stlearn.tl.clustering.kmeans

stlearn.tl.clustering.kmeans(adata: anndata._core.anndata.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, precompute_distances: str = 'auto', random_state: str = None, copy_x: bool = True, n_jobs: int = None, algorithm: str = 'auto', key_added: str = 'kmeans', copy: bool = False) → Optional[anndata._core.anndata.AnnData]

Perform kmeans clustering for spatial transcriptomics data

Parameters

  • adata – Annotated data matrix.

  • n_clusters – The number of clusters to form as well as the number of centroids to generate.

  • use_data – Use dimensionality reduction result.

  • init – Method for initialization, defaults to ‘k-means++’

  • max_iter – Maximum number of iterations of the k-means algorithm for a single run.

  • tol – Relative tolerance with regards to inertia to declare convergence.

  • precompute_distances – Precompute distances (faster but takes more memory).

  • random_state – Determines random number generation for centroid initialization. Use an int to make the randomness deterministic.

  • copy_x – 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.

  • n_jobs – The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.

  • algorithm – K-means algorithm to use. The classical EM-style algorithm is “full”. The “elkan” variation is more efficient by using the triangle inequality, but currently doesn’t support sparse data. “auto” chooses “elkan” for dense data and “full” for sparse data.

  • key_added – Key add to adata.obs

  • copy – Return a copy instead of writing to adata.

Returns

Return type

Anndata