p1/preprocess/Atrial_Fibrillation/code/GSE235307.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE235307"

# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE235307"

# Output paths
out_data_file = "./output/preprocess/1/Atrial_Fibrillation/GSE235307.csv"
out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/GSE235307.csv"
out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/GSE235307.csv"
json_path = "./output/preprocess/1/Atrial_Fibrillation/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 gene expression availability
is_gene_available = True  # From the background information, this dataset involves gene expression analysis

# Step 2.1: Identify row indices for trait, age, and gender
trait_row = 5    # "cardiac rhythm after 1 year follow-up: Sinus rhythm / Atrial fibrillation"
age_row = 2      # "age: ##"
gender_row = 1   # "gender: Male / Female"

# Step 2.2: Define functions for data type conversion
def convert_trait(value: str):
    """
    Convert the trait data into binary (0 or 1).
    Expecting strings like 'cardiac rhythm after 1 year follow-up: Sinus rhythm'
    or 'cardiac rhythm after 1 year follow-up: Atrial fibrillation'.
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[-1].strip().lower()
    if 'atrial fibrillation' in val:
        return 1
    elif 'sinus rhythm' in val:
        return 0
    return None

def convert_age(value: str):
    """
    Convert the age data into a continuous float.
    Expecting strings like 'age: 64'.
    """
    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 into binary (female=0, male=1).
    Expecting strings like 'gender: Male' or 'gender: Female'.
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[-1].strip().lower()
    if val == 'male':
        return 1
    elif val == 'female':
        return 0
    return None

# Step 3: Conduct initial filtering and save metadata
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=(trait_row is not None)
)

# Step 4: If trait data is available, extract clinical features and preview
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,       # Assume 'clinical_data' DataFrame is loaded from previous steps
        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
    )
    print("Preview of selected clinical features:", preview_df(selected_clinical_df))
    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])
# From examining the provided gene expression data index (4,5,6...23), 
# these do not look like standard human gene symbols.
# They are more likely numeric probe IDs or array-specific identifiers.
# Therefore, mapping to recognized human gene symbols is required.

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. Decide which column in 'gene_annotation' matches the gene expression data ID and which column stores gene symbols.
#    From the preview, 'ID' in the annotation appears to match the numeric IDs in the expression data,
#    and 'GENE_SYMBOL' provides the corresponding gene symbols.

# 2. Get the gene mapping dataframe by extracting those columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# 3. Apply the mapping to convert probe-level expression to gene-level expression.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# For reference, let's print the shape and a small preview of the mapped gene_data.
print("Mapped gene_data shape:", gene_data.shape)
print("Preview of mapped gene_data:")
print(preview_df(gene_data))
# 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
linked_data_processed = handle_missing_values(linked_data, trait_col=trait)

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

# 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)