# Path Configuration from tools.preprocess import * # Processing context trait = "Chronic_kidney_disease" cohort = "GSE104948" # Input paths in_trait_dir = "../DATA/GEO/Chronic_kidney_disease" in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE104948" # Output paths out_data_file = "./output/preprocess/1/Chronic_kidney_disease/GSE104948.csv" out_gene_data_file = "./output/preprocess/1/Chronic_kidney_disease/gene_data/GSE104948.csv" out_clinical_data_file = "./output/preprocess/1/Chronic_kidney_disease/clinical_data/GSE104948.csv" json_path = "./output/preprocess/1/Chronic_kidney_disease/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) Determine if gene expression data is available is_gene_available = True # Based on the metadata (Affymetrix microarrays for gene expression) # 2) Identify variable availability trait_row = 1 # diagnosis field with multiple diagnoses age_row = None # no age data detected gender_row = None # no gender data detected # 2) Define data type conversions def convert_trait(value: Any) -> Optional[int]: """ Convert the diagnosis field to a binary indicator for Chronic Kidney Disease: - If 'Tumor Nephrectomy' or unknown, map to 0/None - Otherwise, map to 1 """ if pd.isna(value): return None # Extract the part after the colon if present parts = str(value).split(':', 1) if len(parts) == 2: val_str = parts[1].strip() else: val_str = parts[0].strip() if val_str.lower() in ['tumor nephrectomy', '']: return 0 if val_str.lower() == 'nan': return None # Everything else is considered CKD = 1 return 1 def convert_age(value: Any) -> Optional[float]: # This dataset has no age data; return None return None def convert_gender(value: Any) -> Optional[int]: # This dataset has no gender data; return None return None # 3) Conduct initial filtering and save metadata 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) If trait data is available, extract and preview the clinical features if trait_row is not None: selected_clinical_data = geo_select_clinical_features( 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_result = preview_df(selected_clinical_data) print("Preview of selected clinical features:", preview_result) # Save the clinical data selected_clinical_data.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 the ID patterns (e.g., "10000_at", "10001_at"), these look like probe set IDs # from a microarray platform rather than human gene symbols. Hence, they 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) Identify the columns in the gene annotation dataframe that match the # probe identifiers and the columns that provide the gene symbols. prob_col = "ID" gene_col = "Symbol" # 2) Get the gene mapping dataframe by extracting these two columns. mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col) # 3) Convert probe-level measurements to gene-level expression data. gene_data = apply_gene_mapping(gene_data, mapping_df) # Print out a brief check of the mapped gene data print("Mapped gene_data shape:", gene_data.shape) print("First few gene symbols:", gene_data.index[:10]) # STEP7 # 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library. normalized_gene_data = normalize_gene_symbols_in_index(gene_data) normalized_gene_data.to_csv(out_gene_data_file) # 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library. linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data) # 3. Handle missing values in the linked data linked_data = handle_missing_values(linked_data, trait) # 4. Determine whether the trait and some demographic features are severely biased, and remove biased features. is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Conduct quality check and save the cohort information. 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=is_trait_biased, df=linked_data ) # 6. If the linked data is usable, save it as a CSV file to 'out_data_file'. if is_usable: unbiased_linked_data.to_csv(out_data_file)