Runs the Uniform Manifold Approximation and Projection (UMAP) dimensional
reduction technique. To run, you must first install the umaplearn python
package (e.g. via pip install umaplearn
). Details on this package can be
found here: https://github.com/lmcinnes/umap. For a more in depth
discussion of the mathematics underlying UMAP, see the ArXiv paper here:
https://arxiv.org/abs/1802.03426.
RunUMAP(object, ...) # S3 method for default RunUMAP( object, reduction.key = "UMAP_", assay = NULL, reduction.model = NULL, return.model = FALSE, umap.method = "uwot", n.neighbors = 30L, n.components = 2L, metric = "cosine", n.epochs = NULL, learning.rate = 1, min.dist = 0.3, spread = 1, set.op.mix.ratio = 1, local.connectivity = 1L, repulsion.strength = 1, negative.sample.rate = 5, a = NULL, b = NULL, uwot.sgd = FALSE, seed.use = 42, metric.kwds = NULL, angular.rp.forest = FALSE, densmap = FALSE, dens.lambda = 2, dens.frac = 0.3, dens.var.shift = 0.1, verbose = TRUE, ... ) # S3 method for Graph RunUMAP( object, assay = NULL, umap.method = "umaplearn", n.components = 2L, metric = "correlation", n.epochs = 0L, learning.rate = 1, min.dist = 0.3, spread = 1, repulsion.strength = 1, negative.sample.rate = 5L, a = NULL, b = NULL, uwot.sgd = FALSE, seed.use = 42L, metric.kwds = NULL, densmap = FALSE, densmap.kwds = NULL, verbose = TRUE, reduction.key = "UMAP_", ... ) # S3 method for Neighbor RunUMAP(object, reduction.model, ...) # S3 method for Seurat RunUMAP( object, dims = NULL, reduction = "pca", features = NULL, graph = NULL, assay = DefaultAssay(object = object), nn.name = NULL, slot = "data", umap.method = "uwot", reduction.model = NULL, return.model = FALSE, n.neighbors = 30L, n.components = 2L, metric = "cosine", n.epochs = NULL, learning.rate = 1, min.dist = 0.3, spread = 1, set.op.mix.ratio = 1, local.connectivity = 1L, repulsion.strength = 1, negative.sample.rate = 5L, a = NULL, b = NULL, uwot.sgd = FALSE, seed.use = 42L, metric.kwds = NULL, angular.rp.forest = FALSE, densmap = FALSE, dens.lambda = 2, dens.frac = 0.3, dens.var.shift = 0.1, verbose = TRUE, reduction.name = "umap", reduction.key = "UMAP_", ... )
object  An object 

...  Arguments passed to other methods and UMAP 
reduction.key  dimensional reduction key, specifies the string before the number for the dimension names. UMAP by default 
assay  Assay to pull data for when using 
reduction.model 

return.model  whether UMAP will return the uwot model 
umap.method  UMAP implementation to run. Can be

n.neighbors  This determines the number of neighboring points used in local approximations of manifold structure. Larger values will result in more global structure being preserved at the loss of detailed local structure. In general this parameter should often be in the range 5 to 50. 
n.components  The dimension of the space to embed into. 
metric  metric: This determines the choice of metric used to measure distance in the input space. A wide variety of metrics are already coded, and a user defined function can be passed as long as it has been JITd by numba. 
n.epochs  he number of training epochs to be used in optimizing the low dimensional embedding. Larger values result in more accurate embeddings. If NULL is specified, a value will be selected based on the size of the input dataset (200 for large datasets, 500 for small). 
learning.rate  The initial learning rate for the embedding optimization. 
min.dist  This controls how tightly the embedding is allowed compress points together. Larger values ensure embedded points are moreevenly distributed, while smaller values allow the algorithm to optimise more accurately with regard to local structure. Sensible values are in the range 0.001 to 0.5. 
spread  The effective scale of embedded points. In combination with min.dist this determines how clustered/clumped the embedded points are. 
set.op.mix.ratio  Interpolate between (fuzzy) union and intersection as the set operation used to combine local fuzzy simplicial sets to obtain a global fuzzy simplicial sets. Both fuzzy set operations use the product tnorm. The value of this parameter should be between 0.0 and 1.0; a value of 1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy intersection. 
local.connectivity  The local connectivity required  i.e. the number of nearest neighbors that should be assumed to be connected at a local level. The higher this value the more connected the manifold becomes locally. In practice this should be not more than the local intrinsic dimension of the manifold. 
repulsion.strength  Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples. 
negative.sample.rate  The number of negative samples to select per positive sample in the optimization process. Increasing this value will result in greater repulsive force being applied, greater optimization cost, but slightly more accuracy. 
a  More specific parameters controlling the embedding. If NULL, these values are set automatically as determined by min. dist and spread. Parameter of differentiable approximation of right adjoint functor. 
b  More specific parameters controlling the embedding. If NULL, these values are set automatically as determined by min. dist and spread. Parameter of differentiable approximation of right adjoint functor. 
uwot.sgd  Set 
seed.use  Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed 
metric.kwds  A dictionary of arguments to pass on to the metric, such as the p value for Minkowski distance. If NULL then no arguments are passed on. 
angular.rp.forest  Whether to use an angular random projection forest to initialise the approximate nearest neighbor search. This can be faster, but is mostly on useful for metric that use an angular style distance such as cosine, correlation etc. In the case of those metrics angular forests will be chosen automatically. 
densmap  Whether to use the densityaugmented objective of densMAP. Turning on this option generates an embedding where the local densities are encouraged to be correlated with those in the original space. Parameters below with the prefix ‘dens’ further control the behavior of this extension. Default is FALSE. Only compatible with 'umaplearn' method and version of umaplearn >= 0.5.0 
dens.lambda  Specific parameter which controls the regularization weight of the density correlation term in densMAP. Higher values prioritize density preservation over the UMAP objective, and vice versa for values closer to zero. Setting this parameter to zero is equivalent to running the original UMAP algorithm. Default value is 2. 
dens.frac  Specific parameter which controls the fraction of epochs (between 0 and 1) where the densityaugmented objective is used in densMAP. The first (1  dens_frac) fraction of epochs optimize the original UMAP objective before introducing the density correlation term. Default is 0.3. 
dens.var.shift  Specific parameter which specifies a small constant added to the variance of local radii in the embedding when calculating the density correlation objective to prevent numerical instability from dividing by a small number. Default is 0.1. 
verbose  Controls verbosity 
densmap.kwds  A dictionary of arguments to pass on to the densMAP optimization. 
dims  Which dimensions to use as input features, used only if

reduction  Which dimensional reduction (PCA or ICA) to use for the UMAP input. Default is PCA 
features  If set, run UMAP on this subset of features (instead of running on a
set of reduced dimensions). Not set (NULL) by default; 
graph  Name of graph on which to run UMAP 
nn.name  Name of knn output on which to run UMAP 
slot  The slot used to pull data for when using 
reduction.name  Name to store dimensional reduction under in the Seurat object 
Returns a Seurat object containing a UMAP representation
McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv eprints 1802.03426, 2018