Cellcharter

cluster_cellcharter(adata, batch_key, n_clusters='auto', spatial_key='spatial', cluster_field='cluster', layer=None, random_seed=42, n_nodes_hidden_layers=32, dim_latent_layers=10, n_hidden_layers=5, gene_likelihood_model='poisson', latent_distribution='normal', save_model=None, use_model=None, inplace=True)

Perform clustering analysis on spatial transcriptomics data using the Cellcharter algorithm.

Parameters:

• adata – AnnData object containing gene expression data and spatial coordinates

• batch_key – Column name for batch information

• n_clusters – Number of clusters, can be an integer or a tuple specifying range, default is ‘auto’

• spatial_key – Key name for spatial coordinates, default is ‘spatial’

• cluster_field – Column name to store clustering results, default is ‘cluster’

• layer – Data layer to use, default is None

• random_seed – Random seed, default is 42

• n_nodes_hidden_layers – Number of nodes in hidden layers, default is 32

• dim_latent_layers – Dimension of latent space, default is 10

• n_hidden_layers – Number of hidden layers, default is 5

• gene_likelihood_model – Gene likelihood model, default is ‘poisson’

• latent_distribution – Type of latent distribution, default is ‘normal’

• save_model – Whether to save model, can be boolean or string path, default is None

• use_model – Whether to use existing model, can be boolean or string path, default is None

• inplace – Whether to modify the original data, default is True

Returns:

If inplace is False, returns the processed AnnData object; otherwise returns None

Example usage:

import spot as sp
adata = sp.cluster_cellcharter(adata, batch_key='batch', n_clusters=12, save_model='cellcharter_model.pkl')

 palette = ['turquoise', 'lightcoral', 'steelblue', 'darkslateblue', 'teal', 'cornflowerblue', 'wheat', 'lightpink']
 sns.scatterplot(x=adata.obsm['spatial'][:, 0], y=adata.obsm['spatial'][:, 1], s=8,
             hue=adata.obs.cluster, palette=palette, alpha=0.3, legend=False, edgecolor='none')