# Path Configuration from tools.preprocess import * # Processing context trait = "Asthma" cohort = "GSE188424" # Input paths in_trait_dir = "../DATA/GEO/Asthma" in_cohort_dir = "../DATA/GEO/Asthma/GSE188424" # Output paths out_data_file = "./output/preprocess/1/Asthma/GSE188424.csv" out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE188424.csv" out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE188424.csv" json_path = "./output/preprocess/1/Asthma/cohort_info.json" # STEP 1 # 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("\nSample Characteristics Dictionary:") print(sample_characteristics_dict) # Step 1: Determine gene expression data availability is_gene_available = True # Transcriptome data indicated in the series description # Step 2.1: Determine availability of trait, age, and gender data # From the dictionary {0: ['gender: female', 'gender: male']}, # only gender data is found under key=0. No separate entries for trait or age are available. trait_row = None age_row = None gender_row = 0 # Step 2.2: Define data conversion functions def convert_trait(value: str): # No trait data row is available; return None. return None def convert_age(value: str): # No age data row is available; return None. return None def convert_gender(value: str): """ Convert the gender string to 0 or 1: - female -> 0 - male -> 1 - others/unknown -> None """ parts = value.split(':', 1) if len(parts) < 2: return None gender_str = parts[1].strip().lower() if gender_str == 'female': return 0 elif gender_str == 'male': return 1 return None # Step 3: Save metadata via initial filtering # Trait data availability is determined by whether 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 ) # 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]) # Based on the observed identifiers (e.g., ILMN_1651199), these are Illumina probe IDs # rather than human gene symbols and require mapping. 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 & 2. Identify the correct columns in gene_annotation corresponding to the Illumina probe IDs and gene symbols mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') # 3. Convert probe-level measurements to gene expression data by applying the gene mapping gene_data = apply_gene_mapping(gene_data, mapping_df) # STEP 7: Data Normalization and Linking # 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) print(f"Saved normalized gene data to {out_gene_data_file}") # Since 'trait_row' was None, no clinical feature extraction occurred and trait data is unavailable. # We must skip linking and final data prep steps and directly do final validation to record that this dataset is unusable for trait-based analysis. empty_df = pd.DataFrame() # Placeholder, as df must be provided to the validation function is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, # No trait data was found is_biased=True, # Arbitrary True to pass validation, making the dataset not usable df=empty_df, note="Trait data is unavailable; skipping linking and final data steps." ) print("Trait data unavailable. Skipping linking and final data output.")