`spateo.tools.cluster.find_clusters`#

Module Contents#

Functions#

`spagcn_pyg`(→ Optional[anndata.AnnData])	Function to find clusters with spagcn.
`scc`(→ Optional[anndata.AnnData])	Spatially constrained clustering (scc) to identify continuous tissue domains.

Attributes#

to_dense_matrix

spateo.tools.cluster.find_clusters.to_dense_matrix[source]#

spateo.tools.cluster.find_clusters.spagcn_pyg(adata: anndata.AnnData, n_clusters: int, p: float = 0.5, s: int = 1, b: int = 49, refine_shape: str | None = None, his_img_path: str | None = None, total_umi: str | None = None, x_pixel: str = None, y_pixel: str = None, x_array: str = None, y_array: str = None, seed: int = 100, copy: bool = False) → anndata.AnnData | None[source]#

Function to find clusters with 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)

Parameters:

adata: an Anndata object, after normalization.
n_clusters: Desired number of clusters.
p: parameter p in spagcn algorithm. See SpaGCN for details. Defaults to 0.5.
s: alpha to control the color scale in calculating adjacent matrix. Defaults to 1.
b: beta to control the range of neighbourhood when calculate grey value for one spot in calculating adjacent matrix. Defaults to 49.
refine_shape: Smooth the spatial domains with given spatial topology, “hexagon” for Visium data, “square” for ST data. Defaults to None.
his_img_path: The file path of histology image used to calculate adjacent matrix in spagcn algorithm. Defaults to None.
total_umi: By providing the key(colname) in adata.obs which contains total UMIs(counts) for each spot, the function use the total counts as a grayscale image when histology image is not provided. Ignored if his_img_path is not None. Defaults to “total_umi”.
x_pixel: The key(colname) in adata.obs which contains corresponding x-pixels in histology image. Defaults to None.
y_pixel: The key(colname) in adata.obs which contains corresponding y-pixels in histology image. Defaults to None.
x_array: The key(colname) in adata.obs which contains corresponding x-coordinates. Defaults to None.
y_array: The key(colname) in adata.obs which contains corresponding y-coordinates. Defaults to None.
seed: Global seed for random, torch, numpy. Defaults to 100.
copy: Whether to return a new deep copy of adata instead of updating adata object passed in arguments. Defaults to False.

Returns:

~anndata.AnnData: An ~anndata.AnnData object with cluster info in “spagcn_pred”, and in “spagcn_pred_refined” if refine_shape is set.: The adjacent matrix used in spagcn algorithm is saved in adata.uns[“adj_spagcn”].

Return type:

class

spateo.tools.cluster.find_clusters.scc(adata: anndata.AnnData, spatial_key: str = 'spatial', key_added: str | None = 'scc', pca_key: str = 'pca', e_neigh: int = 30, s_neigh: int = 6, resolution: float | None = None) → anndata.AnnData | None[source]#

Spatially constrained clustering (scc) to identify continuous tissue domains.

Reference:: Ao Chen, Sha Liao, Mengnan Cheng, Kailong Ma, Liang Wu, Yiwei Lai, Xiaojie Qiu, Jin Yang, Wenjiao Li, Jiangshan Xu, Shijie Hao, Xin Wang, Huifang Lu, Xi Chen, Xing Liu, Xin Huang, Feng Lin, Zhao Li, Yan Hong, Defeng Fu, Yujia Jiang, Jian Peng, Shuai Liu, Mengzhe Shen, Chuanyu Liu, Quanshui Li, Yue Yuan, Huiwen Zheng, Zhifeng Wang, H Xiang, L Han, B Qin, P Guo, PM Cánoves, JP Thiery, Q Wu, F Zhao, M Li, H Kuang, J Hui, O Wang, B Wang, M Ni, W Zhang, F Mu, Y Yin, H Yang, M Lisby, RJ Cornall, J Mulder, M Uhlen, MA Esteban, Y Li, L Liu, X Xu, J Wang. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell, 2022.