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.
...: Additional arguments to be passed to stats::cmdscale, MASS::isoMDS or MASS::sammon.
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used. When the object also contains ChromatinAssay, the default assay and additional ChromatinAssay will be preprocessed sequentially.
layer: Which layer to use. Default is data.
features: A character vector of features to use. Default is NULL.
nmds: The number of dimensions to be computed. Default is 50.
dist.method: The distance metric to be used. Currently supported values are "euclidean", "chisquared", "kullback", "jeffreys", "jensen", "manhattan", "maximum", "canberra", "minkowski", and "hamming". Default is "euclidean".
mds.method: The MDS algorithm to be used. Currently supported values are "cmdscale", "isoMDS", and "sammon". Default is "cmdscale".
rev.mds: Whether to perform reverse MDS (i.e., transpose the input matrix) before running the analysis. Default is FALSE.
reduction.name: The name of the reduction to be stored in the Seurat object. Default is "mds".
reduction.key: The prefix for the column names of the basis vectors. Default is "MDS_".
verbose: Whether to print the message. Default is TRUE.
seed.use: Random seed for reproducibility. Default is 11.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-04-26 02:23:04] Start standard processing workflow...
#> ℹ [2026-04-26 02:23:05] Checking a list of <Seurat>...
#> ! [2026-04-26 02:23:05] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:23:05] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:23:08] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:23:08] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:23:08] Number of available HVF: 2000
#> ℹ [2026-04-26 02:23:08] Finished check
#> ℹ [2026-04-26 02:23:09] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:23:09] Perform pca linear dimension reduction
#> ℹ [2026-04-26 02:23:10] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-04-26 02:23:10] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-04-26 02:23:10] Reorder clusters...
#> ℹ [2026-04-26 02:23:10] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:23:10] Perform umap nonlinear dimension reduction
#> ℹ [2026-04-26 02:23:10] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-04-26 02:23:16] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-04-26 02:23:22] Standard processing workflow completed
pancreas_sub <- RunMDS(pancreas_sub)
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "mds"
)