p1/preprocess/Age-Related_Macular_Degeneration/code/GSE62224.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Age-Related_Macular_Degeneration"
cohort = "GSE62224"

# Input paths
in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE62224"

# Output paths
out_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/GSE62224.csv"
out_gene_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/gene_data/GSE62224.csv"
out_clinical_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/clinical_data/GSE62224.csv"
json_path = "./output/preprocess/1/Age-Related_Macular_Degeneration/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 "Agilent whole genome microarrays" note in the background

# 2. Variable Availability and Data Type Conversion

# After reviewing the sample characteristics, there is no indication of AMD status, age, or gender. 
# The data rows represent donor IDs (which appear to be fetal IDs), plating densities, passage number, 
# culture days, cultureware, and treatments, none of which provide a varying "AMD" trait, numeric age, 
# or gender classification. Thus, all three variables are unavailable.

trait_row = None
age_row = None
gender_row = None

# Even though no conversion is needed (as data is unavailable), we define the required functions:

def convert_trait(value: str) -> None:
    """
    Since the trait data is not available, always return None.
    """
    return None

def convert_age(value: str) -> None:
    """
    Since age data is not available, always return None.
    """
    return None

def convert_gender(value: str) -> None:
    """
    Since gender data is not available, always return None.
    """
    return None

# 3. Save Metadata via initial filtering (is_final=False).
# Trait data is not available if trait_row is None.
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
# Since trait_row is None, we skip this substep (no clinical data found).
# 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 index, these IDs appear to be numeric, not standard human gene symbols.
# Therefore, gene symbol mapping 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. We identify that the gene expression data uses the 'ID' column in the annotation 
#    and the gene symbols are stored in the 'GENE_SYMBOL' column.
# 2. Build a gene mapping dataframe with these columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")

# 3. Convert probe-level measurements to gene expression data by applying the mapping.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7: Data Normalization and Linking

# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.

# 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)

# 2. Since trait data is missing, skip linking clinical and genetic data, 
#    skip missing-value handling and bias detection for the trait.

# 3. Conduct final validation and record info.
#    Since trait data is unavailable, set is_trait_available=False, 
#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
dummy_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,
    df=dummy_df,
    note="No trait data found; skipped clinical-linking steps."
)

# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
if is_usable:
    dummy_df.to_csv(out_data_file, index=True)