene symbols are in 'Gene Symbol'
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 3) Apply mapping to aggregate probe-level data to gene-level
probe_data = gene_data  # from previous step
gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize the obtained gene data and save
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)

# 3. Handle missing values in the linked data
linked_data_processed = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
is_trait_biased_raw, unbiased_linked_data = judge_and_remove_biased_features(linked_data_processed, trait)

# 5. Conduct quality check and save the cohort information.
# Ensure native Python bools are passed into the validator
is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
is_trait_available = bool((trait in selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))
is_trait_biased = bool(is_trait_biased_raw)

# Ensure df has plain Python list for columns (extra safety for JSON metadata creation)
df_for_validation = unbiased_linked_data.copy()
df_for_validation.columns = list(df_for_validation.columns)

note = ("INFO: Affymetrix array data mapped from probes to symbols; trait derived from 'disease state' "
        "in sample characteristics; age/gender not available in this series; left/right atrial tissues mixed.")

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    is_biased=is_trait_biased,
    df=df_for_validation,
    note=note
)

# 6. If the linked data is usable, save it
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    df_for_validation.to_csv(out_data_file)