p1/preprocess/Cervical_Cancer/code/GSE138080.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cervical_Cancer"
cohort = "GSE138080"

# Input paths
in_trait_dir = "../DATA/GEO/Cervical_Cancer"
in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE138080"

# Output paths
out_data_file = "./output/preprocess/1/Cervical_Cancer/GSE138080.csv"
out_gene_data_file = "./output/preprocess/1/Cervical_Cancer/gene_data/GSE138080.csv"
out_clinical_data_file = "./output/preprocess/1/Cervical_Cancer/clinical_data/GSE138080.csv"
json_path = "./output/preprocess/1/Cervical_Cancer/cohort_info.json"

# STEP1
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: Dataset Analysis and Clinical Feature Extraction

# 1. Determine if the dataset likely contains gene expression data
is_gene_available = True  # Based on the "mRNA tissues-Agilent" description

# 2. Determine availability of variables and write conversion functions

# From the sample characteristics:
# {0: ['cell type: normal cervical squamous epithelium',
#      'cell type: cervical intraepithelial neoplasia, grade 2-3',
#      'cell type: cervical squamous cell carcinoma'],
#  1: ['hpv: high-risk HPV-positive',
#      'hpv: HPV-negative']}

# Observing these, row 0 contains different states of cervical tissue,
# which we interpret as relevant to the trait "Cervical_Cancer."
# Hence we set:
trait_row = 0

# There is no row indicating age, so:
age_row = None

# There is no row indicating gender, so:
gender_row = None

# Data Type Conversion Functions
def convert_trait(value: str):
    # Extract the text after the colon if present
    parts = value.split(':', 1)
    val = parts[1].strip().lower() if len(parts) == 2 else value.strip().lower()
    # Convert to binary (0 = normal, 1 = pre-cancer or cancer)
    if "normal" in val:
        return 0
    elif "intraepithelial" in val or "carcinoma" in val:
        return 1
    return None

def convert_age(value: str):
    # Not used since age is unavailable
    return None

def convert_gender(value: str):
    # Not used since gender is unavailable
    return None

# 3. Perform initial filtering and save metadata
# Trait data is available if trait_row is not None
is_trait_available = (trait_row is not None)

is_usable = 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 data is 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,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    # Preview dataframe
    preview = preview_df(selected_clinical_df, n=5, max_items=200)
    print("Preview of selected clinical features:", preview)

    # Save the clinical data
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 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])
print("These numeric entries appear to be probe IDs or some numeric references, not standard human gene symbols.\nrequires_gene_mapping = True")
# STEP5
# 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: Gene Identifier Mapping

# 1. Determine which columns in gene_annotation match the probe IDs in gene_data and which store gene symbols.
#    From the preview, "ID" matches the probe IDs, and "GENE_SYMBOL" corresponds to gene symbols.

# 2. Create a mapping dataframe from the gene_annotation by extracting the probe ID column and gene symbol column.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")

# 3. Convert the probe-level data to gene-level data using the mapping, distributing expression among genes if a probe
#    maps to multiple genes, and summing across probes for the same gene.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (Optional) Print a brief check of the new gene_data
print("Gene data shape after mapping:", gene_data.shape)
print("First 20 genes after mapping:", gene_data.index[:20])
# STEP 7

# 1. Normalize gene symbols in the gene_data, then save to CSV.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data on sample IDs
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 demographic features are biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct final validation and save cohort info
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=trait_biased,
    df=linked_data,
    note="Trait is available. Completed linking and QC steps."
)

# 6. If the dataset is usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file)