Run UMAP — RunUMAP2 • scop

Run UMAP

Usage

RunUMAP2(object, ...)

# S3 method for class 'Seurat'
RunUMAP2(
  object,
  reduction = "pca",
  dims = NULL,
  features = NULL,
  neighbor = NULL,
  graph = NULL,
  assay = NULL,
  layer = "data",
  umap.method = "uwot",
  reduction.model = NULL,
  n_threads = NULL,
  return.model = FALSE,
  n.neighbors = 30L,
  n.components = 2L,
  metric = "cosine",
  n.epochs = 200L,
  cores = 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 = 11,
  ...
)

# Default S3 method
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,
  cores = 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: A Seurat object, matrix-like object, Neighbor, or Graph.
...: Passed to the UMAP implementation.
reduction: Linear reduction used as input.
dims: Dimensions to use. Supply only one of dims, features, neighbor, or graph.
features: Features used instead of a reduction.
neighbor, graph: Existing Neighbor or Graph object name.
assay: Assay to use. NULL uses the default assay.
layer: Assay layer to use.
umap.method: "uwot" or "naive".
reduction.model: Pre-trained UMAP DimReduc used to embed new data.
n_threads: Number of threads.
return.model: Store the UMAP model.
n.neighbors, n.components, metric, n.epochs: UMAP layout parameters. String metric values include "euclidean", "manhattan", "cosine", "pearson", and "pearson2".
cores: Number of CPU cores.
min.dist: Minimum embedding distance (how tightly points pack).
set.op.mix.ratio: Mix of fuzzy union (1) and intersection (0).
local.connectivity, negative.sample.rate: Fuzzy simplicial set and optimization sampling.
a, b: UMAP curve parameters. NULL estimates them automatically.
learning.rate, repulsion.strength: Layout optimization rates.
reduction.name, reduction.key: Stored reduction name and embedding prefix.
verbose: Whether to print the message. Default is TRUE.
seed.use: Random seed.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 05:52:24] Start standard processing workflow...
#> ℹ [2026-08-30 05:52:24] Checking a list of <Seurat>...
#> ! [2026-08-30 05:52:24] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:52:24] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:52:24] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:52:24] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:52:24] Number of available HVF: 2000
#> ℹ [2026-08-30 05:52:24] Finished check
#> ℹ [2026-08-30 05:52:24] Perform `ScaleData()`
#> ℹ [2026-08-30 05:52:24] Perform pca linear dimension reduction
#> ℹ [2026-08-30 05:52:25] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 05:52:25] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 05:52:25] Reorder clusters...
#> ℹ [2026-08-30 05:52:25] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:52:25] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 05:52:34] Standard processing workflow completed
pancreas_sub <- RunUMAP2(pancreas_sub, dims = 1:30)
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "umap"
)