p1/preprocess/Chronic_kidney_disease/code/GSE104948.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_kidney_disease"
cohort = "GSE104948"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE104948"

# Output paths
out_data_file = "./output/preprocess/1/Chronic_kidney_disease/GSE104948.csv"
out_gene_data_file = "./output/preprocess/1/Chronic_kidney_disease/gene_data/GSE104948.csv"
out_clinical_data_file = "./output/preprocess/1/Chronic_kidney_disease/clinical_data/GSE104948.csv"
json_path = "./output/preprocess/1/Chronic_kidney_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 if gene expression data is available
is_gene_available = True  # Based on the metadata (Affymetrix microarrays for gene expression)
# 2) Identify variable availability
trait_row = 1   # diagnosis field with multiple diagnoses
age_row = None  # no age data detected
gender_row = None  # no gender data detected

# 2) Define data type conversions
def convert_trait(value: Any) -> Optional[int]:
    """
    Convert the diagnosis field to a binary indicator for Chronic Kidney Disease:
      - If 'Tumor Nephrectomy' or unknown, map to 0/None
      - Otherwise, map to 1
    """
    if pd.isna(value):
        return None
    # Extract the part after the colon if present
    parts = str(value).split(':', 1)
    if len(parts) == 2:
        val_str = parts[1].strip()
    else:
        val_str = parts[0].strip()

    if val_str.lower() in ['tumor nephrectomy', '']:
        return 0
    if val_str.lower() == 'nan':
        return None
    # Everything else is considered CKD = 1
    return 1

def convert_age(value: Any) -> Optional[float]:
    # This dataset has no age data; return None
    return None

def convert_gender(value: Any) -> Optional[int]:
    # This dataset has no gender data; return None
    return None

# 3) Conduct initial filtering and save metadata
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) If trait data is available, extract and preview the clinical features
if trait_row is not None:
    selected_clinical_data = 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
    )
    preview_result = preview_df(selected_clinical_data)
    print("Preview of selected clinical features:", preview_result)
    
    # Save the clinical data
    selected_clinical_data.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 ID patterns (e.g., "10000_at", "10001_at"), these look like probe set IDs 
# from a microarray platform rather than human gene symbols. Hence, they require mapping.

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 the columns in the gene annotation dataframe that match the 
#    probe identifiers and the columns that provide the gene symbols.
prob_col = "ID"
gene_col = "Symbol"

# 2) Get the gene mapping dataframe by extracting these two columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3) Convert probe-level measurements to gene-level expression data.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Print out a brief check of the mapped gene data
print("Mapped gene_data shape:", gene_data.shape)
print("First few gene symbols:", gene_data.index[:10])
# 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)

# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
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 some demographic features are severely biased, and remove biased features.
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct quality check 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=linked_data
)

# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
if is_usable:
    unbiased_linked_data.to_csv(out_data_file)