Run UMAP

Run Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP).

run_umap(
  mat,
  transpose = TRUE,
  pca = ncol(mat),
  add_names = TRUE,
  n_components = 2,
  n_neighbors = 15,
  min_dist = 0.01,
  metric = "euclidean",
  init = "spectral",
  seed = 2020,
  verbose = TRUE,
  ...
)

Arguments

mat	Matrix to run UMAP on.
transpose	Whether to transpose the matrix first.
pca	If set to a positive integer value, reduce data to this number of columns using PCA. Doesn't applied if the distance metric is "hamming", or the dimensions of the data is larger than the number specified (i.e. number of rows and columns must be larger than the value of this parameter). If you have > 100 columns in a data frame or matrix, reducing the number of columns in this way may substantially increase the performance of the nearest neighbor search at the cost of a potential decrease in accuracy. In many t-SNE applications, a value of 50 is recommended, although there's no guarantee that this is appropriate for all settings.
add_names	Add colnames and rownames to embeddings and loadings.
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.
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.
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.
metric	Type of distance metric to use to find nearest neighbors. One of: "euclidean" (the default) "cosine" "manhattan" "hamming" "correlation" (a distance based on the Pearson correlation) "categorical" (see below) Only applies if nn_method = "annoy" (for nn_method = "fnn", the distance metric is always "euclidean"). If X is a data frame or matrix, then multiple metrics can be specified, by passing a list to this argument, where the name of each item in the list is one of the metric names above. The value of each list item should be a vector giving the names or integer ids of the columns to be included in a calculation, e.g. metric = list(euclidean = 1:4, manhattan = 5:10). Each metric calculation results in a separate fuzzy simplicial set, which are intersected together to produce the final set. Metric names can be repeated. Because non-numeric columns are removed from the data frame, it is safer to use column names than integer ids. Factor columns can also be used by specifying the metric name "categorical". Factor columns are treated different from numeric columns and although multiple factor columns can be specified in a vector, each factor column specified is processed individually. If you specify a non-factor column, it will be coerced to a factor. For a given data block, you may override the pca and pca_center arguments for that block, by providing a list with one unnamed item containing the column names or ids, and then any of the pca or pca_center overrides as named items, e.g. metric = list(euclidean = 1:4, manhattan = list(5:10, pca_center = FALSE)). This exists to allow mixed binary and real-valued data to be included and to have PCA applied to both, but with centering applied only to the real-valued data (it is typical not to apply centering to binary data before PCA is applied).
init	Type of initialization for the coordinates. Options are: "spectral" Spectral embedding using the normalized Laplacian of the fuzzy 1-skeleton, with Gaussian noise added. "normlaplacian". Spectral embedding using the normalized Laplacian of the fuzzy 1-skeleton, without noise. "random". Coordinates assigned using a uniform random distribution between -10 and 10. "lvrandom". Coordinates assigned using a Gaussian distribution with standard deviation 1e-4, as used in LargeVis (Tang et al., 2016) and t-SNE. "laplacian". Spectral embedding using the Laplacian Eigenmap (Belkin and Niyogi, 2002). "pca". The first two principal components from PCA of X if X is a data frame, and from a 2-dimensional classical MDS if X is of class "dist". "spca". Like "pca", but each dimension is then scaled so the standard deviation is 1e-4, to give a distribution similar to that used in t-SNE. This is an alias for init = "pca", init_sdev = 1e-4. "agspectral" An "approximate global" modification of "spectral" which all edges in the graph to a value of 1, and then sets a random number of edges (negative_sample_rate edges per vertex) to 0.1, to approximate the effect of non-local affinities. A matrix of initial coordinates. For spectral initializations, ("spectral", "normlaplacian", "laplacian"), if more than one connected component is identified, each connected component is initialized separately and the results are merged. If verbose = TRUE the number of connected components are logged to the console. The existence of multiple connected components implies that a global view of the data cannot be attained with this initialization. Either a PCA-based initialization or increasing the value of n_neighbors may be more appropriate.
seed	Seed passed to \[base]set.seed for reproducibility between runs.
verbose	If TRUE, log details to the console.
...	Additional parameters passed to umap.

Source

UMAP documentation

Details

Uses umap, but runs and returns PCA by default.