p1/preprocess/COVID-19/code/GSE216705.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "COVID-19"
cohort = "GSE216705"

# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE216705"

# Output paths
out_data_file = "./output/preprocess/1/COVID-19/GSE216705.csv"
out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE216705.csv"
out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE216705.csv"
json_path = "./output/preprocess/1/COVID-19/cohort_info.json"

# STEP1
from tools.preprocess import *

# 1. Attempt to identify the paths to the SOFT file and the matrix file
try:
    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
except AssertionError:
    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
    soft_file, matrix_file = None, None

if soft_file is None or matrix_file is None:
    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
else:
    # 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("\nSample Characteristics Dictionary:")
    print(sample_characteristics_dict)
# Step 1: Determine if the dataset contains gene expression data
is_gene_available = True  # Based on "metaData_microarrays.txt", this is likely microarray gene expression data

# Step 2: Identify availability of trait, age, and gender data
#         Since the sample characteristics only have "strain" and "metadata info" with no variation or mention of these variables,
#         treat them all as not available.
trait_row = None
age_row = None
gender_row = None

# Step 2.2: Define the data conversion functions (though they won't be used here because the rows are None)
def convert_trait(x: str):
    # For demonstration, parse after colon; return 1 if "COVID-19" is found, else 0, else None.
    parts = x.split(':', 1)
    value = parts[1].strip() if len(parts) > 1 else x
    if "COVID-19" in value.lower():
        return 1
    return None

def convert_age(x: str):
    # Convert to continuous age. We do not have actual data, so return None.
    return None

def convert_gender(x: str):
    # Convert to binary: female -> 0, male -> 1. Return None by default here.
    return None

# Step 3: Conduct initial filtering and save metadata
# Trait data is considered available only if trait_row is not None.
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
)

# Step 4: Since trait_row is None, we skip clinical feature extraction.
# 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 given identifiers (e.g. "10338001", "10338002") are typically probe IDs from a microarray platform.
# They do not look like standard human gene symbols such as "TP53" or "ACTB".
# Therefore, gene mapping to standard 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. Identify the columns in the gene_annotation DataFrame where 'ID' matches the probe identifiers
#    and 'gene_assignment' contains the gene symbols or descriptive info to be parsed.
prob_col = "ID"
gene_col = "gene_assignment"

# 2. Get a DataFrame mapping from probe IDs to gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Convert probe-level measurements to gene-level expression
gene_data = apply_gene_mapping(gene_data, mapping_df)
import os
import pandas as pd

# STEP7: Data Normalization and Linking

# 1) Normalize the gene symbols in the previously obtained gene_data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2) Load clinical data only if it exists and is non-empty
if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
    # Read the file
    clinical_temp = pd.read_csv(out_clinical_data_file)

    # Adjust row index to label the trait, age, and gender properly
    if clinical_temp.shape[0] == 3:
        clinical_temp.index = [trait, "Age", "Gender"]
    elif clinical_temp.shape[0] == 2:
        clinical_temp.index = [trait, "Gender"]
    elif clinical_temp.shape[0] == 1:
        clinical_temp.index = [trait]

    # 2) Link the clinical and normalized genetic data
    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)

    # 3) Handle missing values
    linked_data = handle_missing_values(linked_data, trait)

    # 4) Check for severe bias in the trait; remove biased demographic features if present
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5) Final quality validation and save metadata
    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,
        note=f"Final check on {cohort} with {trait}."
    )

    # 6) If the linked data is usable, save it
    if is_usable:
        linked_data.to_csv(out_data_file)
else:
    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
    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=True,  # Force a fallback so that it's flagged as unusable
        df=pd.DataFrame(),
        note=f"No trait data found for {cohort}, final metadata recorded."
    )
    # Per instructions, do not save a final linked data file when trait data is absent.