pegasus.net_umap¶
-
pegasus.
net_umap
(data, rep='pca', n_jobs=- 1, n_components=2, n_neighbors=15, min_dist=0.5, spread=1.0, random_state=0, select_frac=0.1, select_K=25, select_alpha=1.0, full_speed=False, net_alpha=0.1, polish_learning_rate=10.0, polish_n_epochs=30, out_basis='net_umap')[source]¶ Calculate approximated UMAP embedding using Deep Learning model to improve the speed.
In specific, the deep model used is MLPRegressor, the scikit-learn implementation of Multi-layer Perceptron regressor.
- Parameters
data (
anndata.AnnData
) – 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.random_state (
int
, optional, default:0
) – Random seed set for reproducing results.select_frac (
float
, optional, default:0.1
) – Down sampling fraction on the cells.select_K (
int
, optional, default:25
) – Number of neighbors to be used to estimate local density for each data point for down sampling.select_alpha (
float
, optional, default:1.0
) – Weight the down sample to be proportional toradius ** select_alpha
.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.
net_alpha (
float
, optional, default:0.1
) – L2 penalty (regularization term) parameter of the deep regressor.polish_learning_frac (
float
, optional, default:10.0
) – After running the deep regressor to predict new coordinates, usepolish_learning_frac
*n_obs
as the learning rate to polish the coordinates.polish_n_iter (
int
, optional, default:30
) – Number of iterations for polishing UMAP run.out_basis (
str
, optional, default:"net_umap"
) – Key name for calculated UMAP coordinates to store.
- Return type
None
- Returns
None
Update
data.obsm
–data.obsm['X_' + out_basis]
: Net UMAP coordinates of the data.
Update
data.obs
–data.obs['ds_selected']
: Boolean array to indicate which cells are selected during the down sampling phase.
Examples
>>> pg.net_umap(adata)