:py:mod:`deeprvat.deeprvat.associate` ===================================== .. py:module:: deeprvat.deeprvat.associate .. autodoc2-docstring:: deeprvat.deeprvat.associate :allowtitles: Module Contents --------------- Functions ~~~~~~~~~ .. list-table:: :class: autosummary longtable :align: left * - :py:obj:`cli ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.cli :summary: * - :py:obj:`get_burden ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.get_burden :summary: * - :py:obj:`separate_parallel_results ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.separate_parallel_results :summary: * - :py:obj:`make_dataset_ ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.make_dataset_ :summary: * - :py:obj:`make_dataset ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.make_dataset :summary: * - :py:obj:`compute_xy_ ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.compute_xy_ :summary: * - :py:obj:`compute_xy ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.compute_xy :summary: * - :py:obj:`make_regenie_input_ ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.make_regenie_input_ :summary: * - :py:obj:`make_regenie_input ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.make_regenie_input :summary: * - :py:obj:`convert_regenie_output_ ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.convert_regenie_output_ :summary: * - :py:obj:`convert_regenie_output ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.convert_regenie_output :summary: * - :py:obj:`load_one_model ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.load_one_model :summary: * - :py:obj:`reverse_models ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.reverse_models :summary: * - :py:obj:`load_models ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.load_models :summary: * - :py:obj:`compute_burdens_ ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.compute_burdens_ :summary: * - :py:obj:`compute_burdens ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.compute_burdens :summary: * - :py:obj:`combine_burden_chunks ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.combine_burden_chunks :summary: * - :py:obj:`combine_burden_chunks_ ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.combine_burden_chunks_ :summary: * - :py:obj:`regress_on_gene_scoretest ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_on_gene_scoretest :summary: * - :py:obj:`regress_on_gene ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_on_gene :summary: * - :py:obj:`regress_ ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_ :summary: * - :py:obj:`regress ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress :summary: * - :py:obj:`combine_regression_results ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.combine_regression_results :summary: * - :py:obj:`average_burdens ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.average_burdens :summary: * - :py:obj:`regress_common ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_common :summary: * - :py:obj:`regress_common_ ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_common_ :summary: Data ~~~~ .. list-table:: :class: autosummary longtable :align: left * - :py:obj:`logger ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.logger :summary: * - :py:obj:`PLOF_COLS ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.PLOF_COLS :summary: * - :py:obj:`AGG_FCT ` - .. autodoc2-docstring:: deeprvat.deeprvat.associate.AGG_FCT :summary: API ~~~ .. py:data:: logger :canonical: deeprvat.deeprvat.associate.logger :value: 'getLogger(...)' .. autodoc2-docstring:: deeprvat.deeprvat.associate.logger .. py:data:: PLOF_COLS :canonical: deeprvat.deeprvat.associate.PLOF_COLS :value: ['Consequence_stop_gained', 'Consequence_frameshift_variant', 'Consequence_stop_lost', 'Consequence_... .. autodoc2-docstring:: deeprvat.deeprvat.associate.PLOF_COLS .. py:data:: AGG_FCT :canonical: deeprvat.deeprvat.associate.AGG_FCT :value: None .. autodoc2-docstring:: deeprvat.deeprvat.associate.AGG_FCT .. py:function:: cli() :canonical: deeprvat.deeprvat.associate.cli .. autodoc2-docstring:: deeprvat.deeprvat.associate.cli .. py:function:: get_burden(batch: typing.Dict, agg_models: typing.Dict[str, typing.List[torch.nn.Module]], device: torch.device = torch.device('cpu')) -> typing.Tuple[torch.Tensor, torch.Tensor] :canonical: deeprvat.deeprvat.associate.get_burden .. autodoc2-docstring:: deeprvat.deeprvat.associate.get_burden .. py:function:: separate_parallel_results(results: typing.List) -> typing.Tuple[typing.List, ...] :canonical: deeprvat.deeprvat.associate.separate_parallel_results .. autodoc2-docstring:: deeprvat.deeprvat.associate.separate_parallel_results .. py:function:: make_dataset_(config: typing.Dict, debug: bool = False, data_key: str = 'association_testing_data', skip_genotypes: bool = False, samples: typing.Optional[typing.List[int]] = None) -> torch.utils.data.Dataset :canonical: deeprvat.deeprvat.associate.make_dataset_ .. autodoc2-docstring:: deeprvat.deeprvat.associate.make_dataset_ .. py:function:: make_dataset(debug: bool, data_key: str, skip_genotypes: bool, config_file: str, out_file: str) :canonical: deeprvat.deeprvat.associate.make_dataset .. autodoc2-docstring:: deeprvat.deeprvat.associate.make_dataset .. py:function:: compute_xy_(config: typing.Dict, ds: torch.utils.data.Dataset, data_key='association_testing_data') -> typing.Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray] :canonical: deeprvat.deeprvat.associate.compute_xy_ .. autodoc2-docstring:: deeprvat.deeprvat.associate.compute_xy_ .. py:function:: compute_xy(dataset_file: typing.Optional[str], data_key: str, data_config_file: str, sample_file: pathlib.Path, x_file: pathlib.Path, y_file: pathlib.Path) :canonical: deeprvat.deeprvat.associate.compute_xy .. autodoc2-docstring:: deeprvat.deeprvat.associate.compute_xy .. py:function:: make_regenie_input_(debug: bool, skip_covariates: bool, skip_phenotypes: bool, skip_burdens: bool, burdens_genes_samples: typing.Optional[typing.Tuple[pathlib.Path, pathlib.Path, pathlib.Path]], repeat: int, average_repeats: bool, phenotype: typing.Tuple[typing.Tuple[str, pathlib.Path, pathlib.Path, pathlib.Path]], sample_file: typing.Optional[pathlib.Path], covariate_file: typing.Optional[pathlib.Path], phenotype_file: typing.Optional[pathlib.Path], bgen: typing.Optional[pathlib.Path], gene_metadata_file: pathlib.Path, gtf: pathlib.Path) :canonical: deeprvat.deeprvat.associate.make_regenie_input_ .. autodoc2-docstring:: deeprvat.deeprvat.associate.make_regenie_input_ .. py:function:: make_regenie_input(debug: bool, skip_covariates: bool, skip_phenotypes: bool, skip_burdens: bool, burdens_genes_samples: typing.Optional[typing.Tuple[pathlib.Path, pathlib.Path, pathlib.Path]], repeat: int, average_repeats: bool, phenotype: typing.Tuple[typing.Tuple[str, pathlib.Path, pathlib.Path]], sample_file: typing.Optional[pathlib.Path], covariate_file: typing.Optional[pathlib.Path], phenotype_file: typing.Optional[pathlib.Path], bgen: typing.Optional[pathlib.Path], gene_metadata_file: pathlib.Path, gtf: pathlib.Path) :canonical: deeprvat.deeprvat.associate.make_regenie_input .. autodoc2-docstring:: deeprvat.deeprvat.associate.make_regenie_input .. py:function:: convert_regenie_output_(repeat: int, phenotype: typing.Tuple[str, typing.Tuple[pathlib.Path, pathlib.Path]], gene_file: pathlib.Path) :canonical: deeprvat.deeprvat.associate.convert_regenie_output_ .. autodoc2-docstring:: deeprvat.deeprvat.associate.convert_regenie_output_ .. py:function:: convert_regenie_output(repeat: int, phenotype: typing.Tuple[str, typing.Tuple[pathlib.Path, pathlib.Path]], gene_file: pathlib.Path) :canonical: deeprvat.deeprvat.associate.convert_regenie_output .. autodoc2-docstring:: deeprvat.deeprvat.associate.convert_regenie_output .. py:function:: load_one_model(config: typing.Dict, checkpoint: str, device: torch.device = torch.device('cpu')) :canonical: deeprvat.deeprvat.associate.load_one_model .. autodoc2-docstring:: deeprvat.deeprvat.associate.load_one_model .. py:function:: reverse_models(model_config_file: str, data_config_file: str, checkpoint_files: typing.Tuple[str]) :canonical: deeprvat.deeprvat.associate.reverse_models .. autodoc2-docstring:: deeprvat.deeprvat.associate.reverse_models .. py:function:: load_models(config: typing.Dict, checkpoint_files: typing.Tuple[str], device: torch.device = torch.device('cpu')) -> typing.Dict[str, typing.List[torch.nn.Module]] :canonical: deeprvat.deeprvat.associate.load_models .. autodoc2-docstring:: deeprvat.deeprvat.associate.load_models .. py:function:: compute_burdens_(debug: bool, config: typing.Dict, ds: torch.utils.data.Dataset, cache_dir: str, agg_models: typing.Dict[str, typing.List[torch.nn.Module]], data_key: str = 'association_testing_data', n_chunks: typing.Optional[int] = None, chunk: typing.Optional[int] = None, device: torch.device = torch.device('cpu'), bottleneck: bool = False, compression_level: int = 1) -> typing.Tuple[numpy.ndarray, zarr.core.Array, zarr.core.Array] :canonical: deeprvat.deeprvat.associate.compute_burdens_ .. autodoc2-docstring:: deeprvat.deeprvat.associate.compute_burdens_ .. py:function:: compute_burdens(debug: bool, bottleneck: bool, data_key: str, n_chunks: typing.Optional[int], chunk: typing.Optional[int], dataset_file: typing.Optional[str], data_config_file: str, model_config_file: str, checkpoint_files: typing.Tuple[str], out_dir: str) :canonical: deeprvat.deeprvat.associate.compute_burdens .. autodoc2-docstring:: deeprvat.deeprvat.associate.compute_burdens .. py:function:: combine_burden_chunks(n_chunks: int, skip_burdens: bool, overwrite: bool, burdens_chunks_dir: pathlib.Path, result_dir: pathlib.Path) :canonical: deeprvat.deeprvat.associate.combine_burden_chunks .. autodoc2-docstring:: deeprvat.deeprvat.associate.combine_burden_chunks .. py:function:: combine_burden_chunks_(n_chunks: int, burdens_chunks_dir: pathlib.Path, skip_burdens: bool, overwrite: bool, result_dir: pathlib.Path) :canonical: deeprvat.deeprvat.associate.combine_burden_chunks_ .. autodoc2-docstring:: deeprvat.deeprvat.associate.combine_burden_chunks_ .. py:function:: regress_on_gene_scoretest(gene: str, burdens: numpy.ndarray, model_score) -> typing.Tuple[typing.List[str], typing.List[float], typing.List[float]] :canonical: deeprvat.deeprvat.associate.regress_on_gene_scoretest .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_on_gene_scoretest .. py:function:: regress_on_gene(gene: str, X: numpy.ndarray, y: numpy.ndarray, x_pheno: numpy.ndarray, use_bias: bool, use_x_pheno: bool) -> typing.Tuple[typing.List[str], typing.List[float], typing.List[float]] :canonical: deeprvat.deeprvat.associate.regress_on_gene .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_on_gene .. py:function:: regress_(config: typing.Dict, use_bias: bool, burdens: numpy.ndarray, y: numpy.ndarray, gene_indices: numpy.ndarray, genes: pandas.Series, x_pheno: numpy.ndarray, use_x_pheno: bool = True, do_scoretest: bool = True) -> pandas.DataFrame :canonical: deeprvat.deeprvat.associate.regress_ .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_ .. py:function:: regress(debug: bool, chunk: int, n_chunks: int, use_bias: bool, gene_file: str, config_file: str, xy_dir: str, burden_file: str, out_dir: str, do_scoretest: bool, sample_file: typing.Optional[str]) :canonical: deeprvat.deeprvat.associate.regress .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress .. py:function:: combine_regression_results(result_files: typing.Tuple[str], out_file: str, model_name: typing.Optional[str]) :canonical: deeprvat.deeprvat.associate.combine_regression_results .. autodoc2-docstring:: deeprvat.deeprvat.associate.combine_regression_results .. py:function:: average_burdens(repeats: typing.Tuple, burden_file: str, burden_out_file: str, agg_fct: typing.Optional[str] = 'mean', n_chunks: typing.Optional[int] = None, chunk: typing.Optional[int] = None) :canonical: deeprvat.deeprvat.associate.average_burdens .. autodoc2-docstring:: deeprvat.deeprvat.associate.average_burdens .. py:function:: regress_common(debug: bool, chunk: int, n_chunks: int, use_bias: bool, gene_file: str, repeat: int, config_file: str, burden_dir: str, out_file: str, do_scoretest: bool, sample_file: typing.Optional[str], burden_file: typing.Optional[str], genes_to_keep: typing.Optional[str], common_genotype_prefix: str) :canonical: deeprvat.deeprvat.associate.regress_common .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_common .. py:function:: regress_common_(config: typing.Dict, use_bias: bool, burdens: numpy.ndarray, y: numpy.ndarray, gene_indices: numpy.ndarray, genes: pandas.Series, x_pheno: numpy.ndarray, common_genotype_prefix: str, use_x_pheno: bool = True, do_scoretest: bool = True) -> pandas.DataFrame :canonical: deeprvat.deeprvat.associate.regress_common_ .. autodoc2-docstring:: deeprvat.deeprvat.associate.regress_common_