p1/preprocess/Crohns_Disease/code/GSE83448.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Crohns_Disease"
cohort = "GSE83448"

# Input paths
in_trait_dir = "../DATA/GEO/Crohns_Disease"
in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE83448"

# Output paths
out_data_file = "./output/preprocess/1/Crohns_Disease/GSE83448.csv"
out_gene_data_file = "./output/preprocess/1/Crohns_Disease/gene_data/GSE83448.csv"
out_clinical_data_file = "./output/preprocess/1/Crohns_Disease/clinical_data/GSE83448.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 data availability
is_gene_available = True  # This dataset likely measures gene expression on a human whole genome array.

# 2. Variable Availability and Data Type Conversion
# Based on the sample characteristics dictionary, we see that:
#   - trait_row corresponds to index=1 (inflammation: Control, Inflamed margin, Non-inflamed margin)
#   - age_row is not available
#   - gender_row is not available

trait_row = 1
age_row = None
gender_row = None

# We convert "Control" -> 0, "Inflamed margin"/"Non-inflamed margin" -> 1, others -> None
def convert_trait(value: str) -> Optional[int]:
    parts = value.split(":")
    val = parts[1].strip().lower() if len(parts) > 1 else value.strip().lower()
    if val == "control":
        return 0
    elif val in ["inflamed margin", "non-inflamed margin"]:
        return 1
    return None

# Not available
convert_age = None
convert_gender = None

# Trait data availability
is_trait_available = (trait_row is not None)

# 3. Initial Filtering and Saving Dataset Info
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 if trait_row is not None)
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=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
    )
    # Preview the extracted clinical features
    preview_result = preview_df(selected_clinical_df, n=5)
    print("Selected clinical data preview:", preview_result)

    # Save the clinical data to CSV
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# 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 listed gene identifiers (e.g., "GE469557", "GE469567", etc.),
# they do not appear to be standard human gene symbols.
# Therefore, we conclude that gene mapping is required.

print("\nrequires_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. Decide which columns match the gene expression data and which columns store gene symbols.
#    Observing the preview, the 'ID' column matches the "GExxxxxx" identifiers in the gene expression dataset,
#    while the 'DESCRIPTION' column contains text where actual gene symbols or references can be extracted.

prob_col = "ID"
gene_col = "DESCRIPTION"

# 2. Get a gene mapping dataframe
gene_mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Apply the gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, gene_mapping_df)

print("Gene data shape after mapping:", gene_data.shape)
print("First few mapped gene identifiers:", gene_data.index[:10])
# STEP7

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)

# 2. Link the clinical and genetic data on sample IDs
#    We already know from previous steps that trait_row=1, meaning trait data is available.
#    Load the clinical data we previously saved.
clinical_temp = pd.read_csv(out_clinical_data_file)

# The first column in that CSV is "Crohns_Disease", and the remaining columns are sample IDs.
# We want the columns to be the sample IDs, and the row index to be the trait name.
if "Crohns_Disease" in clinical_temp.columns:
    clinical_temp = clinical_temp.drop(columns=["Crohns_Disease"])
clinical_temp.index = [trait]

linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)

# 3. Handle missing values (drop missing trait rows, remove genes with >20% missing, etc.)
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait or demographic features are severely biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final quality validation and save info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Successfully processed and trait data is available."
)

# 6. If usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file)