pegasus.tsne¶
- pegasus.tsne(data, rep='pca', n_jobs=- 1, n_components=2, perplexity=30, early_exaggeration=12, learning_rate='auto', initialization='pca', random_state=0, out_basis='tsne')[source]¶
Calculate t-SNE embedding of cells using the FIt-SNE package.
This function uses fitsne package. See [Linderman19] for details on FIt-SNE algorithm.
- Parameters
data (
pegasusio.MultimodalData
) – Annotated data matrix with rows for cells and columns for genes.rep (
str
, optional, default:"pca"
) – Representation of data used for the calculation. By default, use PCA coordinates. IfNone
, use the count matrixdata.X
.n_jobs (
int
, optional, default:-1
) – Number of threads to use. If-1
, use all physical CPU cores.n_components (
int
, optional, default:2
) – Dimension of calculated FI-tSNE coordinates. By default, generate 2-dimensional data for 2D visualization.perplexity (
float
, optional, default:30
) – The perplexity is related to the number of nearest neighbors used in other manifold learning algorithms. Larger datasets usually require a larger perplexity.early_exaggeration (
int
, optional, default:12
) – Controls how tight natural clusters in the original space are in the embedded space, and how much space will be between them.learning_rate (
float
, optional, default:auto
) – By default, the learning rate is determined automatically as max(data.shape[0] / early_exaggeration, 200). See [Belkina19] and [Kobak19] for details.initialization (
str
, optional, default:pca
) – Initialization can be eitherpca
orrandom
or np.ndarray. By default, we usepca
initialization according to [Kobak19].random_state (
int
, optional, default:0
) – Random seed set for reproducing results.out_basis (
str
, optional, default:"fitsne"
) – Key name for calculated FI-tSNE coordinates to store.
- Return type
None
- Returns
None
Update
data.obsm
–data.obsm['X_' + out_basis]
: FI-tSNE coordinates of the data.
Examples
>>> pg.tsne(data)