spateo.tools.cluster.cluster_spagcn
===================================

.. py:module:: spateo.tools.cluster.cluster_spagcn


Functions
---------

.. autoapisummary::

   spateo.tools.cluster.cluster_spagcn.spagcn_vanilla


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

.. py:function:: spagcn_vanilla(adata: anndata.AnnData, spatial_key: str = 'spatial', key_added: Optional[str] = 'spagcn_pred', n_pca_components: Optional[int] = None, e_neigh: int = 10, resolution: float = 0.4, n_clusters: Optional[int] = None, refine_shape: Literal['hexagon', 'square'] = 'hexagon', p: float = 0.5, seed: int = 100, numIterMaxSpa: int = 2000, copy: bool = False) -> Optional[anndata.AnnData]

   Integrating gene expression and spatial location to identify spatial domains via SpaGCN.
   Original Code Repository: https://github.com/jianhuupenn/SpaGCN

   Reference:
       Jian Hu, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J. Irwin, Edward B. Lee,
       Russell T. Shinohara & Mingyao Li. SpaGCN: Integrating gene expression, spatial location and histology to
       identify spatial domains and spatially variable genes by graph convolutional network. Nature Methods volume 18,
       pages1342–1351 (2021)

   :param adata: An Anndata object after normalization.
   :param spatial_key: the key in `.obsm` that corresponds to the spatial coordinate of each bucket.
   :param key_added: adata.obs key under which to add the cluster labels.
                     The initial clustering results of SpaGCN are under `key_added`,
                     and the refined clustering results are under `f'{key_added}_refined'`.
   :param n_pca_components: Number of principal components to compute.
                            If `n_pca_components` == None, the value at the inflection point of the PCA curve is
                            automatically calculated as n_comps.
   :param e_neigh: Number of nearest neighbor in gene expression space.
                   Used in dyn.pp.neighbors(adata, n_neighbors=e_neigh).
   :param resolution: Resolution in the Louvain clustering method. Used when `n_clusters`==None.
   :param n_clusters: Number of spatial domains wanted.
                      If `n_clusters` != None, the suitable resolution in the initial Louvain clustering method
                      will be automatically searched based on n_clusters.
   :param refine_shape: Smooth the spatial domains with given spatial topology, "hexagon" for Visium data, "square" for ST
                        data. Defaults to None.
   :param p: Percentage of total expression contributed by neighborhoods.
   :param seed: Global seed for `random`, `torch`, `numpy`. Defaults to 100.
   :param numIterMaxSpa: SpaGCN maximum number of training iterations.
   :param copy: Whether to copy `adata` or modify it inplace.

   :returns: Depending on the parameter `copy`, when True return an updates adata with the field ``adata.obs[key_added]`` and
             ``adata.obs[f'{key_added}_refined']``, containing the cluster result based on SpaGCN; else inplace update the
             adata object.