# Path Configuration from tools.preprocess import * # Processing context trait = "Allergies" cohort = "GSE192454" # Input paths in_trait_dir = "../DATA/GEO/Allergies" in_cohort_dir = "../DATA/GEO/Allergies/GSE192454" # Output paths out_data_file = "./output/preprocess/1/Allergies/GSE192454.csv" out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE192454.csv" out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE192454.csv" json_path = "./output/preprocess/1/Allergies/cohort_info.json" # STEP 1 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("\nSample Characteristics Dictionary:") print(sample_characteristics_dict) # 1. Gene Expression Data Availability # Based on "whole transcriptome profiling by microarray", we consider gene expression data present. is_gene_available = True # 2. Variable Availability and Data Type Conversion # From the sample characteristics dictionary, there is no row that indicates 'Allergies' # or any direct or inferred measure of atopic condition variability, so trait data is not available. trait_row = None # No 'age' or 'gender' information is provided. Hence, both are unavailable. age_row = None gender_row = None # Define data conversion functions as requested (they will not be used here since rows are None). def convert_trait(value: str): # Typically extract the part after the colon parts = value.split(':', 1) val = parts[1].strip() if len(parts) > 1 else '' # For "Allergies" we would normally map, but data is not available here # Unknown or missing values go to None return None def convert_age(value: str): # Typically extract numeric age or None parts = value.split(':', 1) val = parts[1].strip() if len(parts) > 1 else '' # Not available, so default to None return None def convert_gender(value: str): # Typically map female->0, male->1 parts = value.split(':', 1) val = parts[1].strip() if len(parts) > 1 else '' # Not available, so default to None return None # 3. Save Metadata with initial filtering 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 ) # 4. Clinical Feature Extraction # Since trait_row is None, no clinical feature extraction is performed. # 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 provided identifiers, they appear to be numeric IDs rather than human gene symbols. # Therefore, they likely need to be mapped to proper gene symbols. 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 that match the gene expression data ID and the gene symbol. # Here, the 'ID' column in gene_annotation matches the numeric IDs in gene_data, # and the 'GENE_SYMBOL' column stores the gene symbols. # 2. Get the gene mapping dataframe: mapping_df = get_gene_mapping(gene_annotation, "ID", "GENE_SYMBOL") # 3. Convert probe-level measurements to gene-level expression data: gene_data = apply_gene_mapping(gene_data, mapping_df) import pandas as pd # STEP 5: 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, index=True) print(f"Saved normalized gene data to {out_gene_data_file}") # Since in earlier steps trait_row was None, we have no clinical data to link. # Hence, there's no trait column to process. We'll skip linking and further steps # that require the trait. However, we must still perform a final validation. # Prepare a dummy DataFrame for the final validation dummy_df = pd.DataFrame() # We must provide is_biased and df to the final validation. # Because trait data is not available, this dataset won't be usable. is_biased = False # Arbitrarily set; since trait is unavailable, "is_usable" will be False anyway. is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, # Gene data is available is_trait_available=False, # Trait data is not available is_biased=is_biased, df=dummy_df, note="No trait data available; skipping linking." ) # 6. If data were usable, we would save it; otherwise we do nothing if is_usable: print("Data is unexpectedly marked usable, but trait is unavailable. Skipping save.")