rapids_singlecell.tl.tsne#
- rapids_singlecell.tl.tsne(adata, n_pcs=None, *, use_rep=None, perplexity=30, early_exaggeration=12, learning_rate=200, method='barnes_hut', metric='euclidean', copy=False)[source]#
Performs t-distributed stochastic neighborhood embedding (tSNE) using cuml library.
- Parameters:
- adata
AnnData
Annotated data matrix.
- n_pcs
int
(default:None
) Use this many PCs. If
n_pcs==0
use.X
ifuse_rep is None
.- use_rep
str
(default:None
) Use the indicated representation.
'X'
or any key for.obsm
is valid. If None, the representation is chosen automatically: For .n_vars < 50, .X is used, otherwise'X_pca'
is used. If'X_pca'
is not present, it’s computed with default parameters orn_pcs
if present.- perplexity
int
(default:30
) The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50. The choice is not extremely critical since t-SNE is quite insensitive to this parameter.
- early_exaggeration
int
(default:12
) Controls how tight natural clusters in the original space are in the embedded space and how much space will be between them. For larger values, the space between natural clusters will be larger in the embedded space. Again, the choice of this parameter is not very critical. If the cost function increases during initial optimization, the early exaggeration factor or the learning rate might be too high.
- learning_rate
int
(default:200
) Note that the R-package “Rtsne” and cuML uses a default of 200. The learning rate can be a critical parameter. It should be between 100 and 1000. If the cost function increases during initial optimization, the early exaggeration factor or the learning rate might be too high. If the cost function gets stuck in a bad local minimum increasing the learning rate helps sometimes.
- method
str
(default:'barnes_hut'
) ‘barnes_hut’ and ‘fft’ are fast approximations. ‘exact’ is more accurate but slower.
- metric
str
(default:'euclidean'
) Distance metric to use. Supported distances are [‘l1, ‘cityblock’, ‘manhattan’, ‘euclidean’, ‘l2’, ‘sqeuclidean’, ‘minkowski’, ‘chebyshev’, ‘cosine’, ‘correlation’]
- copy
bool
(default:False
) Return a copy instead of writing to adata.
- adata
- Return type:
- Returns:
Depending on
copy
, returns or updatesadata
with the following fields.- X_tsne
np.ndarray
(adata.obsm
, dtypefloat
) tSNE coordinates of data.
- X_tsne