Source code for stlearn.embedding.umap

from typing import Optional, Union

import numpy as np
from anndata import AnnData
from numpy.random.mtrand import RandomState

from .._compat import Literal
import scanpy

_InitPos = Literal["paga", "spectral", "random"]

[docs]def run_umap( adata: AnnData, min_dist: float = 0.5, spread: float = 1.0, n_components: int = 2, maxiter: Optional[int] = None, alpha: float = 1.0, gamma: float = 1.0, negative_sample_rate: int = 5, init_pos: Union[_InitPos, np.ndarray, None] = "spectral", random_state: Optional[Union[int, RandomState]] = 0, a: Optional[float] = None, b: Optional[float] = None, copy: bool = False, method: Literal["umap", "rapids"] = "umap", ) -> Optional[AnnData]: """\ Wrap function scanpy.pp.umap Embed the neighborhood graph using UMAP [McInnes18]_. UMAP (Uniform Manifold Approximation and Projection) is a manifold learning technique suitable for visualizing high-dimensional data. Besides tending to be faster than tSNE, it optimizes the embedding such that it best reflects the topology of the data, which we represent throughout Scanpy using a neighborhood graph. tSNE, by contrast, optimizes the distribution of nearest-neighbor distances in the embedding such that these best match the distribution of distances in the high-dimensional space. We use the implementation of `umap-learn <>`__ [McInnes18]_. For a few comparisons of UMAP with tSNE, see this `preprint <>`__. Parameters ---------- adata Annotated data matrix. n_components The number of dimensions of the embedding. random_state If `int`, `random_state` is the seed used by the random number generator; If `RandomState`, `random_state` is the random number generator; If `None`, the random number generator is the `RandomState` instance used by `np.random`. Returns ------- Depending on `copy`, returns or updates `adata` with the following fields. `X_umap` : :class:`numpy.ndarray` (`adata.obsm`) Independent Component Analysis representation of data. """ adata, min_dist=min_dist, spread=spread, n_components=n_components, maxiter=maxiter, alpha=alpha, gamma=gamma, negative_sample_rate=negative_sample_rate, init_pos=init_pos, random_state=random_state, a=a, b=b, copy=copy, method=method, ) print("UMAP is done! Generated in adata.obsm['X_umap'] nad adata.uns['umap']")