runUMAP
¶
runUMAP
Description¶
run UMAP
Usage¶
runUMAP(
gobject,
expression_values = c("normalized", "scaled", "custom"),
reduction = c("cells", "genes"),
dim_reduction_to_use = "pca",
dim_reduction_name = "pca",
dimensions_to_use = 1:10,
name = "umap",
genes_to_use = NULL,
return_gobject = TRUE,
n_neighbors = 40,
n_components = 2,
n_epochs = 400,
min_dist = 0.01,
n_threads = NA,
spread = 5,
set_seed = TRUE,
seed_number = 1234,
verbose = T,
...
)
Arguments¶
Argument |
Description |
---|---|
|
giotto object |
|
expression values to use |
|
cells or genes |
|
use another dimension reduction set as input |
|
name of dimension reduction set to use |
|
number of dimensions to use as input |
|
arbitrary name for UMAP run |
|
if dim_reduction_to_use = NULL, which genes to use |
|
boolean: return giotto object (default = TRUE) |
|
UMAP param: number of neighbors |
|
UMAP param: number of components |
|
UMAP param: number of epochs |
|
UMAP param: minimum distance |
|
UMAP param: threads/cores to use |
|
UMAP param: spread |
|
use of seed |
|
seed number to use |
|
verbosity of function |
|
additional UMAP parameters |
Details¶
See ``umap` <#umap>`_ for more information about these and other parameters.
Input for UMAP dimension reduction can be another dimension reduction (default = ‘pca’)
To use gene expression as input set dim_reduction_to_use = NULL
If dim_reduction_to_use = NULL, genes_to_use can be used to select a column name of highly variable genes (see ``calculateHVG` <#calculatehvg>`_ ) or simply provide a vector of genes
multiple UMAP results can be stored by changing the name of the analysis
Value¶
giotto object with updated UMAP dimension recuction
Examples¶
data(mini_giotto_single_cell)
mini_giotto_single_cell <- runUMAP(mini_giotto_single_cell,
dimensions_to_use = 1:3,
n_threads = 1,
n_neighbors = 3)
plotUMAP(gobject = mini_giotto_single_cell)