p1/preprocess/Cystic_Fibrosis/code/GSE76347.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE76347"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE76347"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE76347.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE76347.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE76347.csv"
json_path = "./output/preprocess/1/Cystic_Fibrosis/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. Determine if gene expression data is available
is_gene_available = True  # Microarray data is mentioned in the background

# 2. Determine availability of trait, age, and gender
#    and define data conversion functions

# From the sample characteristics dictionary, there is only one unique trait value ("CF"),
# so it is effectively constant (not useful for association), thus not available.
trait_row = None  

# No information about age or gender in the dictionary, so set them to None
age_row = None
gender_row = None

# Define the data conversion functions
def convert_trait(x: str):
    # Since trait_row is None, we won't actually use this, but defining for completeness
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    # If trait were variable, we'd map values accordingly, but it's constant in this dataset
    return None

def convert_age(x: str):
    # Since age_row is None, we won't actually use this, but defining for completeness
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    # Normally, parse to a float/int if valid; otherwise None
    return None

def convert_gender(x: str):
    # Since gender_row is None, we won't actually use this, but defining for completeness
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    # Typically, 'female' -> 0, 'male' -> 1; else None
    return None

# 3. Save Metadata (initial filtering)
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
#    Only proceed if trait_row is available (not None), otherwise skip
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        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
    )
    print("Preview of extracted clinical features:")
    print(preview_df(selected_clinical_df))
    selected_clinical_df.to_csv(out_clinical_data_file)
# 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])
# Observing the IDs: they appear to be numeric probe identifiers (e.g., from an array platform).
# These are not standard human gene symbols and likely need to be mapped 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. Identify which columns correspond to the probe IDs and gene symbols in the annotation
#    - The "ID" column in gene_annotation matches the probe IDs in gene_data
#    - The "gene_assignment" column contains gene symbol information

# 2. Get a gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# 3. Convert probe-level measurements into gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)
import os
import pandas as pd

# STEP 7

# 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 we have no trait data (trait_row was None), we skip linking clinical data,
# missing value handling, bias checks, and final validation for this dataset.
# The partial validation has already been done previously (is_final=False), indicating
# that trait data is missing and thus the dataset is not usable for associative studies.