spateo.tdr.interpolations.interpolation_gp¶
Classes¶
Functions¶
|
Learn a continuous mapping from space to gene expression pattern with the Gaussian Process method. |
Module Contents¶
- class spateo.tdr.interpolations.interpolation_gp.Imputation_GPR(source_adata: anndata.AnnData, target_points: numpy.ndarray | None = None, keys: str | list = None, spatial_key: str = 'spatial', layer: str = 'X', device: str = 'cpu', method: Literal['SVGP', 'ExactGP'] = 'SVGP', batch_size: int = 1024, shuffle: bool = True, inducing_num: int = 512, normalize_spatial: bool = True)[source]¶
-
- normalize_coords(data: numpy.ndarray | torch.Tensor, given_normalize: bool = False)[source]¶
- spateo.tdr.interpolations.interpolation_gp.gp_interpolation(source_adata: anndata.AnnData, target_points: numpy.ndarray | None = None, keys: str | list = None, spatial_key: str = 'spatial', layer: str = 'X', training_iter: int = 50, device: str = 'cpu', method: Literal['SVGP', 'ExactGP'] = 'SVGP', batch_size: int = 1024, shuffle: bool = True, inducing_num: int = 512) anndata.AnnData [source]¶
Learn a continuous mapping from space to gene expression pattern with the Gaussian Process method.
- Parameters:
- source_adata
AnnData object that contains spatial (numpy.ndarray) in the obsm attribute.
- target_points
The spatial coordinates of new data point. If target_coords is None, generate new points based on grid_num.
- keys
Gene list or info list in the obs attribute whose interpolate expression across space needs to learned.
- spatial_key
The key in
.obsm
that corresponds to the spatial coordinate of each bucket.- layer
If
'X'
, uses.X
, otherwise uses the representation given by.layers[layer]
.- training_iter
Max number of iterations for training.
- device
Equipment used to run the program. You can also set the specified GPU for running.
E.g.: '0'
.
- Returns:
an anndata object that has interpolated expression.
- Return type:
interp_adata