Rust-based den-SNE

Performs den-SNE dimensionality reduction on the input data. den-SNE is t-SNE with an added density-preserving term, so a tight cluster stays tight and a diffuse one stays diffuse. Plain t-SNE gives you no such guarantee: relative sizes in the embedding mean nothing. This function provides a user-friendly interface with input validation before calling the Rust implementation.

Usage

densne(
  data,
  knn = NULL,
  n_dim = 2L,
  perplexity = 20,
  approx_type = c("bh", "fft"),
  knn_method = c("kmknn", "balltree", "hnsw", "annoy", "nndescent", "exhaustive", "ivf"),
  nn_params = params_nn(),
  tsne_params = params_tsne(),
  dens_params = params_densne(),
  seed = 42L,
  use_high_precision = NULL,
  .verbose = TRUE
)

Arguments

data

Numerical matrix or data frame. The data to embed of shape samples x features. Will be coerced to a matrix.

knn

Optional NearestNeighbours class. If provided, den-SNE will skip the k-nearest neighbour graph generation and use this one. Defaults to NULL.

n_dim

Integer. Number of dimensions in the embedding space. Currently only 2L is supported. Defaults to 2L.

perplexity

Numeric. Perplexity parameter, related to the number of nearest neighbours used in manifold learning. Typical values are between 5 and 50. Defaults to 20.0.

approx_type

Character. Approximation method for computing repulsive forces. One of "bh" for Barnes-Hut or "fft" for FFT-accelerated interpolation. Defaults to "bh".

knn_method

Character. (Approximate) Nearest neighbour method to use. One of "kmknn", "hnsw", "annoy", "nndescent", "balltree", "ivf" or "exhaustive". Defaults to "kmknn".

nn_params

Named list. Nearest neighbour search parameters, see params_nn().

tsne_params

Named list. t-SNE algorithm parameters, see params_tsne().

dens_params

Named list. Density-preservation parameters, see params_densne().

seed

Integer. Random seed for reproducibility.

use_high_precision

Optional boolean. Gives fine-grained control over fp32 vs fp64 usage.

.verbose

Logical. Controls verbosity. Defaults to TRUE.

Value

A numerical matrix with dimensions samples x n_dim containing the den-SNE embedding.

Details

The number of neighbours will be 3 * perplexity, as this is a usual default in tSNE. Setting lambda to 0 in params_densne() recovers plain tsne() exactly. The default lambda is twenty times smaller than the densMAP one, matching the reference implementations.

References

Narayan, Berger & Cho, Nat. Biotechnol., 2021