p1/preprocess/Asthma/code/GSE123088.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE123088"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE123088"

# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE123088.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE123088.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE123088.csv"
json_path = "./output/preprocess/1/Asthma/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. Decide if gene expression data is available:
is_gene_available = True  # Based on the background info, we assume it's a gene expression dataset.

# Step 2. Identify keys and define conversion functions.

# 2.1 Find the rows that hold the trait (Asthma), age, and gender data.
trait_row = 1   # multiple diagnoses found here, including 'ASTHMA'
age_row = 3     # row with various ages
gender_row = 2  # row containing both 'Sex: Male' and 'Sex: Female'

# 2.2 Data type conversion functions
def convert_trait(x: str):
    """
    Convert trait to binary:
    1 -> Asthma
    0 -> Non-Asthma
    If cannot parse, return None.
    """
    parts = x.split(":")
    if len(parts) < 2:
        return None
    value = parts[1].strip().lower()
    # If the word "asthma" appears, treat it as 1; otherwise 0.
    return 1 if "asthma" in value else 0

def convert_age(x: str):
    """
    Convert age to a float (continuous).
    Unknown or unparsable -> None
    """
    parts = x.split(":")
    if len(parts) < 2:
        return None
    value = parts[1].strip()
    try:
        return float(value)
    except:
        return None

def convert_gender(x: str):
    """
    Convert gender to binary:
    0 -> Female
    1 -> Male
    Unknown -> None
    """
    parts = x.split(":")
    if len(parts) < 2:
        return None
    value = parts[1].strip().lower()
    if value == "male":
        return 1
    elif value == "female":
        return 0
    else:
        return None

# Step 3. Save basic 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
)

# Step 4. Clinical feature extraction (only if trait data is available).
if trait_row is not None:
    df_clinical = 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
    )
    # Observe the output
    preview_result = preview_df(df_clinical)
    print("Preview of extracted clinical features:\n", preview_result)

    # Save the clinical features
    df_clinical.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 numeric IDs observed (e.g., '1', '2', '3'), these are not standard human gene symbols.
# They appear to be Entrez IDs or some other numeric identifiers. Therefore, gene mapping is required.

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 which columns correspond to the gene expression IDs and the gene symbols:
#    From the preview, the "ID" column matches the numeric identifiers in the gene expression DataFrame,
#    and "ENTREZ_GENE_ID" represents the gene symbol (though it's also numeric, it's the only available gene label).

mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col="ID",           # The probe/ID column that matches the expression data index
    gene_col="ENTREZ_GENE_ID"  # The column we treat as the 'Gene' symbol
)

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

# gene_data now contains aggregated expression by gene.
# 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(df_clinical, 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, passing the final unbiased data.
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
)

# 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)