p1/preprocess/Crohns_Disease/code/GSE259353.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Crohns_Disease"
cohort = "GSE259353"

# Input paths
in_trait_dir = "../DATA/GEO/Crohns_Disease"
in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE259353"

# Output paths
out_data_file = "./output/preprocess/1/Crohns_Disease/GSE259353.csv"
out_gene_data_file = "./output/preprocess/1/Crohns_Disease/gene_data/GSE259353.csv"
out_clinical_data_file = "./output/preprocess/1/Crohns_Disease/clinical_data/GSE259353.csv"
json_path = "./output/preprocess/1/Crohns_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)
# 1. Determine gene expression availability
is_gene_available = True  # The dataset uses a Nanostring panel to measure expression of 760 genes.

# 2.1 Data Availability
#    Everyone in this dataset appears to have Crohn's disease (no healthy controls), hence trait is constant.
trait_row = None  # Not available as a variable distinguishing samples
age_row = 2       # Row 2 has age data
gender_row = 1    # Row 1 has gender data

# 2.2 Data Type Conversions
def convert_trait(x: str):
    # Trait not available => always None
    return None

def convert_age(x: str):
    # Extract integer age from a string like "age: 27"
    try:
        value = x.split(":", 1)[1].strip()
        return int(value)
    except:
        return None

def convert_gender(x: str):
    # Extract gender from a string like "gender: Female" -> female => 0, male => 1
    try:
        value = x.split(":", 1)[1].strip().lower()
        if value == "female":
            return 0
        elif value == "male":
            return 1
        else:
            return None
    except:
        return None

# 3. Save Metadata with initial filtering
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. If trait data is not available, we skip clinical feature extraction.
#    Hence, no further steps here if trait_row is None.
# 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., ABCA1, ABCB11, ACAA2, etc.) appear to be standard human gene symbols, 
# so no further mapping is required.
print("requires_gene_mapping = False")
import os
import pandas as pd

# 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 in earlier steps, we have no trait data or clinical file.
# We'll therefore skip linking and missing-value handling of clinical data.

# 2. Perform final validation. Because there's no trait data, set is_trait_available=False
#    and pass an empty DataFrame plus a biased flag to mark the dataset unusable for trait analysis.
df_dummy = pd.DataFrame()
is_biased_dummy = True  # Marks the dataset as not usable

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=is_biased_dummy,
    df=df_dummy,
    note="No trait data available. Skipped linking."
)

# 3. If the dataset were considered usable, we would save the linked data here.
#    Since trait is unavailable, no final data file is produced.