p1/preprocess/Creutzfeldt-Jakob_Disease/code/GSE62699.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Creutzfeldt-Jakob_Disease"
cohort = "GSE62699"

# Input paths
in_trait_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease"
in_cohort_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease/GSE62699"

# Output paths
out_data_file = "./output/preprocess/1/Creutzfeldt-Jakob_Disease/GSE62699.csv"
out_gene_data_file = "./output/preprocess/1/Creutzfeldt-Jakob_Disease/gene_data/GSE62699.csv"
out_clinical_data_file = "./output/preprocess/1/Creutzfeldt-Jakob_Disease/clinical_data/GSE62699.csv"
json_path = "./output/preprocess/1/Creutzfeldt-Jakob_Disease/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 os
import json
from typing import Optional, Callable

# 1. Gene Expression Data Availability
# Based on the background info, mRNA data (Affymetrix GeneChip HG-U133A 2.0) is indeed present.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# The sample characteristics dictionary shows:
#  {0: ['diagnosis: alcohol dependence (AD)', 'diagnosis: Control'],
#   1: ['tissue type: post mortem brain']}
# There's no mention of Creutzfeldt-Jakob_Disease, age, or gender. So all are set to None.
trait_row = None
age_row = None
gender_row = None

# 2.2 Data Type Conversion
# Functions to convert potential values (though not used due to unavailability).
def convert_trait(x: str) -> Optional[float]:
    # Returns 1 or 0 for presence/absence of CJD, None if unknown; not used here
    if not x or ":" not in x:
        return None
    # Extract the portion after ':'
    val = x.split(":", 1)[1].strip().lower()
    if "creutzfeldt-jakob" in val:
        return 1.0
    elif "control" in val or "no" in val:
        return 0.0
    return None

def convert_age(x: str) -> Optional[float]:
    # Returns a float if age is found, None if unknown; not used here
    if not x or ":" not in x:
        return None
    val = x.split(":", 1)[1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(x: str) -> Optional[int]:
    # Returns 0 for female, 1 for male, None if unknown; not used here
    if not x or ":" not in x:
        return None
    val = x.split(":", 1)[1].strip().lower()
    if "female" in val:
        return 0
    elif "male" in val:
        return 1
    return None

# 3. Save Metadata (initial filtering)
# Trait data availability depends on whether trait_row is 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
# Because trait_row is None, we skip this step.
# 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("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 & 2. Identify the columns in the annotation that correspond to probe ID and gene symbol
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")

# 3. Apply the mapping to convert probe-level measurements to gene-level expression
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP7

# 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)

# Since trait_row was None, there is no clinical data to link or trait to analyze.
# Thus, we skip steps that depend on trait data or linking clinical and genetic data.

# 5. Final quality validation
# Even though we have no trait, the instructions ask for final validation.
# Because "is_trait_available=False", the dataset will be deemed un-usable for trait-based analysis.
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # No trait data
    is_biased=False,  # Required non-None value, but has no effect since is_trait_available=False
    df=normalized_gene_data,   # Required DataFrame for final validation
    note="No trait data found; only gene data is present."
)

# 6. If the dataset were usable (it won't be, since trait data is unavailable), save the linked data.
if is_usable:
    # No final data to save because trait is missing.
    pass