spateo.tdr.widgets.interpolations#
Module Contents#
Functions#
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Learn a continuous mapping from space to gene expression pattern with the Kernel method (sparseVFC). |
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Learn a continuous mapping from space to gene expression pattern with the deep neural net model. |
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Prepare the X (spatial coordinates), Y (gene expression) and grid points for the kernel or deep model. |
- spateo.tdr.widgets.interpolations.kernel_interpolation(adata: Optional[anndata.AnnData] = None, genes: Optional[List] = None, X: Optional[numpy.ndarray] = None, Y: Optional[numpy.ndarray] = None, NX: Optional[numpy.ndarray] = None, grid_num: List = [50, 50, 50], lambda_: float = 0.02, lstsq_method: str = 'scipy', **kwargs) anndata.AnnData[source]#
Learn a continuous mapping from space to gene expression pattern with the Kernel method (sparseVFC).
- Parameters
- adata
AnnData object that contains spatial (numpy.ndarray) in the obsm attribute.
- genes
Gene list whose interpolate expression across space needs to learned. If Y is provided, genes will only be used to retrive the gene annotation info.
- X
The spatial coordinates of each data point.
- Y
The gene expression of the corresponding data point.
- NX
The spatial coordinates of new data point. If NX is None, generate new points based on grid_num.
- grid_num
Number of grid to generate. Default is 50 for each dimension. Must be non-negative.
- 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. The row of the adata object is a grid point within the convex hull formed by the input data points while each column corresponds a gene whose expression values are interpolated.
- Return type
interp_adata
- spateo.tdr.widgets.interpolations.deep_intepretation(adata: Optional[anndata.AnnData] = None, genes: Optional[List] = None, X: Optional[numpy.ndarray] = None, Y: Optional[numpy.ndarray] = None, NX: Optional[numpy.ndarray] = None, grid_num: List = [50, 50, 50], **kwargs) anndata.AnnData[source]#
Learn a continuous mapping from space to gene expression pattern with the deep neural net model.
- Parameters
- adata
AnnData object that contains spatial (numpy.ndarray) in the obsm attribute.
- genes
Gene list whose interpolate expression across space needs to learned. If Y is provided, genes will only be used to retrive the gene annotation info.
- X
The spatial coordinates of each data point.
- Y
The gene expression of the corresponding data point.
- NX
The spatial coordinates of new data point. If NX is None, generate new points based on grid_num.
- grid_num
Number of grid to generate. Default is 50 for each dimension. Must be non-negative.
- **kwargs
Additional parameters that will be passed to the training step of the deep neural net.
- Returns
an anndata object that has interpolated expression. The row of the adata object is a grid point within the convex hull formed by the input data points while each column corresponds a gene whose expression values are interpolated.
- Return type
interp_adata
- spateo.tdr.widgets.interpolations.get_X_Y_grid(adata: Optional[anndata.AnnData] = None, genes: Optional[List] = None, X: Optional[numpy.ndarray] = None, Y: Optional[numpy.ndarray] = None, grid_num: List = [50, 50, 50]) Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray][source]#
Prepare the X (spatial coordinates), Y (gene expression) and grid points for the kernel or deep model.
- Parameters
- adata
AnnData object that contains spatial (numpy.ndarray) in the obsm attribute.
- genes
Gene list whose interpolate expression across space needs to learned. If Y is provided, genes will only be used to retrive the gene annotation info.
- X
The spatial coordinates of each data point.
- Y
The gene expression of the corresponding data point.
- grid_num
Number of grid to generate. Default is 50 for each dimension. Must be non-negative.
- Returns
spatial coordinates. Y: gene expression of the associated spatial coordinates. Grid: grid points formed with the input spatial coordinates. grid_in_hull: A list of booleans indicates whether the current grid points is within the convex hull formed by
the input data points.
- Return type
X