Run UMAP (Uniform Manifold Approximation and Projection)

Run UMAP (Uniform Manifold Approximation and Projection)

Usage

RunUMAP2(object, ...)

# S3 method for Seurat
RunUMAP2(
  object,
  reduction = "pca",
  dims = NULL,
  features = NULL,
  neighbor = NULL,
  graph = NULL,
  assay = NULL,
  slot = "data",
  umap.method = "uwot",
  reduction.model = NULL,
  n_threads = NULL,
  return.model = FALSE,
  n.neighbors = 30L,
  n.components = 2L,
  metric = "cosine",
  n.epochs = 200L,
  spread = 1,
  min.dist = 0.3,
  set.op.mix.ratio = 1,
  local.connectivity = 1L,
  negative.sample.rate = 5L,
  a = NULL,
  b = NULL,
  learning.rate = 1,
  repulsion.strength = 1,
  reduction.name = "umap",
  reduction.key = "UMAP_",
  verbose = TRUE,
  seed.use = 11L,
  ...
)

# S3 method for default
RunUMAP2(
  object,
  assay = NULL,
  umap.method = "uwot",
  reduction.model = NULL,
  n_threads = NULL,
  return.model = FALSE,
  n.neighbors = 30L,
  n.components = 2L,
  metric = "cosine",
  n.epochs = 200L,
  spread = 1,
  min.dist = 0.3,
  set.op.mix.ratio = 1,
  local.connectivity = 1L,
  negative.sample.rate = 5L,
  a = NULL,
  b = NULL,
  learning.rate = 1,
  repulsion.strength = 1,
  reduction.key = "UMAP_",
  verbose = TRUE,
  seed.use = 11L,
  ...
)

Arguments

object

An object. This can be a Seurat object, a matrix-like object, a Neighbor object, or a Graph object.

...

Unused argument.

reduction

A character string specifying the reduction to be used. Default is "pca".

dims

An integer vector specifying the dimensions to be used. Default is NULL.

features

A character vector specifying the features to be used. Default is NULL.

neighbor

A character string specifying the name of the Neighbor object to be used. Default is NULL.

graph

A character string specifying the name of the Graph object to be used. Default is NULL.

assay

A character string specifying the assay to be used. Default is NULL.

slot

A character string specifying the slot name to be used. Default is "data".

umap.method

A character string specifying the UMAP method to be used. Options are "naive" and uwot". Default is "uwot".

reduction.model

A DimReduc object containing a pre-trained UMAP model. Default is NULL.

return.model

A logical value indicating whether to return the UMAP model. Default is FALSE.

n.neighbors

An integer specifying the number of nearest neighbors to be used. Default is 30.

n.components

An integer specifying the number of UMAP components. Default is 2.

metric

A character string specifying the metric or a function to be used for distance calculations. 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 is "cosine".

n.epochs

An integer specifying the number of iterations performed during layout optimization for UMAP. Default is 200.

spread

A numeric value specifying the spread parameter for UMAP, used during automatic estimation of a/b parameters. Default is 1.

min.dist

A numeric value specifying the minimum distance between UMAP embeddings, determines how close points appear in the final layout. Default is 0.3.

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

An integer specifying the local connectivity, used during construction of fuzzy simplicial set. Default is 1.

negative.sample.rate

An integer specifying the negative sample rate for UMAP optimization. Determines how many non-neighbor points are used per point and per iteration during layout optimization. Default is 5.

a

A numeric value specifying the parameter a for UMAP optimization. Contributes to gradient calculations during layout optimization. When left at NA, a suitable value will be estimated automatically. Default is NULL.

b

A numeric value specifying the parameter b for UMAP optimization. Contributes to gradient calculations during layout optimization. When left at NA, a suitable value will be estimated automatically. Default is NULL.

learning.rate

A numeric value specifying the initial value of "learning rate" of layout optimization. Default is 1.

repulsion.strength

A numeric value determines, together with alpha, the learning rate of layout optimization. Default is 1.

reduction.name

A character string specifying the name of the reduction to be stored in the Seurat object. Default is "umap".

reduction.key

A character string specifying the prefix for the column names of the UMAP embeddings. Default is "UMAP_".

verbose

A logical value indicating whether to print verbose output. Default is TRUE.

seed.use

An integer specifying the random seed to be used. Default is 11.

Examples

pancreas_sub <- Seurat::FindVariableFeatures(pancreas_sub)
pancreas_sub <- RunUMAP2(object = pancreas_sub, features = Seurat::VariableFeatures(pancreas_sub))
#> 07:52:35 UMAP embedding parameters a = 0.9922 b = 1.112
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> 07:52:35 Read 1000 rows and found 2000 numeric columns
#> 07:52:35 Using Annoy for neighbor search, n_neighbors = 30
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> 07:52:35 Building Annoy index with metric = cosine, n_trees = 50
#> 0%   10   20   30   40   50   60   70   80   90   100%
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#> |
#> 07:52:35 Writing NN index file to temp file /tmp/RtmpZNskfH/file1e2b1d35cf70
#> 07:52:35 Searching Annoy index using 2 threads, search_k = 3000
#> 07:52:36 Annoy recall = 100%
#> 07:52:38 Commencing smooth kNN distance calibration using 2 threads
#>  with target n_neighbors = 30
#> 07:52:40 Initializing from normalized Laplacian + noise (using RSpectra)
#> 07:52:40 Commencing optimization for 200 epochs, with 40796 positive edges
#> 07:52:42 Optimization finished
CellDimPlot(pancreas_sub, group.by = "CellType", reduction = "umap")