stlearn.tl.clustering.kmeans

stlearn.tl.clustering.kmeans(adata: AnnData, n_clusters: int = 20, use_data: str = 'X_pca', init: str = 'k-means++', n_init: int = 10, max_iter: int = 300, tol: float = 0.0001, random_state: int | RandomState | None = None, copy_x: bool = True, algorithm: str = 'lloyd', key_added: str = 'kmeans', copy: bool = False) → AnnData | None[source]

Perform kmeans cluster for spatial transcriptomics data

Parameters:

  • adata (AnnData) – Annotated data matrix.

  • n_clusters (int, default = 20) – The number of clusters to form as well as the number of centroids to generate.

  • use_data (str, default = "X_pca") – Use dimensionality reduction result.

  • init (str, default = "k-means++") – Method for initialization, defaults to ‘k-means++’.

  • n_init (int, default = 10) – Number of time the k-means algorithm will be run with different centroid seeds.

  • max_iter (int, default = 300) – Maximum number of iterations of the k-means algorithm for a single run.

  • tol (float, default = 0.0001) – Relative tolerance with regard to inertia to declare convergence.

  • random_state (int | np.random.RandomState | None, default = None) – Determines random number generation for centroid initialization. Use an int to make the randomness deterministic.

  • copy_x (bool, default = True) – When pre-computing distances it is more numerically accurate to center the data first. If copy_x is True (default), then the original data is not modified, ensuring X is C-contiguous. If False, the original data is modified, and put back before the function returns, but small numerical differences may be introduced by subtracting and then adding the data mean, in this case it will also not ensure that data is C-contiguous which may cause a significant slowdown.

  • algorithm (str, default = "lloyd") – K-means algorithm to use. The classical EM-style algorithm is “lloyd”. The “elkan” variation can be more efficient on some datasets with well-defined clusters, by using the triangle inequality.

  • key_added (str, default = "kmeans") – Key add to adata.obs

  • copy (bool, default = False) – Return a copy instead of writing to adata.

Return type:

Anndata