p1/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/preprocess/1/Cervical_Cancer/GSE75132.csv"
out_gene_data_file = "./output/preprocess/1/Cervical_Cancer/gene_data/GSE75132.csv"
out_clinical_data_file = "./output/preprocess/1/Cervical_Cancer/clinical_data/GSE75132.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 1: Determine if gene expression data is available
# Based on the background info (microarray analysis on RNA), we consider this dataset to contain gene expression data.
is_gene_available = True

# Step 2: Assign row keys and define conversion functions for trait, age, and gender

# Observing the sample characteristics dictionary:
# 3: ['disease state: none', 'disease state: moderate dysplasia', 'disease state: severe dysplasia',
#     'disease state: CIS', 'disease state: cancer']
# We map "none" -> 0 and everything else -> 1 for a binary trait of Cervical_Cancer.

trait_row = 3
def convert_trait(x: str):
    # Extract the value after the colon
    parts = x.split(':', 1)
    val = parts[1].strip() if len(parts) > 1 else None
    if val is None:
        return None
    val_lower = val.lower()
    if val_lower == 'none':
        return 0
    else:
        return 1

# No age, no gender data found
age_row = None
convert_age = None

gender_row = None
convert_gender = None

# Step 2.1: Data availability
is_trait_available = (trait_row is not None)

# Step 3: Initial filtering and metadata saving
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
)

# Step 4: If trait data is available, extract clinical features
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 the extracted clinical data
    clinical_preview = preview_df(selected_clinical_df)
    print("Clinical Data Preview:", clinical_preview)

    # Save the clinical data to CSV
    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("They appear to be Affymetrix probe set IDs. Hence they are not standard human gene symbols.")
print("requires_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 & 2. Decide which columns in the annotation data correspond to probe IDs and gene symbols.
# In this case, the 'ID' column matches our probe identifiers, and the 'Gene Symbol' column are the gene symbols.

mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col="ID",
    gene_col="Gene Symbol"
)

# 3. Convert probe-level measurements to gene expression data using the mapping from above.
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
# 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)