output/preprocess/Cervical_Cancer/code/GSE107754.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cervical_Cancer"
cohort = "GSE107754"

# Input paths
in_trait_dir = "../DATA/GEO/Cervical_Cancer"
in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE107754"

# Output paths
out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE107754.csv"
out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE107754.csv"
out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE107754.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

# 1) Gene expression availability
is_gene_available = True  # Whole human genome gene expression microarrays per background

# 2) Determine availability rows based on Sample Characteristics Dictionary
trait_row = 2    # tissue info including 'tissue: Cervix/Cervical cancer'
age_row = None   # No age field observed
gender_row = 0   # 'gender: Female'/'gender: Male'

# 2.2) Conversion functions
def _after_colon(x):
    try:
        parts = str(x).split(":", 1)
        return parts[1].strip() if len(parts) > 1 else str(x).strip()
    except Exception:
        return None

def convert_trait(x):
    # Binary: 1 = Cervical/Cervix cancer, 0 = other tissues; unknown if not a tissue field
    try:
        s = str(x).strip()
        # If header not present, try best-effort on the whole string
        header = s.split(":", 1)[0].strip().lower() if ":" in s else ""
        value = _after_colon(s)
        if value is None:
            return None
        v = value.lower()
        if "tissue" in header:
            if ("cervix" in v) or ("cervical" in v):
                return 1
            else:
                return 0
        else:
            # e.g., "biopsy location" or other non-tissue annotations in this row -> unknown
            return None
    except Exception:
        return None

def convert_age(x):
    # Not available in this dataset
    return None

def convert_gender(x):
    try:
        v = _after_colon(x)
        if v is None:
            return None
        vl = v.strip().lower()
        if vl in {"female", "f"}:
            return 0
        if vl in {"male", "m"}:
            return 1
        return None
    except Exception:
        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 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
    )
    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
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
# Identify the appropriate columns in the annotation: 'ID' (probe IDs) and 'GENE_SYMBOL' (gene symbols)
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

# 1) Normalize 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)

# Prepare clinical dataframe from memory if available, else load from saved CSV
try:
    clinical_df = selected_clinical_df
except NameError:
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 2) Link clinical and genetic data
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 check and feature cleanup
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# Debug: show resulting shape to aid troubleshooting
print(f"Unbiased linked data shape: {unbiased_linked_data.shape}")

# Availability flags as native Python bools
is_gene_available_final = bool(normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
is_trait_available_final = bool((trait in clinical_df.index) and clinical_df.loc[trait].notna().any())

note = "INFO: Trait derived from tissue field; Age unavailable; Gender available."

# 5) Final quality validation and cohort info saving with robustness to legacy JSON issues
def _finalize_and_save():
    return validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=is_gene_available_final,
        is_trait_available=is_trait_available_final,
        is_biased=is_trait_biased,
        df=unbiased_linked_data,
        note=note
    )

try:
    is_usable = _finalize_and_save()
except Exception as e:
    # If JSON serialization or legacy content issue occurs, reset the JSON file and retry once
    if os.path.exists(json_path):
        try:
            os.remove(json_path)
        except Exception:
            pass
    is_usable = _finalize_and_save()

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