View source: R/reduce_dimensions.R
reduce_dimension  R Documentation 
Monocle3 aims to learn how cells transition through a
biological program of gene expression changes in an experiment. Each cell
can be viewed as a point in a highdimensional space, where each dimension
describes the expression of a different gene. Identifying the program of
gene expression changes is equivalent to learning a trajectory that
the cells follow through this space. However, the more dimensions there are
in the analysis, the harder the trajectory is to learn. Fortunately, many
genes typically covary with one another, and so the dimensionality of the
data can be reduced with a wide variety of different algorithms. Monocle3
provides two different algorithms for dimensionality reduction via
reduce_dimension
(UMAP and tSNE). The function
reduce_dimension
is the second step in the trajectory building
process after preprocess_cds
.
UMAP is implemented from the package uwot.
reduce_dimension(
cds,
max_components = 2,
reduction_method = c("UMAP", "tSNE", "PCA", "LSI", "Aligned"),
preprocess_method = NULL,
umap.metric = "cosine",
umap.min_dist = 0.1,
umap.n_neighbors = 15L,
umap.fast_sgd = FALSE,
umap.nn_method = "annoy",
verbose = FALSE,
cores = 1,
build_nn_index = FALSE,
nn_control = list(),
...
)
cds 
the cell_data_set upon which to perform this operation. 
max_components 
the dimensionality of the reduced space. Default is 2. 
reduction_method 
A character string specifying the algorithm to use for dimensionality reduction. Currently "UMAP", "tSNE", "PCA", "LSI", and "Aligned" are supported. 
preprocess_method 
A string indicating the preprocessing method used on the data. Options are "PCA" and "LSI". Default is "LSI". 
umap.metric 
A string indicating the distance metric to be used when
calculating UMAP. Default is "cosine". See uwot package's

umap.min_dist 
Numeric indicating the minimum distance to be passed to
UMAP function. Default is 0.1.See uwot package's 
umap.n_neighbors 
Integer indicating the number of neighbors to use
during kNN graph construction. Default is 15L. See uwot package's

umap.fast_sgd 
Logical indicating whether to use fast SGD. Default is
TRUE. See uwot package's 
umap.nn_method 
String indicating the nearest neighbor method to be
used by UMAP. Default is "annoy". See uwot package's

verbose 
Logical, whether to emit verbose output. 
cores 
Number of cores to use for computing the UMAP. 
build_nn_index 
logical When this argument is set to TRUE, preprocess_cds builds the nearest neighbor index from the reduced dimension matrix for later use. Default is FALSE. 
nn_control 
An optional list of parameters used to make the nearest neighbor index. See the set_nn_control help for detailed information. The default metric is cosine for reduction_methods PCA, LSI, and Aligned, and is euclidean for reduction_methods tSNE and UMAP. Note: distances in tSNE space reflect spatial differences poorly so using nearest neighbors with it may be meaningless. 
... 
additional arguments to pass to the dimensionality reduction function. 
an updated cell_data_set object
UMAP: McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv eprints 1802.03426, 2018
tSNE: Laurens van der Maaten and Geoffrey Hinton. Visualizing data using tSNE. J. Mach. Learn. Res., 9(Nov):2579– 2605, 2008.
cell_metadata < readRDS(system.file('extdata',
'worm_embryo/worm_embryo_coldata.rds',
package='monocle3'))
gene_metadata < readRDS(system.file('extdata',
'worm_embryo/worm_embryo_rowdata.rds',
package='monocle3'))
expression_matrix < readRDS(system.file('extdata',
'worm_embryo/worm_embryo_expression_matrix.rds',
package='monocle3'))
cds < new_cell_data_set(expression_data=expression_matrix,
cell_metadata=cell_metadata,
gene_metadata=gene_metadata)
cds < preprocess_cds(cds)
cds < reduce_dimension(cds)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.