spateo.tdr.models.models_backbone.backbone
#
Module Contents#
Functions#
|
Organ's backbone construction based on 3D point cloud model. |
|
Update the bakcbone through interaction or input of selected nodes. |
|
Spatially constrained clustering (scc) along the backbone. |
- spateo.tdr.models.models_backbone.backbone.construct_backbone(model: pyvista.PolyData | pyvista.UnstructuredGrid, spatial_key: str | None = None, nodes_key: str = 'nodes', rd_method: Literal[ElPiGraph, SimplePPT, PrinCurve] = 'ElPiGraph', num_nodes: int = 50, color: str = 'gainsboro', **kwargs) Tuple[pyvista.PolyData, float, str | None] [source]#
Organ’s backbone construction based on 3D point cloud model.
- Parameters:
- model
A point cloud model.
- spatial_key
If spatial_key is None, the spatial coordinates are in model.points, otherwise in model[spatial_key].
- nodes_key
The key that corresponds to the coordinates of the nodes in the backbone.
- rd_method
The method of constructing a backbone model. Available
rd_method
are:'ElPiGraph'
: Generate a principal elastic tree.'SimplePPT'
: Generate a simple principal tree.'PrinCurve'
: This is the global module that contains principal curve and nonlinear principalcomponent analysis algorithms that work to optimize a line over an entire dataset.
- num_nodes
Number of nodes for the backbone model.
- color
Color to use for plotting backbone model.
- **kwargs
Additional parameters that will be passed to
ElPiGraph_method
,SimplePPT_method
orPrinCurve_method
function.
- Returns:
A three-dims backbone model. backbone_length: The length of the backbone model. plot_cmap: Recommended colormap parameter values for plotting.
- Return type:
backbone_model
- spateo.tdr.models.models_backbone.backbone.update_backbone(backbone: pyvista.PolyData, nodes_key: str = 'nodes', key_added: str = 'updated_nodes', select_nodes: list | numpy.ndarray | None = None, interactive: bool | None = True, model_size: float | list = 8.0, colormap: str = 'Spectral') pyvista.PolyData | pyvista.UnstructuredGrid [source]#
Update the bakcbone through interaction or input of selected nodes.
- Parameters:
- backbone
The backbone model.
- nodes_key
The key that corresponds to the coordinates of the nodes in the backbone.
- key_added
The key under which to add the labels of new nodes.
- select_nodes
Nodes that need to be retained.
- interactive
Whether to delete useless nodes interactively. When
interactive
is True,select_nodes
is invalid.- model_size
Thickness of backbone. When
interactive
is False,model_size
is invalid.- colormap
Colormap of backbone. When
interactive
is False,colormap
is invalid.
- Returns:
The updated backbone model.
- Return type:
updated_backbone
- spateo.tdr.models.models_backbone.backbone.backbone_scc(adata: anndata.AnnData, backbone: pyvista.PolyData, genes: list | None = None, adata_nodes_key: str = 'backbone_nodes', backbone_nodes_key: str = 'updated_nodes', key_added: str | None = 'backbone_scc', layer: str | None = None, e_neigh: int = 10, s_neigh: int = 6, cluster_method: Literal[leiden, louvain] = 'leiden', resolution: float | None = None, inplace: bool = True) anndata.AnnData | None [source]#
Spatially constrained clustering (scc) along the backbone.
- Parameters:
- adata
The anndata object.
- backbone
The backbone model.
- genes
The list of genes that will be used to subset the data for clustering. If
genes = None
, all genes will be used.- adata_nodes_key
The key that corresponds to the nodes in the adata.
- backbone_nodes_key
The key that corresponds to the nodes in the backbone.
- key_added
adata.obs key under which to add the cluster labels.
- layer
The layer that will be used to retrieve data for dimension reduction and clustering. If
layer = None
,.X
is used.- e_neigh
the number of nearest neighbor in gene expression space.
- s_neigh
the number of nearest neighbor in physical space.
- cluster_method
the method that will be used to cluster the cells.
- resolution
the resolution parameter of the louvain clustering algorithm.
- inplace
Whether to copy adata or modify it inplace.
- Returns:
An
AnnData
object is updated/copied with thekey_added
in the.obs
attribute, storing the clustering results.