p1/preprocess/Cystic_Fibrosis/code/GSE67698.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE67698"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE67698"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE67698.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE67698.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE67698.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. Gene Expression Data Availability
is_gene_available = True  # "Transcriptional profiling" implies likely RNA gene expression

# 2. Variable Availability and Data Type Conversion
#   Based on the sample characteristics dictionary, row=1 has two unique values indicating
#   deltaF508 CFTR or wildtype CFTR, which can be mapped to the trait (Cystic_Fibrosis vs. not).
#   Age and gender do not appear to be present.

trait_row = 1
age_row = None
gender_row = None

def convert_trait(value: str) -> Optional[int]:
    """
    Convert the trait (CF vs. non-CF) to a binary integer.
    Values containing 'deltaF508' -> 1 (CF)
    Values containing 'wildtype'  -> 0 (non-CF)
    Otherwise -> None
    """
    # Attempt to split by colon, keep the part after it
    parts = value.split(':', 1)
    if len(parts) == 2:
        val = parts[1].strip().lower()
    else:
        val = value.strip().lower()

    if 'deltaf508' in val:
        return 1
    elif 'wildtype' in val:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    # Age data not available, return None
    return None

def convert_gender(value: str) -> Optional[int]:
    # Gender data not available, return 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
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 = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview)

    # Save the clinical dataframe to CSV
    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])
# Based on observation, the identifiers (e.g., "A_23_P100001") are not standard human gene symbols.
# They appear to be array probe IDs that 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))
# STEP6: Gene Identifier Mapping

# 1. Identify the columns in the annotation that match the expression data's probe IDs and human gene symbols.
#    From inspection, 'ID' matches the "A_23_P..." probe IDs, and 'GENE_SYMBOL' holds the human gene symbols.

# 2. Build a gene mapping dataframe using these columns.
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col="ID",
    gene_col="GENE_SYMBOL"
)

# 3. Convert probe-level measurements to gene-level expression data by applying the gene mapping.
gene_data = apply_gene_mapping(
    expression_df=gene_data,
    mapping_df=mapping_df
)

# (Optional) Display some basic information about the newly mapped gene_data.
print("Mapped gene_data shape:", gene_data.shape)
print("First 5 rows of mapped gene_data:")
print(gene_data.head())
import os
import pandas as pd

# STEP 7 (Corrected)

# 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) Instead of reloading the clinical data from CSV (which was saved without index),
#    we directly use the in-memory DataFrame "selected_clinical_df" from earlier steps.
#    That DataFrame already has the trait as a row label, which is required downstream.

# 3) Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 4) Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait)

# 5) Check for biased features (including the trait)
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6) Final validation and saving metadata
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="Data from GSE123086, trait is Crohn's disease."
)

# 7) If the dataset is usable, save the linked data
if is_usable:
    linked_data.to_csv(out_data_file)