p1/preprocess/Autoinflammatory_Disorders/code/GSE80060.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autoinflammatory_Disorders"
cohort = "GSE80060"

# Input paths
in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders"
in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE80060"

# Output paths
out_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/GSE80060.csv"
out_gene_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/gene_data/GSE80060.csv"
out_clinical_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/clinical_data/GSE80060.csv"
json_path = "./output/preprocess/1/Autoinflammatory_Disorders/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)
# Step 1: Determine whether this dataset likely contains gene expression data
is_gene_available = True  # Based on the title "Gene expression data of whole blood..."

# Step 2.1: Identify data availability for trait, age, and gender
trait_row = 1   # "disease status: SJIA" vs "disease status: Healthy"
age_row = None  # No age info found
gender_row = None  # No gender info found

# Step 2.2: Define data type conversions
def convert_trait(value: str):
    # Extract the substring after the colon
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    # Map SJIA -> 1, Healthy -> 0
    if val == 'sjia':
        return 1
    elif val == 'healthy':
        return 0
    else:
        return None

def convert_age(value: str):
    # No age data; return None
    return None

def convert_gender(value: str):
    # No gender data; return None
    return None

# Step 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
)

# Step 4: Clinical feature extraction if trait_row is not None
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,
        trait='Disease Status',
        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 selected clinical data
    preview = preview_df(selected_clinical_df)
    print("Selected Clinical Data Preview:", preview)
    
    # Save extracted clinical features
    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])
# These identifiers appear to be Affymetrix probe IDs rather than standard human gene symbols.
# Therefore, they require mapping 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))
# STEP: Gene Identifier Mapping

# 1. Identify the columns in gene_annotation that correspond to the probe IDs and the gene symbols.
#    From the preview, the "ID" column matches the probe identifiers in the gene_data index,
#    and the "Gene Symbol" column contains the gene symbols.
probe_col = "ID"
gene_symbol_col = "Gene Symbol"

# 2. Get a gene mapping dataframe from the annotation.
mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)

# 3. Convert probe-level data to gene-level data using the mapping, dividing probe expression among multiple genes.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# For verification, print a small preview of the resulting gene expression dataframe.
print("Preview of Gene Expression Data (first few genes):")
print(preview_df(gene_data, n=5))
# STEP7

# 1. Normalize the obtained gene data using the NCBI Gene synonym database
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
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values systematically (note the trait column name matches the clinical data's "Disease Status")
linked_data_processed = handle_missing_values(linked_data, trait_col="Disease Status")

# 4. Check for biased trait and remove any biased demographic features
trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, "Disease Status")

# 5. Final quality validation and metadata saving
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_final,
    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
)

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