# 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
