p1/preprocess/Colon_and_Rectal_Cancer/code/GSE46862.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Colon_and_Rectal_Cancer"
cohort = "GSE46862"

# Input paths
in_trait_dir = "../DATA/GEO/Colon_and_Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Colon_and_Rectal_Cancer/GSE46862"

# Output paths
out_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/GSE46862.csv"
out_gene_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/gene_data/GSE46862.csv"
out_clinical_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/clinical_data/GSE46862.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
is_gene_available = True  # The dataset uses Affymetrix GeneChip arrays, indicating gene expression data.

# 2. Variable Availability and Data Type Conversion

# Based on the sample characteristics dictionary, the human trait "Colon_and_Rectal_Cancer" is not explicitly
# listed. All samples are rectal cancer, so there's no meaningful variation for our trait.
trait_row = None  # Not available

# 'Age' data appears in row 1, with multiple distinct values.
age_row = 1

# 'Gender' data appears in row 2, with values "male" and "female".
gender_row = 2

# Define conversion functions:
def convert_trait(x: str) -> int:
    # Since trait data is not available (trait_row is None),
    # this function will never be called, but we define it for completeness.
    return None

def convert_age(x: str) -> float:
    # Typical format: "age: 68"
    # Extract the substring after the first colon and convert to float.
    try:
        value_str = x.split(':', 1)[1].strip()
        return float(value_str)
    except:
        return None

def convert_gender(x: str) -> int:
    # Typical format: "Sex: male" or "Sex: female"
    # Extract the substring after the colon and convert to binary (male=1, female=0).
    try:
        value_str = x.split(':', 1)[1].strip().lower()
        if value_str == 'male':
            return 1
        elif value_str == 'female':
            return 0
        else:
            return None
    except:
        return None

# 3. Save Metadata
# Trait data is not available because 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 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 numeric IDs appear to be probe identifiers rather than standard human gene symbols.
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 & 2. Identify the columns "ID" and "gene_assignment" as the probe IDs and gene symbol fields, respectively,
#    then extract them to form the mapping dataframe.
mapping_df = get_gene_mapping(gene_annotation, "ID", "gene_assignment")

# 3. Convert probe-level data into gene-level data by applying this mapping to our gene_data.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP7
# Since trait_row was determined to be None, there is no available trait data to link with.
# We only proceed with normalizing and saving the gene expression data, then record partial metadata.

# 1. Normalize the obtained gene data with synonyms from the NCBI Gene database.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2 - 4. Skip any clinical linking or missing value handling since no trait data is available.
# 5. Perform partial validation (not final) to record that trait data is unavailable.
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    note="No trait data available; skipping final validation and combined dataset."
)

# 6. Since the dataset is not usable for trait-based analysis, we do not save any linked data.