p1/preprocess/Cystic_Fibrosis/code/GSE129168.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE129168"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE129168"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE129168.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE129168.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE129168.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  # This dataset provides transcriptome data for CF iPSCs, so we consider it as gene expression data.

# 2) Variable Availability
# Observing the sample characteristics dictionary, row=2 contains genotype info
# indicating CF vs non-CF lines (p.Phe508del vs gene-corrected/WT).
# No suitable age or gender info is present.
trait_row = 2
age_row = None
gender_row = None

# 2) Data Type Conversion
def convert_trait(value):
    if not value or pd.isnull(value):
        return None
    val = value.split(':')[-1].strip().lower()
    # Mark p.Phe508del (but not gene-corrected) as CF
    if 'p.phe508del' in val and 'gene corrected' not in val:
        return 1
    return 0

def convert_age(value):
    return None  # Not available

def convert_gender(value):
    return None  # Not available

# 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
# Proceed only if the trait data is available
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
    preview_result = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_result)
    # Save 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 index names like "A_23_P100001", these are array probe IDs rather than standard human gene symbols.
# Therefore, they need to be mapped to gene symbols.
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 the columns in gene_annotation that correspond to the probe ID and gene symbol
probe_col = "ID"
symbol_col = "GENE_SYMBOL"

# 2) Obtain the mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)

# 3) Convert probe-level measurements to gene expression data by applying the mapping
gene_data = apply_gene_mapping(gene_data, mapping_df)
import pandas as pd

# STEP7

# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Based on Step 2, we concluded trait_row = 2 (thus trait data is available).
is_trait_available = True

if not is_trait_available:
    # 2-4: Skip linking, missing value handling, and bias checks because trait is unavailable.
    empty_df = pd.DataFrame()
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=True,
        df=empty_df,
        note="Trait data not available; skipping further steps."
    )
else:
    # 2. Load the clinical data from the previous step and set its index to the trait name
    selected_clinical_data = pd.read_csv(out_clinical_data_file)
    selected_clinical_data.index = [trait]

    # Link the clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

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

    # 4. Determine whether the trait and demographic features are severely biased
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Conduct final quality validation and save the cohort information
    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=is_trait_biased,
        df=unbiased_linked_data,
        note="Final check after linking and missing-value handling."
    )

    # 6. If the dataset is usable, save it as CSV
    if is_usable:
        unbiased_linked_data.to_csv(out_data_file)