# Path Configuration from tools.preprocess import * # Processing context trait = "Chronic_kidney_disease" cohort = "GSE180394" # Input paths in_trait_dir = "../DATA/GEO/Chronic_kidney_disease" in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE180394" # Output paths out_data_file = "./output/preprocess/1/Chronic_kidney_disease/GSE180394.csv" out_gene_data_file = "./output/preprocess/1/Chronic_kidney_disease/gene_data/GSE180394.csv" out_clinical_data_file = "./output/preprocess/1/Chronic_kidney_disease/clinical_data/GSE180394.csv" json_path = "./output/preprocess/1/Chronic_kidney_disease/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. Gene Expression Data Availability is_gene_available = True # This dataset uses microarray for renal transcriptome analysis # 2. Variable Availability and Data Type Conversion # After assessing the sample characteristics: # - The disease/trait information is at row=0, so trait_row=0 # - Age and gender information are not present, so age_row=None and gender_row=None trait_row = 0 age_row = None gender_row = None # Define data type conversion functions def convert_trait(value: str): """ Convert sample group to a binary indicator of CKD presence: 0 for healthy (e.g., living donor or unaffected part), 1 for CKD or other kidney disease states. """ if not value or ':' not in value: return None # Extract text after "sample group:" val = value.split(':', 1)[-1].strip().lower() if 'donor' in val or 'unaffected' in val: return 0 else: return 1 def convert_age(value: str): """No age data available, return None.""" return None def convert_gender(value: str): """No gender data available, return None.""" return None # 3. Save Metadata (initial filtering) 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. Clinical Feature Extraction (only if trait data is available) if trait_row is not None: selected_clinical_df = geo_select_clinical_features( clinical_df=clinical_data, # assumed available from previous context 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 extracted clinical features preview = preview_df(selected_clinical_df) print("Preview of selected clinical features:", preview) # Save clinical data if available selected_clinical_df.to_csv(out_clinical_data_file, index=False) # STEP3 # Attempt to read gene expression data; if the library function yields an empty DataFrame, # try re-reading without ignoring lines that start with '!' (because sometimes GEO data may # place actual expression rows under lines that begin with '!'). gene_data = get_genetic_data(matrix_file) if gene_data.empty: print("[WARNING] The gene_data is empty. Attempting alternative loading without treating '!' as comments.") import gzip # Locate the marker line first skip_rows = 0 with gzip.open(matrix_file, 'rt') as file: for i, line in enumerate(file): if "!series_matrix_table_begin" in line: skip_rows = i + 1 break # Read the data again, this time not treating '!' as comment gene_data = pd.read_csv( matrix_file, compression="gzip", skiprows=skip_rows, delimiter="\t", on_bad_lines="skip" ) gene_data = gene_data.rename(columns={"ID_REF": "ID"}).astype({"ID": "str"}) gene_data.set_index("ID", inplace=True) # Print the first 20 row IDs to confirm data structure print(gene_data.index[:20]) # These identifiers (e.g., "100009613_at") are typical Affymetrix probe IDs, not standard human gene symbols. print("\nrequires_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) From the annotation preview, we see that the column "ID" matches the probe IDs in the gene expression data, # and "ENTREZ_GENE_ID" stores the corresponding gene identifier (which we'll treat as the 'gene symbol' column). probe_col = "ID" symbol_col = "ENTREZ_GENE_ID" # 2) Get a two-column dataframe for mapping probes to genes gene_mapping = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col) # 3) Apply the mapping to convert probe-level data into gene-level data gene_data = apply_gene_mapping(gene_data, gene_mapping) # Print a brief preview print("Gene expression data shape:", gene_data.shape) print("Preview of the mapped gene expression data:") print(gene_data.head()) 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, "Age"] 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.