p1/preprocess/Atherosclerosis/code/GSE83500.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atherosclerosis"
cohort = "GSE83500"

# Input paths
in_trait_dir = "../DATA/GEO/Atherosclerosis"
in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE83500"

# Output paths
out_data_file = "./output/preprocess/1/Atherosclerosis/GSE83500.csv"
out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE83500.csv"
out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE83500.csv"
json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"

# STEP 1: Initial Data Loading

# 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,
    prefixes_a=background_prefixes,
    prefixes_b=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 the microarray-based gene expression mention

# 2.1 Variable Availability
#    The entire cohort has atherosclerosis, so it does not vary => trait_row = None
trait_row = None
age_row = 1   # "age: ..."
gender_row = 2  # "Sex: ..."

# 2.2 Data Type Conversions
def convert_trait(value: str):
    # No trait variation in this dataset => return None
    return None

def convert_age(value: str):
    # Example: "age: 69"
    parts = value.split(":")
    if len(parts) < 2:
        return None
    age_str = parts[1].strip()
    try:
        return float(age_str)
    except ValueError:
        return None

def convert_gender(value: str):
    # Example: "Sex: Male" or "Sex: Female"
    parts = value.split(":")
    if len(parts) < 2:
        return None
    gender_str = parts[1].strip().lower()
    if gender_str == 'male':
        return 1
    elif gender_str == 'female':
        return 0
    return None

# 3. Save Metadata (initial filtering)
is_trait_available = (trait_row is not None)
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
#    Skip because trait_row is None (trait not available)
# 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])
# The provided gene identifiers (e.g., '11715100_at') are Affymetrix probe IDs, not 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 for probe IDs and gene symbols in the annotation dataframe.
#    From the preview, the "ID" column matches the probe identifiers in gene_data,
#    and "Gene Symbol" column contains the actual gene symbols.

# 2. Get the gene mapping dataframe using these columns
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")

# 3. Convert probe-level data into gene-level data using the mapping
gene_data = apply_gene_mapping(gene_data, mapping_df)

# For verification, print out the shape and first few gene symbols
print("Gene data shape after mapping:", gene_data.shape)
print("First 20 gene symbols in the mapped data:", list(gene_data.index[:20]))
# STEP 7

# Since trait data is unavailable (trait_row = None), we do NOT have any clinical data to link.
# We'll only normalize the gene data, then finalize validation indicating no trait data.

# 1. Normalize the gene expression data to standard gene symbols.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print("Normalized gene expression data saved to:", out_gene_data_file)

# 2. Skip linking and missing value handling because we have no clinical data for trait-based analysis.

# 3. For final validation, we must provide a DataFrame and an is_biased flag. Since there's no trait,
#    we set is_trait_available=False, and use an empty DataFrame with is_biased=False.
empty_df = pd.DataFrame()

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=False,  # Arbitrarily False; trait is missing anyway.
    df=empty_df,
    note="No trait data available; dataset cannot be used for trait-based analysis."
)

if is_usable:
    print("Unexpectedly marked usable despite missing trait data.")
else:
    print("Dataset is not usable due to missing trait data. No final data saved.")