output/preprocess/Canavan_Disease/code/GSE41445.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Canavan_Disease"
cohort = "GSE41445"

# Input paths
in_trait_dir = "../DATA/GEO/Canavan_Disease"
in_cohort_dir = "../DATA/GEO/Canavan_Disease/GSE41445"

# Output paths
out_data_file = "./output/z2/preprocess/Canavan_Disease/GSE41445.csv"
out_gene_data_file = "./output/z2/preprocess/Canavan_Disease/gene_data/GSE41445.csv"
out_clinical_data_file = "./output/z2/preprocess/Canavan_Disease/clinical_data/GSE41445.csv"
json_path = "./output/z2/preprocess/Canavan_Disease/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 math
import pandas as pd

# 1) Gene expression data availability (Affymetrix HG-U133 Plus 2 expression arrays)
is_gene_available = True

# 2) Variable availability
trait_row = 2       # 'disease' field includes "aspartoacylase deficiency; possible Canavan disease"
age_row = None      # No age information provided for cell lines
gender_row = 0      # 'gender' field

# 2.2) Converters
def _after_colon(x):
    if x is None or (isinstance(x, float) and math.isnan(x)):
        return None
    s = str(x)
    parts = s.split(":", 1)
    val = parts[1] if len(parts) > 1 else parts[0]
    return val.strip()

def convert_trait(x):
    val = _after_colon(x)
    if val is None or val == "" or val.lower() in {"na", "n/a"}:
        return None
    v = val.lower()
    # Map Canavan disease related annotations to 1, everything else to 0
    if ("canavan" in v) or ("aspartoacylase" in v):
        return 1
    return 0

def convert_gender(x):
    val = _after_colon(x)
    if val is None:
        return None
    v = val.strip().lower()
    if v == "female":
        return 0
    if v == "male":
        return 1
    return None

# Age not available in this cohort
convert_age = 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
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 and save
    preview = preview_df(selected_clinical_df)
    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
# 1-2. Identify the appropriate columns for mapping and create mapping dataframe
# Probe identifiers: 'ID'; Gene symbols: 'Gene Symbol'
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
import pandas as pd

# 1) Normalize gene symbols 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 clinical and genetic data
# Be robust to variable name mismatch across steps
try:
    clinical_df = selected_clinical_df
except NameError:
    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

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

# 5) Final quality validation and save cohort info
note = ("INFO: Cell line dataset; trait inferred from 'disease' field. "
        "Highly imbalanced with very few Canavan-like cases.")
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=note
)

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