| # Path Configuration | |
| from tools.preprocess import * | |
| # Processing context | |
| trait = "Breast_Cancer" | |
| cohort = "GSE236725" | |
| # Input paths | |
| in_trait_dir = "../DATA/GEO/Breast_Cancer" | |
| in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE236725" | |
| # Output paths | |
| out_data_file = "./output/preprocess/1/Breast_Cancer/GSE236725.csv" | |
| out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE236725.csv" | |
| out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE236725.csv" | |
| json_path = "./output/preprocess/1/Breast_Cancer/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 the dataset likely contains gene expression data | |
| is_gene_available = True # The study used Affymetrix microarrays, so it's gene expression data | |
| # 2. Check variable availability | |
| # The "disease state: breast cancer" field is constant (i.e., identical for all samples), | |
| # so it does not provide variability for association analysis. Age and gender are not present. | |
| trait_row = None | |
| age_row = None | |
| gender_row = 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. If trait_row were available, we would extract clinical features here, but it's None, so we skip. | |
| # 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]) | |
| # These identifiers (e.g., "1007_s_at", "1053_at") are Affymetrix probe IDs, not standard gene symbols. | |
| 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 for probe IDs and gene symbols | |
| # ('ID' for probes, 'Gene Symbol' for gene symbols). | |
| # 2. Extract the mapping between probe IDs and gene symbols into a DataFrame. | |
| mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') | |
| # 3. Convert probe-level measurements to gene expression data using the mapping information. | |
| gene_data = apply_gene_mapping(gene_data, mapping_df) | |
| # STEP7: Data Normalization and Partial Validation (No Trait Data) | |
| # 1. Normalize gene symbols in the obtained gene expression data using synonym information from the NCBI Gene database. | |
| # Remove data of unrecognized gene symbols, and average the expression values of gene symbols that are mapped to the | |
| # same standard symbol. Save the normalized data as a CSV file to out_gene_data_file. | |
| normalized_gene_data = normalize_gene_symbols_in_index(gene_data) | |
| normalized_gene_data.to_csv(out_gene_data_file) | |
| # Since we do not have a trait (trait_row was None in previous steps), we cannot perform a final trait-based analysis. | |
| # Therefore, we record partial validation with is_final=False, so we do not need to provide df or is_biased. | |
| is_trait_available = False | |
| is_gene_available = True | |
| 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 | |
| ) | |
| # There is no trait data to link or validate further, so we do not perform additional steps here. | |
| # is_usable is expected to be False, indicating we cannot proceed with final usage. |