output/preprocess/Adrenocortical_Cancer/code/GSE75415.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE75415"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE75415"

# Output paths
out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE75415.csv"
out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE75415.csv"
out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE75415.csv"
json_path = "./output/z1/preprocess/Adrenocortical_Cancer/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

# 1) Gene expression data availability
is_gene_available = True  # Microarray-based gene expression profiling per background

# 2) Variable availability
trait_row = 1  # 'histologic type' with carcinoma/adenoma/normal/unknown
age_row = None  # Age not present in the characteristics dictionary
gender_row = 0  # 'gender' with female/male/unknown

# 2.2 Conversion functions
def _extract_value(x):
    if x is None or (isinstance(x, float) and math.isnan(x)):
        return None
    try:
        s = str(x)
    except Exception:
        return None
    if ':' in s:
        s = s.split(':', 1)[1]
    return s.strip().lower()

# Trait (binary): adrenocortical carcinoma=1; normal/adenoma=0; unknown=None
def convert_trait(x):
    v = _extract_value(x)
    if v is None or v == '' or v == 'unknown':
        return None
    if 'carcinoma' in v:
        return 1
    if 'normal' in v or 'adenoma' in v:
        return 0
    return None

# Age (continuous): not available here, but provide a robust parser
def convert_age(x):
    v = _extract_value(x)
    if v is None or v == '' or v == 'unknown' or v == 'not applicable' or v == 'not available':
        return None
    # Try to extract a number (years). Handles formats like "5", "5 yrs", "5 years", "60 months"
    import re
    m = re.search(r'(\d+(\.\d+)?)', v)
    if not m:
        return None
    num = float(m.group(1))
    # Convert months to years if clearly indicated
    if 'month' in v:
        return num / 12.0
    return num

# Gender (binary): female=0; male=1; unknown=None
def convert_gender(x):
    v = _extract_value(x)
    if v is None or v == '' or v == 'unknown':
        return None
    if v.startswith('fem'):
        return 0
    if v.startswith('mal'):
        return 1
    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 data is available)
if is_trait_available:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        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)

# 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
requires_gene_mapping = True
print(f"requires_gene_mapping = {requires_gene_mapping}")

# 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
# Determine mapping columns based on previous previews:
# Probe IDs: 'ID'; Gene symbols: 'Gene Symbol'
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Step 7: Data Normalization and Linking
import os
import pandas as pd

# Ensure required input paths are available
if 'soft_file' not in locals() or 'matrix_file' not in locals():
    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Ensure clinical data is available (reload if not in scope)
if 'selected_clinical_df' not in locals():
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Reconstruct gene-level expression data deterministically (probe -> gene mapping)
raw_probe_df = get_genetic_data(matrix_file)
gene_annotation = get_gene_annotation(soft_file)
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
gene_data = apply_gene_mapping(raw_probe_df, mapping_df)

# 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
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Bias checks (remove biased demographic features if needed)
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
note = "INFO: Trait derived from histologic type; Gender available; Age not provided in series characteristics."
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. Save linked data only if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)