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.
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 [2026-01-27 08:21:42] Start standard scop workflow...
#> ℹ [2026-01-27 08:21:43] Checking a list of <Seurat>...
#> ! [2026-01-27 08:21:43] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:21:43] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:21:45] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:21:45] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:21:46] Number of available HVF: 2000
#> ℹ [2026-01-27 08:21:46] Finished check
#> ℹ [2026-01-27 08:21:46] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 08:21:46] Perform pca linear dimension reduction
#> ℹ [2026-01-27 08:21:47] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 08:21:47] Reorder clusters...
#> ℹ [2026-01-27 08:21:48] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 08:21:48] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 08:21:52] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 08:21:57] Run scop standard workflow completed
pancreas_sub <- RunMDS(pancreas_sub)
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "mds"
)