densmap: Density-preserving UMAP

In densvis: Density-Preserving Data Visualization via Non-Linear Dimensionality Reduction

Description

Density-preserving UMAP

Usage

densmap(
  x,
  n_components = 2L,
  dens_frac = 0.3,
  dens_lambda = 0.1,
  var_shift = 0.1,
  n_neighbors = 30L,
  metric = "euclidean",
  n_epochs = 750L,
  learning_rate = 1,
  init = c("spectral", "random"),
  Y_init = NULL,
  min_dist = 0.1,
  spread = 1,
  set_op_mix_ratio = 1,
  local_connectivity = 1L,
  repulsion_strength = 1,
  negative_sample_rate = 5L,
  transform_queue_size = 4,
  random_state = NULL,
  angular_rp_forest = FALSE,
  target_n_neighbors = -1,
  target_weight = 0.5
)

Arguments

x	A numeric matrix or matrix-like object.
n_components	The dimension of the space to embed into. This defaults to 2 to provide easy visualization, but can reasonably be set to any integer value in the range 2 to 100.
dens_frac	numeric; fraction of the iterations for which the full objective function (including the density-preserving term) is used. For the first 1 - dens_frac fraction of the iterations, only the original t-SNE objective function is used.
dens_lambda	numeric; the relative importance of the density-preservation term compared to the original t-SNE objective function.
var_shift	Regularization term added to the variance of embedding local radius for stability (float, non-negative); default 0.1.
n_neighbors	The size of local neighborhood (in terms of number of neighboring sample points) used for manifold approximation. Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. In general values should be in the range 2 to 100.
metric	The metric to use to compute distances in high dimensional space. If a string is passed it must match one of: "euclidean" "manhattan" "chebyshev" "minkowski" "canberra" "braycurtis" "mahalanobis" "wminkowski" "seuclidean" "cosine" "correlation" "haversine" "hamming" "jaccard" "dice" "russelrao" "kulsinski" "rogerstanimoto" "sokalmichener" "sokalsneath" "yule"
n_epochs	The number of training epochs to be used in optimizing the low dimensional embedding. Larger values result in more accurate embeddings. If None is specified a value will be selected based on the size of the input dataset (200 for large datasets, 500 for small). a valid predefined metric.
learning_rate	The initial learning rate for the embedding optimization.
init	How to initialize the low dimensional embedding. Valid options: "spectral": use a spectral embedding of the fuzzy 1-skeleton "random": assign initial embedding positions at random.
Y_init	Numeric matrix specifying the initial locations of the objects in the embedding. If NULL, random or spectral initialization will be used, controlled by the init argument.
min_dist	The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the spread value, which determines the scale at which embedded points will be spread out.
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.
transform_queue_size	For transform operations (embedding new points using a trained model_ this will control how aggressively to search for nearest neighbors. Larger values will result in slower performance but more accurate nearest neighbor evaluation.
random_state	The seed used by the random number generator.
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.
target_n_neighbors	The number of nearest neighbors to use to construct the target simplcial set. If set to -1 use the n_neighbors value.
target_weight	Weighting factor between data topology and target topology. A value of 0.0 weights entirely on data, a value of 1.0 weights entirely on target. The default of 0.5 balances the weighting equally between data and target.

Value

A numeric matrix

References

Density-Preserving Data Visualization Unveils Dynamic Patterns of Single-Cell Transcriptomic Variability Ashwin Narayan, Bonnie Berger, Hyunghoon Cho; bioRxiv (2020) doi:10.1101/2020.05.12.077776

Examples

set.seed(42)
x <- matrix(rnorm(200), ncol=2)
densmap(x)

densvis documentation built on Jan. 28, 2021, 2:01 a.m.