p1/preprocess/Adrenocortical_Cancer/code/GSE68606.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE68606"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68606"

# Output paths
out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE68606.csv"
out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE68606.csv"
out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE68606.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)
# 1) Gene Expression Data Availability
# Based on the "Assay Type: Gene Expression" and "Affymetrix Human Genome U133A arrays" in the metadata,
# we conclude that this dataset likely contains gene expression data.
is_gene_available = True

# 2) Variable Availability and Data Type Conversion

# 2.1 Identify availability of 'trait', 'age', and 'gender' by looking at the Sample Characteristics Dictionary
# We did not find "Adrenocortical_Cancer" or an equivalent entry in any row,
# so trait data is considered not available.
trait_row = None

# Age data is present in row 6 with multiple unique numeric values.
age_row = 6

# Gender data is present in row 5 (female/male).
gender_row = 5

# 2.2 Define conversion functions for each variable

def convert_trait(x: str):
    # Trait data is not available in this dataset, return None for all inputs.
    return None

def convert_age(x: str):
    # Extract the substring after the colon and strip whitespace
    val = x.split(":", 1)[-1].strip()
    # Convert to integer if possible, otherwise None
    return int(val) if val.isdigit() else None

def convert_gender(x: str):
    # Extract the substring after the colon and strip whitespace
    val = x.split(":", 1)[-1].strip().lower()
    if val == "female":
        return 0
    elif val == "male":
        return 1
    else:
        return None

# 3) Save Metadata (Initial Filtering)

is_trait_available = (trait_row is not None)  # False in this case
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
# Skip this step because trait_row is None (no trait data available).
# 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])
# These identifiers (e.g., '1007_s_at', '1053_at') are Affymetrix probe set IDs, not human gene symbols.
# Therefore, they require mapping to 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) The key for the probe identifiers in the gene annotation is "ID",
#    and the key for the gene symbols is "Gene Symbol".

# 2) Build a gene mapping dataframe using those two columns.
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 3) Apply the mapping to convert probe-level measurements to gene expression data.
gene_data = apply_gene_mapping(gene_data, gene_mapping)
# STEP 7: Data Normalization and Linking

# Even though we lack trait data, it's still valuable to finalize gene-level data.
# 1. Normalize gene symbols and save the normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file, index=True)

# Since trait_row = None, there's no trait data to link or analyze.
# We cannot produce a linked dataset or evaluate trait bias in a meaningful way.
# However, the task instructions request a "final" validation.

import pandas as pd

# Provide a dummy DataFrame and set is_biased to False
# so that validate_and_save_cohort_info can finalize and mark this dataset as unusable for trait analysis.
empty_df = pd.DataFrame()
is_biased = False

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,      # We do have gene data
    is_trait_available=False,    # But no trait data
    is_biased=is_biased,         # Arbitrarily set to False since no trait is present
    df=empty_df,                 # An empty DataFrame to satisfy the function's requirements
    note="No trait data available, so no final linked dataset can be produced."
)

# 6. Because the dataset is not usable for trait-based analysis, we do not save a final linked dataset.