Run MDS (multi-dimensional scaling)

Usage

RunMDS(object, ...)

# S3 method for class 'Seurat'
RunMDS(
  object,
  assay = NULL,
  layer = "data",
  features = NULL,
  nmds = 50,
  dist.method = "euclidean",
  mds.method = "cmdscale",
  rev.mds = FALSE,
  reduction.name = "mds",
  reduction.key = "MDS_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# S3 method for class 'Assay'
RunMDS(
  object,
  assay = NULL,
  layer = "data",
  features = NULL,
  nmds = 50,
  dist.method = "euclidean",
  mds.method = "cmdscale",
  rev.mds = FALSE,
  reduction.key = "MDS_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# S3 method for class 'Assay5'
RunMDS(
  object,
  assay = NULL,
  layer = "data",
  features = NULL,
  nmds = 50,
  dist.method = "euclidean",
  mds.method = "cmdscale",
  rev.mds = FALSE,
  reduction.key = "MDS_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# Default S3 method
RunMDS(
  object,
  assay = NULL,
  layer = "data",
  nmds = 50,
  dist.method = "euclidean",
  mds.method = "cmdscale",
  rev.mds = FALSE,
  reduction.key = "MDS_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

Arguments

object: An object. This can be a Seurat object, an assay object, or a matrix-like object.
...: Arguments passed to other functions.
assay: Assay to use. NULL uses the default assay.
layer: Assay layer to use.
features: Features used instead of a reduction.
nmds: The number of dimensions to be computed.
dist.method: The distance metric to be used. Currently supported values are "euclidean", "chisquared", "kullback", "jeffreys", "jensen", "manhattan", "maximum", "canberra", "minkowski", and "hamming".
mds.method: The MDS algorithm to be used. Currently supported values are "cmdscale", "isoMDS", and "sammon".
rev.mds: Whether to perform reverse MDS (i.e., transpose the input matrix) before running the analysis.
reduction.name: Reduction to be stored in the Seurat object.
reduction.key: The prefix for the column names of the basis vectors.
verbose: Whether to print the message. Default is TRUE.
seed.use: Random seed.

Value

A DimReduc object, or a Seurat object containing the reduction. For classical MDS, standard deviations are sqrt(eigenvalue / (n - 1)) for the retained coordinates, where n is the number of embedded points.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-11-12 05:57:33] Start standard processing workflow...
#> ℹ [2026-11-12 05:57:33] Checking a list of <Seurat>...
#> ! [2026-11-12 05:57:33] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-11-12 05:57:33] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-11-12 05:57:34] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-11-12 05:57:34] Use the separate HVF from `srt_list`
#> ℹ [2026-11-12 05:57:34] Number of available HVF: 2000
#> ℹ [2026-11-12 05:57:34] Finished check
#> ℹ [2026-11-12 05:57:34] Perform `ScaleData()`
#> ℹ [2026-11-12 05:57:34] Perform pca linear dimension reduction
#> ℹ [2026-11-12 05:57:34] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-11-12 05:57:35] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-11-12 05:57:35] Reorder clusters...
#> ℹ [2026-11-12 05:57:35] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-11-12 05:57:35] Perform umap nonlinear dimension reduction
#> ✔ [2026-11-12 05:57:44] Standard processing workflow completed
pancreas_sub <- RunMDS(pancreas_sub)
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "mds"
)