p1/preprocess/Allergies/code/GSE270312.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE270312"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE270312"

# Output paths
out_data_file = "./output/preprocess/1/Allergies/GSE270312.csv"
out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE270312.csv"
out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE270312.csv"
json_path = "./output/preprocess/1/Allergies/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  # Based on the transcriptome data (nanostring)

# 2. Variable Availability and Data Type Conversion
# From the sample characteristics dictionary, we see that:
#   - 'allergic rhinitis status: Yes/No' corresponds to allergies, i.e., trait_row = 5
#   - No age information is available => age_row = None
#   - 'gender: Female/Male' => gender_row = 2

trait_row = 5
age_row = None
gender_row = 2

# Define the conversion functions
def convert_trait(value: str):
    # Extract the part after the colon and strip spaces
    val = value.split(':')[-1].strip().lower()
    if val == 'yes':
        return 1
    elif val == 'no':
        return 0
    return None  # For unknown or unexpected values

def convert_age(value: str):
    # Not applicable here, so just return None
    return None

def convert_gender(value: str):
    val = value.split(':')[-1].strip().lower()
    if val == 'female':
        return 0
    elif val == 'male':
        return 1
    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 (only if 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=None,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    # Preview the extracted clinical DataFrame
    preview_result = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_result)

    # Save the clinical features 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])
# These identifiers appear to be valid human gene symbols.
# Hence, no additional mapping is required.
requires_gene_mapping = False

# STEP 6: Data Normalization and Linking

# 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, index=True)
print(f"Saved normalized gene data to {out_gene_data_file}")

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

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

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

# 5. Conduct final validation and record 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_processed,
    note="Final step completed with trait and gene data available."
)

# 6. If the linked data is usable, save it; otherwise, do not save
if is_usable:
    linked_data_processed.to_csv(out_data_file)
    print(f"Final linked data saved to {out_data_file}")