# Path Configuration from tools.preprocess import * # Processing context trait = "COVID-19" cohort = "GSE243348" # Input paths in_trait_dir = "../DATA/GEO/COVID-19" in_cohort_dir = "../DATA/GEO/COVID-19/GSE243348" # Output paths out_data_file = "./output/preprocess/1/COVID-19/GSE243348.csv" out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE243348.csv" out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE243348.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) # 1. Determine gene expression data availability is_gene_available = True # Based on the presence of "host response genes" (mRNA profiling) in the series summary # 2. Check variable availability and prepare conversion functions # 2.1 Identify the rows where the trait, age, and gender data appear trait_row = 0 # "disease status: COVID-19+" or "disease status: Healthy uninfected" age_row = 3 # "age: XX" gender_row = 2 # "Sex: female" or "Sex: male" # 2.2 Create conversion functions def convert_trait(raw_value: str): """ Convert raw trait value to binary (COVID-19+ => 1, Healthy => 0). """ if not isinstance(raw_value, str): return None parts = raw_value.split(":", 1) if len(parts) < 2: return None val = parts[1].strip().lower() if val == "covid-19+": return 1 elif val == "healthy uninfected": return 0 return None def convert_age(raw_value: str): """ Convert raw age value to a continuous variable. """ if not isinstance(raw_value, str): return None parts = raw_value.split(":", 1) if len(parts) < 2: return None val = parts[1].strip() try: return float(val) except ValueError: return None def convert_gender(raw_value: str): """ Convert raw gender value to binary (female => 0, male => 1). """ if not isinstance(raw_value, str): return None parts = raw_value.split(":", 1) if len(parts) < 2: return None val = parts[1].strip().lower() if val == "female": return 0 elif val == "male": return 1 return None # Assume trait data is available if trait_row is not None is_trait_available = (trait_row is not None) # 3. Conduct initial filtering and save metadata _ = 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 only if trait_row is not None if trait_row is not None: # 'clinical_data' is assumed to be the DataFrame obtained from a previous step selected_clinical_df = geo_select_clinical_features( clinical_df=clinical_data, trait=trait, trait_row=trait_row, convert_trait=convert_trait, age_row=age_row, convert_age=convert_age, gender_row=gender_row, convert_gender=convert_gender ) # Preview and save preview = preview_df(selected_clinical_df) print("Preview of selected clinical features:", preview) selected_clinical_df.to_csv(out_clinical_data_file, index=False) # 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 biomedical knowledge, these identifiers (e.g., "ACE", "ACKR2", "ACSL1", "AKT1") are known human gene symbols. print("They appear to be recognized human gene symbols.") print("requires_gene_mapping = False") 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.