# geneid -> GO ID mapping ns2assoc = ns2assoc, # ontology DAG godag = obodag, propagate_counts = False, # multiple testing correction method (fdr_bh is false discovery rate control with Benjamini-Hochberg) methods = ['fdr_bh'], # significance cutoff for method named above alpha = fdr_level_go_term ) goea_results_all = goeaobj.run_study(sig_genes['ens_gene'].tolist()) # write results to text file goeaobj.wr_tsv(snakemake.output.enrichment, goea_results_all) # plot results ensembl_id_to_symbol = dict(zip(all_genes['ens_gene'], all_genes['ext_gene'])) # from first plot output file name, create generic file name to trigger # separate plots for each of the gene ontology name spaces outplot_generic = snakemake.output.plot[0].replace('_BP.','_{NS}.', 1).replace('_CC.','_{NS}.', 1).replace('_MF.', '_{NS}.', 1) goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < fdr_level_go_term] plot_results( outplot_generic, # use pvals for coloring
class GOEngine: def __init__( self, work_dir: str = '.', clean_work_dir: bool = False, organism: str = 'human', study_parameters: Dict[str, Union[int, float, str, List, Dict]] = { 'propagate_counts': False, 'alpha': 0.05, 'methods': ['fdr_bh'] } ) -> GOEngine: """A GOEngine that can be used for performing analysis using GOATOOLS Args: work_dir (str, optional): The path to a temp directory were intermediate-results and raw data will be downloaded/written to. Defaults to the current working directory. clean_work_dir (bool, optional): Whether or not to remove data written to the work directory at class termination, default to True. organism (str, optional): The organism . Defaults to 'human'. study_parameters (Dict[str,Union[int,float,str,List,Dict]], optional): A dict of parameters to control the base function, defaults to {'propagate_counts':False,'alpha':0.05, 'methods':['fdr_bh']} Returns: GOEngine: return a GO engine that can be used for performing GO enrichment analysis GOEnrichmentStudyNS """ print("Creating a GO Engine ...") if not os.path.exists(work_dir): raise ValueError( f"The provided work path: {work_dir} does not exist!!!") self.work_dir = work_dir if organism != 'human' and organism != 'mouse': raise ValueError( f"The provided organism: {organism} is not support, current engine mainly work with human and moues only" ) print(f"\t --> Downloading data ...") obo_fname = download_go_basic_obo( os.path.join(work_dir, 'go-basic.obo')) gene2go_fname = download_ncbi_associations( os.path.join(work_dir, 'gene2go')) ## parse the GO term print( f"\t --> parsing the data and intializing the base GOEA object...") obo_dag = GODag(obo_fname) if organism == 'human': self._goea_obj = GOEnrichmentStudyNS( gene2iden_human.keys(), Gene2GoReader(gene2go_fname, taxids=[9606]).get_ns2assc(), obo_dag, **study_parameters) else: self._goea_obj = GOEnrichmentStudyNS( gene2iden_human.keys(), Gene2GoReader(gene2go_fname, taxids=[10090]).get_ns2assc(), obo_dag, **study_parameters) self._clean_work_dir = clean_work_dir self._gene_ids = None return def load_data(self, exp: Experiment, num_proteins: int = -1) -> None: """Load the data to the Engine, so GOEA can be conducted Args: exp (Experiment): An Experimental object to extract uniprot ids num_proteins (int, optional): The number of proteins to be included in the analysis. Defaults -1 to which mean use all proteins,\ otherwise it uses the number of proteins provided by the user. note that the function is sorted by number of peptides per protein,\ that is the first 10 protein means, getting the top 10 protein with most peptides. Raises: ValueError: if the function called while data being already associated with the engine from a previous call """ if self._gene_ids is not None: raise ValueError( f"There some data still in the engine, the first 10 genes are: {','.join(self._gene_ids[:10])}\ clean your engine from previous data using the function, clean_engine and try again." ) print( f"Getting the number of peptide per protein ..., started at: {time.ctime()}" ) num_protein_per_peptides = exp.get_peptides_per_protein() if num_proteins == -1: list_proteins = num_protein_per_peptides.iloc[:, 0].to_list() else: list_proteins = num_protein_per_peptides.iloc[:, 0].to_list( )[:num_proteins] print( f"Map uniprot to Entrez gene ids ..., starting at: {time.ctime()}") self._gene_ids = [ int(gene_id) for gene_id in map_from_uniprot_to_Entrez_Gene( list_proteins).iloc[:, 1].to_list() ] print(f"{len(self._gene_ids)} Genes have been correctly loaded") return def run_analysis( self, quite: bool = False, only_signifcant: bool = True, significance_level: float = 0.05, get_list_term: bool = False ) -> Union[pd.DataFrame, List[GOEnrichmentRecord]]: if quite: goea_results = self._goea_obj.run_study(self._gene_ids, prt=None) else: goea_results = self._goea_obj.run_study(self._gene_ids) if only_signifcant: goea_results = [ res for res in goea_results if res.p_fdr_bh < significance_level ] if get_list_term: return goea_results else: self._goea_obj.wr_tsv(os.path.join(self.work_dir, 'temp_file.tsv'), goea_results) results_df = pd.read_csv(os.path.join(self.work_dir, 'temp_file.tsv'), sep='\t') os.system(f"rm -f {os.path.join(self.work_dir,'temp_file.tsv')}") return results_df def clean_engine(self) -> None: """Remove Current list of gene ids associated with the engine """ self._gene_ids = None return def __del__(self) -> None: """class destructor, clean work directory if clean_work_dir is set to True """ if self.clean_work_dir: os.system(f"rm -f {self.work_dir}/*") return