:py:mod:`deeprvat.data.dense_gt` ================================ .. py:module:: deeprvat.data.dense_gt .. autodoc2-docstring:: deeprvat.data.dense_gt :allowtitles: Module Contents --------------- Classes ~~~~~~~ .. list-table:: :class: autosummary longtable :align: left * - :py:obj:`DenseGTDataset ` - .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset :summary: Functions ~~~~~~~~~ .. list-table:: :class: autosummary longtable :align: left * - :py:obj:`get_matched_sample_indices ` - .. autodoc2-docstring:: deeprvat.data.dense_gt.get_matched_sample_indices :summary: Data ~~~~ .. list-table:: :class: autosummary longtable :align: left * - :py:obj:`logger ` - .. autodoc2-docstring:: deeprvat.data.dense_gt.logger :summary: * - :py:obj:`DEFAULT_CHROMOSOMES ` - .. autodoc2-docstring:: deeprvat.data.dense_gt.DEFAULT_CHROMOSOMES :summary: * - :py:obj:`AGGREGATIONS ` - .. autodoc2-docstring:: deeprvat.data.dense_gt.AGGREGATIONS :summary: API ~~~ .. py:data:: logger :canonical: deeprvat.data.dense_gt.logger :value: 'getLogger(...)' .. autodoc2-docstring:: deeprvat.data.dense_gt.logger .. py:data:: DEFAULT_CHROMOSOMES :canonical: deeprvat.data.dense_gt.DEFAULT_CHROMOSOMES :value: None .. autodoc2-docstring:: deeprvat.data.dense_gt.DEFAULT_CHROMOSOMES .. py:data:: AGGREGATIONS :canonical: deeprvat.data.dense_gt.AGGREGATIONS :value: None .. autodoc2-docstring:: deeprvat.data.dense_gt.AGGREGATIONS .. py:function:: get_matched_sample_indices(x, y) :canonical: deeprvat.data.dense_gt.get_matched_sample_indices .. autodoc2-docstring:: deeprvat.data.dense_gt.get_matched_sample_indices .. py:class:: DenseGTDataset(gt_file: str = None, variant_file: str = None, split: str = '', train_dataset: typing.Optional[torch.utils.data.Dataset] = None, chromosomes: typing.List[str] = None, phenotype_file: typing.Optional[str] = None, standardize_xpheno: bool = True, standardize_anno: bool = False, standardize_rare_anno: bool = False, standardize_rare_anno_columns: typing.Optional[typing.List] = None, standardize_rare_anno_params: typing.Optional[typing.Dict] = None, permute_y: bool = False, y_transformation: typing.Optional[str] = None, x_phenotypes: typing.List[str] = [], grouping_level: typing.Optional[str] = 'gene', group_common: bool = False, return_sparse: bool = False, annotations: typing.List[str] = [], annotation_file: typing.Optional[str] = None, precomputed_annotations: typing.Optional[typing.Tuple[pandas.DataFrame, pandas.DataFrame, numpy.ndarray]] = None, annotation_aggregation: typing.Union[str, dict] = 'max', y_phenotypes: typing.List[str] = [], skip_y_na: bool = True, skip_x_na: bool = False, sample_file: str = None, sim_phenotype_file: typing.Optional[str] = None, min_common_variant_count: typing.Optional[int] = None, min_common_af: typing.Optional[typing.Dict[str, float]] = None, max_rare_af: typing.Optional[typing.Dict[str, float]] = None, use_common_variants: bool = True, use_rare_variants: bool = False, rare_embedding: typing.Optional[typing.Dict] = None, rare_ignore_unknown_gene: bool = True, exons_to_keep: typing.Optional[typing.Set[int]] = None, genes_to_keep: typing.Optional[typing.Set[str]] = None, gene_file: typing.Optional[str] = None, gene_types_to_keep: typing.Optional[typing.List[str]] = None, ignore_by_annotation: typing.Optional[typing.List[typing.Tuple[str, typing.Any]]] = None, max_pval: typing.Optional[typing.Dict[str, float]] = None, variants: typing.Optional[pandas.DataFrame] = None, variants_to_keep: typing.Optional[typing.Union[typing.List[str], str]] = None, zarr_dir: typing.Optional[str] = None, cache_matrices: bool = False, verbose: bool = False, return_genotypes: bool = True) :canonical: deeprvat.data.dense_gt.DenseGTDataset Bases: :py:obj:`torch.utils.data.Dataset` .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset .. rubric:: Initialization .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.__init__ .. py:method:: __getitem__(idx: int) -> torch.tensor :canonical: deeprvat.data.dense_gt.DenseGTDataset.__getitem__ .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.__getitem__ .. py:method:: __len__() -> int :canonical: deeprvat.data.dense_gt.DenseGTDataset.__len__ .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.__len__ .. py:method:: get_stand_params() :canonical: deeprvat.data.dense_gt.DenseGTDataset.get_stand_params .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.get_stand_params .. py:method:: setup_phenotypes(phenotype_file: str, sim_phenotype_file: typing.Optional[str], skip_y_na: bool, skip_x_na: bool, sample_file: typing.Optional[str]) :canonical: deeprvat.data.dense_gt.DenseGTDataset.setup_phenotypes .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.setup_phenotypes .. py:method:: get_variant_ids(matrix_indices: numpy.ndarray) -> numpy.ndarray :canonical: deeprvat.data.dense_gt.DenseGTDataset.get_variant_ids .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.get_variant_ids .. py:method:: dense_to_sparse(dense_genotype: typing.Union[torch.Tensor, typing.Dict[str, torch.Tensor]], keep_groups: bool = False) -> pandas.DataFrame :canonical: deeprvat.data.dense_gt.DenseGTDataset.dense_to_sparse .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.dense_to_sparse .. py:method:: get_annotations(variant_ids: typing.Union[numpy.ndarray, pandas.Series], group: bool = False, aggregate_groups: bool = False) -> typing.Union[torch.Tensor, typing.Dict[str, torch.Tensor]] :canonical: deeprvat.data.dense_gt.DenseGTDataset.get_annotations .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.get_annotations .. py:method:: setup_zarr(zarr_dir: str) :canonical: deeprvat.data.dense_gt.DenseGTDataset.setup_zarr .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.setup_zarr .. py:method:: transform_data() :canonical: deeprvat.data.dense_gt.DenseGTDataset.transform_data .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.transform_data .. py:method:: setup_annotations(annotation_file: typing.Optional[str], annotation_aggregation: typing.Union[str, dict], precomputed_annotations: typing.Optional[typing.Tuple[pandas.DataFrame, pandas.DataFrame, numpy.ndarray]] = None) :canonical: deeprvat.data.dense_gt.DenseGTDataset.setup_annotations .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.setup_annotations .. py:method:: setup_variants(min_common_variant_count: typing.Optional[int], min_common_af: typing.Optional[typing.Dict[str, float]], train_variants: typing.Optional[pandas.DataFrame]) :canonical: deeprvat.data.dense_gt.DenseGTDataset.setup_variants .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.setup_variants .. py:method:: get_variant_metadata(grouping_level: typing.Optional[str]) :canonical: deeprvat.data.dense_gt.DenseGTDataset.get_variant_metadata .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.get_variant_metadata .. py:method:: setup_common_groups() :canonical: deeprvat.data.dense_gt.DenseGTDataset.setup_common_groups :abstractmethod: .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.setup_common_groups .. py:method:: get_variant_groups() :canonical: deeprvat.data.dense_gt.DenseGTDataset.get_variant_groups .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.get_variant_groups .. py:method:: get_common_variants(sparse_variants: numpy.ndarray, sparse_genotype: numpy.ndarray) :canonical: deeprvat.data.dense_gt.DenseGTDataset.get_common_variants .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.get_common_variants .. py:method:: get_rare_variants(idx, all_sparse_variants, sparse_genotype) :canonical: deeprvat.data.dense_gt.DenseGTDataset.get_rare_variants .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.get_rare_variants .. py:method:: collate_fn(batch: typing.Dict[str, typing.List[typing.Union[int, torch.Tensor]]]) -> typing.Dict[str, typing.Union[torch.Tensor, typing.List[str]]] :canonical: deeprvat.data.dense_gt.DenseGTDataset.collate_fn .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.collate_fn .. py:method:: get_metadata() -> typing.Dict[str, typing.Any] :canonical: deeprvat.data.dense_gt.DenseGTDataset.get_metadata .. autodoc2-docstring:: deeprvat.data.dense_gt.DenseGTDataset.get_metadata