spateo.tdr.interpolations.interpolation_sparseVFC¶
Functions¶
|
Learn a continuous mapping from space to gene expression pattern with Kernel method (sparseVFC). |
Module Contents¶
- spateo.tdr.interpolations.interpolation_sparseVFC.kernel_interpolation(source_adata: anndata.AnnData, target_points: numpy.ndarray | None = None, keys: str | list = None, spatial_key: str = 'spatial', layer: str = 'X', lambda_: float = 0.02, lstsq_method: str = 'scipy', **kwargs) anndata.AnnData [source]¶
Learn a continuous mapping from space to gene expression pattern with Kernel method (sparseVFC).
- 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]
.- lambda
Represents the trade-off between the goodness of data fit and regularization. Larger Lambda_ put more weights on regularization.
- lstsq_method
The name of the linear least square solver, can be either ‘scipy` or douin.
- **kwargs
Additional parameters that will be passed to SparseVFC function.
- Returns:
an anndata object that has interpolated expression.
- Return type:
interp_adata