Run UMAP on a Seurat object

Usage

BuildAnnoyUMAP(
  object,
  neighbors_slot = NULL,
  dims,
  reduction = "pca",
  features = NULL,
  graph_key = "annoy",
  assay = DefaultAssay(object),
  slot = "scale.data",
  n.neighbors = 30L,
  n.components = 2L,
  metric = "cosine",
  n.epochs = NULL,
  prune.snn = 1/15,
  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,
  reduction_key = "UMAP_",
  verbose = TRUE,
  reduction.name = "umap",
  return.model = TRUE,
  save.graphs = TRUE,
  init.pos = "spectral",
  scale = FALSE
)

Arguments

object: A Seurat object
neighbors_slot: Name of the Neighbors slot in object to utilize for pre-computed kNN, such as with FindMultiModalNeighbors. Leave NULL if want to calculate kNN utilizing the remaining arguments in this function.
dims: Which dimensions from the reduction to use as input features
reduction: Which dimensional reduction to use for the UMAP input.
features: If NULL, run UMAP on this subset of features (instead of running on a set of reduced dimensions).
graph_key: graph key, specifies the string before the name of the returned graphs. annoy by default
assay: Assay to pull data for when using features, or assay used to construct Graph if running UMAP on a Graph
slot: The slot used to pull data for when using features. data slot is by default.
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).
prune.snn: Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the stringency of pruning (0 — no pruning, 1 — prune everything).
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 t-norm. 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 uwot::umap(fast_sgd = TRUE); see umap for more details
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.
verbose: Controls verbosity
reduction.name: Name to store dimensional reduction under in the Seurat object
return.model: whether UMAP will return the uwot model
save.graphs: If TRUE, then in addition to the embedding, also return nearest neighbor graphs (fuzzy, kNN, SNN, and cosine) as Graph objects in the Seurat object
init.pos: Type of initialization for the coordinates. See uwot::umap for more details.
scale: Scaling to apply to embeddings. See uwot::umap
densmap: Whether to use the density-augmented 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 'umap-learn' method and version of umap-learn >= 0.5.0
densmap.kwds: A dictionary of arguments to pass on to the densMAP optimization.
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 density-augmented 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.
reduction.key: dimensional reduction key, specifies the string before the number for the dimension names. UMAP by default