p1/preprocess/Asthma/code/GSE182797.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE182797"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE182797"

# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE182797.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE182797.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE182797.csv"
json_path = "./output/preprocess/1/Asthma/cohort_info.json"

# STEP 1

# 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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1) Gene Expression Data Availability
is_gene_available = True  # Based on "Transcriptomic profiling" and "microarray analyses"

# 2) Variable Availability and Data Type Conversion
#    2.1 Identify rows
trait_row = 0  # "diagnosis: ..." contains multiple distinct values including "adult-onset asthma"
age_row = 2    # "age: ..." contains multiple numerical values
gender_row = None  # Only "gender: Female" found, no variability => not available

#    2.2 Define conversion functions
def convert_trait(value: str):
    """
    Convert diagnosis data to a binary label:
    adult-onset asthma -> 1, otherwise (healthy/IEI) -> 0, unknown -> None
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if 'adult-onset asthma' in val:
        return 1
    elif 'healthy' in val or 'iei' in val:
        return 0
    return None

def convert_age(value: str):
    """Convert age data to a float. Unknown or invalid entries -> None."""
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str):
    """
    Convert gender data to binary (female->0, male->1).
    Not used here because gender_row is None, but defined for completeness.
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[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:
    # 'clinical_data' is assumed to be the DataFrame containing sample characteristics
    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 and save the selected clinical data
    preview = preview_df(selected_clinical_df)
    print("Preview of extracted clinical data:", preview)
    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 (e.g., 'A_19_P00315452') are microarray probe IDs
# and do not appear to be standard human gene symbols.
# Therefore, they 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))
# STEP: Gene Identifier Mapping

# 1. Identify columns in gene_annotation for probe IDs and gene symbols
probe_col = 'ID'
gene_symbol_col = 'GENE_SYMBOL'

# 2. Get the mapping of probe IDs to gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

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

# (Optional) Check the shape or a small preview of the mapped gene_data
print("Mapped gene_data shape:", gene_data.shape)
# STEP 7: Data Normalization and Linking

# 1) Normalize gene symbols
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2) Read the previously saved clinical data (which should have shape (2 rows) x (80 columns)) 
#    so that it aligns correctly with normalized_gene_data.
temp_clinical = pd.read_csv(out_clinical_data_file)        # Use the first row as header
temp_clinical.index = [trait, "Age"]
temp_clinical.columns = normalized_gene_data.columns       # Match with the 80 sample IDs

# Link the clinical and gene data
linked_data = geo_link_clinical_genetic_data(temp_clinical, normalized_gene_data)

# 3) Handle missing values
processed_data = handle_missing_values(linked_data, trait_col=trait)

# 4) Remove biased demographic features; check whether our trait is overly biased
trait_biased, final_data = judge_and_remove_biased_features(processed_data, trait=trait)

# 5) Conduct final dataset validation
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=final_data,
    note="Final processed dataset for trait and gene expression."
)

# 6) If the dataset is usable, save the final linked data
if is_usable:
    final_data.to_csv(out_data_file)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("Dataset not usable. No final linked file was saved.")