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Perform UMAP

Source: R/dim-reduction.R
UMAP.Rd

This function provides a wrapper around umap::umap() that exposes the umap defaults as function arguments.

Usage

UMAP(x, ...)

# S3 method for class 'pca'
UMAP(
  x,
  n_neighbors = 15,
  n_components = 2,
  metric = "euclidean",
  n_epochs = 200,
  input = "data",
  init = "spectral",
  min_dist = 0.1,
  set_op_mix_ratio = 1,
  local_connectivity = 1,
  bandwidth = 1,
  alpha = 1,
  gamma = 1,
  negative_sample_rate = 5,
  a = NA,
  b = NA,
  spread = 1,
  random_state = NA,
  transform_state = NA,
  knn = NA,
  knn_repeats = 1,
  verbose = FALSE,
  umap_learn_args = NA
)

# S3 method for class 'prcomp'
UMAP(
  x,
  metadata = NULL,
  n_neighbors = 15,
  n_components = 2,
  metric = "euclidean",
  n_epochs = 200,
  input = "data",
  init = "spectral",
  min_dist = 0.1,
  set_op_mix_ratio = 1,
  local_connectivity = 1,
  bandwidth = 1,
  alpha = 1,
  gamma = 1,
  negative_sample_rate = 5,
  a = NA,
  b = NA,
  spread = 1,
  random_state = NA,
  transform_state = NA,
  knn = NA,
  knn_repeats = 1,
  verbose = FALSE,
  umap_learn_args = NA
)

# S3 method for class 'matrix'
UMAP(
  x,
  metadata = NULL,
  n_neighbors = 15,
  n_components = 2,
  metric = "euclidean",
  n_epochs = 200,
  input = "data",
  init = "spectral",
  min_dist = 0.1,
  set_op_mix_ratio = 1,
  local_connectivity = 1,
  bandwidth = 1,
  alpha = 1,
  gamma = 1,
  negative_sample_rate = 5,
  a = NA,
  b = NA,
  spread = 1,
  random_state = NA,
  transform_state = NA,
  knn = NA,
  knn_repeats = 1,
  verbose = FALSE,
  umap_learn_args = NA
)

# S3 method for class 'data.frame'
UMAP(
  x,
  metadata = NULL,
  n_neighbors = 15,
  n_components = 2,
  metric = "euclidean",
  n_epochs = 200,
  input = "data",
  init = "spectral",
  min_dist = 0.1,
  set_op_mix_ratio = 1,
  local_connectivity = 1,
  bandwidth = 1,
  alpha = 1,
  gamma = 1,
  negative_sample_rate = 5,
  a = NA,
  b = NA,
  spread = 1,
  random_state = NA,
  transform_state = NA,
  knn = NA,
  knn_repeats = 1,
  verbose = FALSE,
  umap_learn_args = NA
)

# S3 method for class 'dist'
UMAP(
  x,
  metadata = NULL,
  n_neighbors = 15,
  n_components = 2,
  metric = "euclidean",
  n_epochs = 200,
  input = "dist",
  init = "spectral",
  min_dist = 0.1,
  set_op_mix_ratio = 1,
  local_connectivity = 1,
  bandwidth = 1,
  alpha = 1,
  gamma = 1,
  negative_sample_rate = 5,
  a = NA,
  b = NA,
  spread = 1,
  random_state = NA,
  transform_state = NA,
  knn = NA,
  knn_repeats = 1,
  verbose = FALSE,
  umap_learn_args = NA
)

Arguments

x

PCA object, prcomp object, or numeric matrix/data.frame that can be converted to a numeric matrix

n_neighbors

Number of nearest neighbors. Default 15

n_components

Dimension of target (output) space. Default 2

metric

character or function; determines how distances between data points are computed. When using a string, available metrics are: euclidean, manhattan. Other available generalized metrics are: cosine, pearson, pearson2. Note the triangle inequality may not be satisfied by some generalized metrics, hence knn search may not be optimal. When using metric.function as a function, the signature must be function(matrix, origin, target) and should compute a distance between the origin column and the target columns. Default "euclidean"

n_epochs

Number of iterations performed during layout optimization. Default 200

input

character, use either "data" or "dist"; determines whether the primary input argument to umap() is treated as a data matrix or as a distance matrix. Default "data"

init

character or matrix. The default string "spectral" computes an initial embedding using eigenvectors of the connectivity graph matrix. An alternative is the string "random", which creates an initial layout based on random coordinates. This setting.can also be set to a matrix, in which case layout optimization begins from the provided coordinates. Default "spectral"

min_dist

numeric; determines how close points appear in the final layout. Default 0.1

set_op_mix_ratio

numeric in range [0,1]; determines who the knn-graph is used to create a fuzzy simplicial graph. Default 1

local_connectivity

numeric; used during construction of fuzzy simplicial set. Default 1

bandwidth

numeric; used during construction of fuzzy simplicial set. Default 1

alpha

numeric; initial value of "learning rate" of layout optimization. Default 1

gamma

numeric; determines, together with alpha, the learning rate of layout optimization. Default 1

negative_sample_rate

integer; determines how many non-neighbor points are used per point and per iteration during layout optimization. Default 5

a

numeric; contributes to gradient calculations during layout optimization. When left at NA, a suitable value will be estimated automatically. Default NA

b

numeric; contributes to gradient calculations during layout optimization. When left at NA, a suitable value will be estimated automatically. Default NA

spread

numeric; used during automatic estimation of a/b parameters. Default 1

random_state

integer; seed for random number generation used during umap(). Default NA

transform_state

nteger; seed for random number generation used during predict(). Default NA

knn

object of class umap.knn; precomputed nearest neighbors. Default NA

knn_repeats

number of times to restart knn search. Default 1

verbose

logical or integer; determines whether to show progress messages. Default FALSE

umap_learn_args

vector of arguments to python package umap-learn. Default NA

metadata

Optional data.frame with sample-level metadata. Used if a prcomp object or data.frame/matrix is supplied. Default NULL

Value

data.frame with the UMAP embeddings. If metadata was supplied then metadata columns are added to the results.

Examples


# Create metadata for plotting
metadata <- data.frame(row.names = colnames(GSE161650_lc))
metadata$Group <- rep(c("DMSO", "THZ1"), each = 3)

# PCA with PCAtools
p <- PCAtools::pca(GSE161650_lc, metadata, center = TRUE, scale = TRUE)

# PCA with prcomp
pr <- prcomp(t(GSE161650_lc), center = TRUE, scale. = FALSE)

# Pre-calculated distance matrix
d <- dist(t(GSE161650_lc))

# Perform UMAP on each data type
udata <- UMAP(p, n_neighbors = 2)
#> Warning: failed creating initial embedding; using random embedding instead
#> Warning: failed creating initial embedding; using random embedding instead
udata2 <- UMAP(pr, metadata, n_neighbors = 2)
#> Warning: failed creating initial embedding; using random embedding instead
#> Warning: failed creating initial embedding; using random embedding instead
udata3 <- UMAP(d, metadata, n_neighbors = 2)
#> Warning: failed creating initial embedding; using random embedding instead
#> Warning: failed creating initial embedding; using random embedding instead

# Also on raw data
udata4 <- UMAP(t(GSE161650_lc), metadata, n_neighbors = 2)
#> Warning: failed creating initial embedding; using random embedding instead
#> Warning: failed creating initial embedding; using random embedding instead