p1/preprocess/Colon_and_Rectal_Cancer/code/GSE46517.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Colon_and_Rectal_Cancer"
cohort = "GSE46517"

# Input paths
in_trait_dir = "../DATA/GEO/Colon_and_Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Colon_and_Rectal_Cancer/GSE46517"

# Output paths
out_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/GSE46517.csv"
out_gene_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/gene_data/GSE46517.csv"
out_clinical_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/clinical_data/GSE46517.csv"
json_path = "./output/preprocess/1/Colon_and_Rectal_Cancer/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
#    Based on the background info: "RNA was extracted and run on the ... microarray chip"
#    This indicates standard gene expression data is very likely available.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
#    We look for the trait "Colon_and_Rectal_Cancer" in the sample characteristics.
#    No rows show consistent data indicating colon/rectal cancer as the primary trait.
#    Therefore, we consider that trait data is NOT available.
trait_row = None

#    For age: row 7 contains multiple entries of "age at time of resection: ...",
#    indicating distinct numeric values. Thus, age data is available at row 7.
age_row = 7

#    For gender: row 8 has both "gender: male" and "gender: female", hence it
#    carries at least two distinct values. So let's use row 8 for gender.
gender_row = 8

# 2.2 Data Type Conversion

def convert_trait(raw_value: str) -> int:
    """
    Since trait data is not available (trait_row=None),
    this function is not expected to be called.
    However, we define a stub to maintain consistency.
    """
    return None

def convert_age(raw_value: str) -> float:
    """
    Convert age string (e.g. 'age at time of resection: 72y 4m')
    to a numeric value in years (float). If parsing fails, return None.
    """
    try:
        # The value after the colon might look like '72y 4m'
        # We'll extract that part and parse the years.
        value_part = raw_value.split(':', 1)[-1].strip()  # '72y 4m'
        # Split on space => ['72y', '4m'] or just one piece if months missing
        parts = value_part.split()
        # The first part is something like '72y'
        year_str = parts[0].lower().replace('y', '')
        year_val = float(year_str)
        return year_val
    except Exception:
        return None

def convert_gender(raw_value: str) -> int:
    """
    Convert gender string (e.g. 'gender: male' or 'gender: female')
    to a binary (female=0, male=1). If parsing fails, return None.
    """
    try:
        value_part = raw_value.split(':', 1)[-1].strip().lower()  # 'male' or 'female'
        if value_part == 'female':
            return 0
        elif value_part == 'male':
            return 1
        else:
            return None
    except Exception:
        return None

# 3. Save Metadata
#    Perform initial filtering. Trait is not available, so is_trait_available=False.
#    This dataset will fail initial filtering due to missing trait, but we still log metadata.
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
#    We only do this if trait_row is not None. Here, trait_row = None, so we skip extraction.
#    End of this step.
# 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 gene identifiers resemble Affymetrix probe set IDs, which are not official gene symbols.
# Therefore, these identifiers will need to be mapped to gene symbols.
print("\nrequires_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. From the annotation preview, we see 'ID' corresponds to the probe ids in gene_data.index,
#    and 'Gene Symbol' holds the corresponding gene symbols.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")

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

# Print a brief summary to confirm successful mapping
print("Gene-level expression data dimensions:", gene_data.shape)
# 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)

# According to previous steps, we found that trait data is not available (trait_row was None),
# so is_trait_available is False.
is_trait_available = False

if not is_trait_available:
    # 5. Conduct final validation to record metadata. Since we have no trait data, the dataset won't be usable.
    # We must provide 'df' and 'is_biased' to the function; passing an empty DataFrame and is_biased=True
    # ensures it is marked as not usable.
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,   # We do have gene data
        is_trait_available=False, # Trait is not available
        is_biased=True,           # This will mark it as not usable
        df=pd.DataFrame(),        # Placeholder DataFrame
        note="Trait data not available; dataset is not usable."
    )

    # Since trait is unavailable, we must skip linking or saving any final linked data.
else:
    # If trait data were available, we would proceed with linking and further steps.
    # But since it is not, this branch is never entered.
    pass