pegasus.umap¶
- pegasus.umap(data, rep='pca', n_components=2, n_neighbors=15, min_dist=0.5, spread=1.0, n_jobs=- 1, full_speed=False, random_state=0, out_basis='umap')[source]¶
Calculate UMAP embedding of cells.
This function uses umap-learn package. See [McInnes18] for details on UMAP.
- 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_components (
int
, optional, default:2
) – Dimension of calculated UMAP coordinates. By default, generate 2-dimensional data for 2D visualization.n_neighbors (
int
, optional, default:15
) – Number of nearest neighbors considered during the computation.min_dist (
float
, optional, default:0.5
) – The effective minimum distance between embedded data points.spread (
float
, optional, default:1.0
) – The effective scale of embedded data points.n_jobs (
int
, optional, default:-1
) – Number of threads to use for computing kNN graphs. If-1
, use all physical CPU cores.full_speed (
bool
, optional, default:False
) –If
True
, use multiple threads in constructinghnsw
index. However, the kNN results are not reproducible.Otherwise, use only one thread to make sure results are reproducible.
random_state (
int
, optional, default:0
) – Random seed set for reproducing results.out_basis (
str
, optional, default:"umap"
) – Key name for calculated UMAP coordinates to store.
- Return type
None
- Returns
None
Update
data.obsm
–data.obsm['X_' + out_basis]
: UMAP coordinates of the data.
Examples
>>> pg.umap(data)