spateo.tdr.models.models_backbone.backbone
==========================================

.. py:module:: spateo.tdr.models.models_backbone.backbone


Functions
---------

.. autoapisummary::

   spateo.tdr.models.models_backbone.backbone.construct_backbone
   spateo.tdr.models.models_backbone.backbone.update_backbone
   spateo.tdr.models.models_backbone.backbone.backbone_scc


Module Contents
---------------

.. py:function:: construct_backbone(model: Union[pyvista.PolyData, pyvista.UnstructuredGrid], spatial_key: Optional[str] = None, nodes_key: str = 'nodes', rd_method: Literal['ElPiGraph', 'SimplePPT', 'PrinCurve'] = 'ElPiGraph', num_nodes: int = 50, color: str = 'gainsboro', **kwargs) -> Tuple[pyvista.PolyData, float, Optional[str]]

   Organ's backbone construction based on 3D point cloud model.

   :param model: A point cloud model.
   :param spatial_key: If spatial_key is None, the spatial coordinates are in model.points, otherwise in model[spatial_key].
   :param nodes_key: The key that corresponds to the coordinates of the nodes in the backbone.
   :param 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 principal
                                        component analysis algorithms that work to optimize a line over an entire dataset.
   :param num_nodes: Number of nodes for the backbone model.
   :param color: Color to use for plotting backbone model.
   :param \*\*kwargs: Additional parameters that will be passed to ``ElPiGraph_method``, ``SimplePPT_method`` or ``PrinCurve_method`` function.

   :returns: A three-dims backbone model.
             backbone_length: The length of the backbone model.
             plot_cmap: Recommended colormap parameter values for plotting.
   :rtype: backbone_model


.. py:function:: update_backbone(backbone: pyvista.PolyData, nodes_key: str = 'nodes', key_added: str = 'updated_nodes', select_nodes: Optional[Union[list, numpy.ndarray]] = None, interactive: Optional[bool] = True, model_size: Union[float, list] = 8.0, colormap: str = 'Spectral') -> Union[pyvista.PolyData, pyvista.UnstructuredGrid]

   Update the bakcbone through interaction or input of selected nodes.

   :param backbone: The backbone model.
   :param nodes_key: The key that corresponds to the coordinates of the nodes in the backbone.
   :param key_added: The key under which to add the labels of new nodes.
   :param select_nodes: Nodes that need to be retained.
   :param interactive: Whether to delete useless nodes interactively. When ``interactive`` is True, ``select_nodes`` is invalid.
   :param model_size: Thickness of backbone. When ``interactive`` is False, ``model_size`` is invalid.
   :param colormap: Colormap of backbone. When ``interactive`` is False, ``colormap`` is invalid.

   :returns: The updated backbone model.
   :rtype: updated_backbone


.. py:function:: backbone_scc(adata: anndata.AnnData, backbone: pyvista.PolyData, genes: Optional[list] = None, adata_nodes_key: str = 'backbone_nodes', backbone_nodes_key: str = 'updated_nodes', key_added: Optional[str] = 'backbone_scc', layer: Optional[str] = None, e_neigh: int = 10, s_neigh: int = 6, cluster_method: Literal['leiden', 'louvain'] = 'leiden', resolution: Optional[float] = None, inplace: bool = True) -> Optional[anndata.AnnData]

   Spatially constrained clustering (scc) along the backbone.

   :param adata: The anndata object.
   :param backbone: The backbone model.
   :param genes: The list of genes that will be used to subset the data for clustering. If ``genes = None``, all genes will be used.
   :param adata_nodes_key: The key that corresponds to the nodes in the adata.
   :param backbone_nodes_key: The key that corresponds to the nodes in the backbone.
   :param key_added: adata.obs key under which to add the cluster labels.
   :param layer: The layer that will be used to retrieve data for dimension reduction and clustering. If ``layer = None``, ``.X`` is used.
   :param e_neigh: the number of nearest neighbor in gene expression space.
   :param s_neigh: the number of nearest neighbor in physical space.
   :param cluster_method: the method that will be used to cluster the cells.
   :param resolution: the resolution parameter of the louvain clustering algorithm.
   :param inplace: Whether to copy adata or modify it inplace.

   :returns: An ``AnnData`` object is updated/copied with the ``key_added`` in the ``.obs`` attribute, storing the clustering results.