p1/preprocess/Adrenocortical_Cancer/code/GSE108088.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE108088"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE108088"

# Output paths
out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE108088.csv"
out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE108088.csv"
out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE108088.csv"
json_path = "./output/preprocess/1/Adrenocortical_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)
import pandas as pd
import numpy as np

# 1. Determine gene expression availability
# Based on the background info "comprehensive molecular profiling," we assume it includes gene expression data.
is_gene_available = True

# 2. Identify the keys for trait, age, and gender
# After examining the sample characteristics dictionary, there's no direct or inferred "Adrenocortical_Cancer," 
# no age info, and no gender info. Hence, we set them all to None.
trait_row = None
age_row = None
gender_row = None

# 2.1 and 2.2: Data type conversion functions
def convert_trait(raw_value: str):
    # This function would parse the raw_value and return 0 or 1 if the trait is binary,
    # or a float if continuous. Here, we have no trait data, so it's a placeholder.
    # If used, ensure to handle unknown or malformed entries by returning None.
    # We split by 'colon' if needed, but since trait_row is None, we won't use it.
    return None

def convert_age(raw_value: str):
    # Sample placeholder function. No age data is found, so it returns None.
    return None

def convert_gender(raw_value: str):
    # Sample placeholder function. No gender data is found, so it returns None.
    return None

# 3. Conduct initial filtering on dataset usability, saving relevant metadata
# Trait data availability is determined by whether trait_row is None.
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
# We only proceed if trait_row is not None.
# Since trait_row is None, we skip this substep.
# 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])
# Based on the given identifiers (e.g., "1007_s_at", "1053_at", etc.), these are Affymetrix probe IDs,
# not human gene symbols. Therefore, further mapping is required to convert them into 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))
# STEP6: Gene Identifier Mapping

# 1. Identify which columns in the annotation dataframe correspond to the probe IDs and gene symbols.
prob_col = "ID"
gene_col = "Gene Symbol"

# 2. Obtain the gene mapping dataframe.
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Convert probe-level expression data to gene-level expression data by applying the mapping.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Let's inspect the shape and a quick preview of the mapped gene expression data.
print("Gene data shape after mapping:", gene_data.shape)
print("Gene data preview:")
print(preview_df(gene_data, n=5))
# STEP 7: Data Normalization and Linking

# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file, index=True)

# 2. Since trait data is missing, skip linking clinical and genetic data, 
#    skip missing-value handling and bias detection for the trait.

# 3. Conduct final validation and record info.
#    Since trait data is unavailable, set is_trait_available=False, 
#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
dummy_df = pd.DataFrame()
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=False,
    df=dummy_df,
    note="No trait data found; skipped clinical-linking steps."
)

# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
if is_usable:
    dummy_df.to_csv(out_data_file, index=True)