output/preprocess/Cervical_Cancer/code/GSE75132.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cervical_Cancer"
cohort = "GSE75132"

# Input paths
in_trait_dir = "../DATA/GEO/Cervical_Cancer"
in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE75132"

# Output paths
out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE75132.csv"
out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE75132.csv"
out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE75132.csv"
json_path = "./output/z2/preprocess/Cervical_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 re
import pandas as pd

# 1) Gene expression data availability
# Based on the series summary, this is an mRNA microarray dataset (gene expression).
is_gene_available = True

# 2) Variable availability and converters

# From Sample Characteristics:
# 0: 'tissue: cervix' -> constant, not useful.
# 1: 'category (0 = normal, 1 = hpv without progression, 2 = hpv with progression)' -> relates to HPV progression, not the Cervical_Cancer trait.
# 2: 'hpv status: ...' -> HPV status, not the Cervical_Cancer trait.
# 3: 'disease state: none/severe dysplasia/CIS/moderate dysplasia/cancer' -> usable to derive Cervical_Cancer.
trait_row = 3
age_row = None
gender_row = None

# Converters
def _after_colon(x: str) -> str:
    if x is None:
        return ''
    s = str(x)
    if ':' in s:
        s = s.split(':', 1)[1]
    return s.strip().strip('"').strip()

def convert_trait(x):
    v = _after_colon(x).lower()
    if v in ['', 'na', 'n/a', 'nan', 'unknown']:
        return None
    # Binary trait: Cervical cancer present (1) vs not (0)
    # Map explicit cancer to 1; dysplasia, CIS, none to 0
    if v in ['cancer', 'cervical cancer']:
        return 1
    if v in ['none', 'normal', 'cis', 'carcinoma in situ', 'moderate dysplasia', 'severe dysplasia']:
        return 0
    if ('dysplasia' in v) or ('cin' in v):
        return 0
    return None

def convert_age(x):
    v = _after_colon(x).lower()
    if v in ['', 'na', 'n/a', 'nan', 'unknown']:
        return None
    m = re.search(r'[-+]?\d*\.?\d+', v)
    return float(m.group()) if m else None

def convert_gender(x):
    v = _after_colon(x).lower()
    if v in ['', 'na', 'n/a', 'nan', 'unknown']:
        return None
    if v in ['f', 'female', 'woman', 'women']:
        return 0
    if v in ['m', 'male', 'man', 'men']:
        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 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=convert_age if age_row is not None else None,
        gender_row=gender_row,
        convert_gender=convert_gender if gender_row is not None else None
    )
    clinical_preview = preview_df(selected_clinical_df)
    print("Clinical feature preview:", clinical_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. Identify the columns for probe IDs and gene symbols in the annotation dataframe
probe_col = 'ID'
gene_symbol_col = 'Gene Symbol'

# 2. Create the mapping dataframe from annotation
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# 3. Apply the 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 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 = 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, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct quality check and save the cohort information.
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
)

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