calculateMDS2: Perform MDS on cell-level data
In FelixErnst/SEtup: SummarizedExperiment tools for unified procedures

Description Usage Arguments Details Value Author(s) See Also Examples

Perform multi-dimensional scaling (MDS) on cells, based on the data in a SummarizedExperiment object.

calculateMDS2(x, ...)

## S4 method for signature 'ANY'
calculateMDS2(
  x,
  FUN = calculateDistance,
  ncomponents = 2,
  ntop = 500,
  subset_row = NULL,
  scale = FALSE,
  transposed = FALSE,
  ...
)

## S4 method for signature 'SummarizedExperiment'
calculateMDS2(x, ..., exprs_values = "counts", FUN = calculateDistance)

`x`	For `calculateMDS`, a numeric matrix of log-expression values where rows are features and columns are cells. Alternatively, a SummarizedExperiment containing such a matrix.
`...`	For the `calculateMDS` generic, additional arguments to pass to specific methods. For the SummarizedExperiment methods, additional arguments to pass to the ANY method.
`FUN`	a function returning a `dist` structure and accepting a matrix or a SummarizedExperiment as its first argument and does not have arguments name `name`, `altexp`, `dimred` or `n_dimred`.
`ncomponents`	Numeric scalar indicating the number of MDS?g dimensions to obtain.
`ntop`	Numeric scalar specifying the number of features with the highest variances to use for dimensionality reduction.
`subset_row`	Vector specifying the subset of features to use for dimensionality reduction. This can be a character vector of row names, an integer vector of row indices or a logical vector.
`scale`	Logical scalar, should the expression values be standardized?
`transposed`	Logical scalar, is x transposed with cells in rows?
`exprs_values`	a single `character` value for specifying which assay to use for calculation.

The function cmdscale is used internally to compute the MDS components.

a matrix is returned containing the MDS coordinates for each row and column.

Aaron Lun, based on code by Davis McCarthy, modified for flexible distance function input by Felix G.M. Ernst

cmdscale, to perform the underlying calculations.

plotMDS, to quickly visualize the results.

# generate some example data
mat <- matrix(1:60, nrow = 6)
df <- DataFrame(n = c(1:6))
se <- SummarizedExperiment(assays = list(counts = mat),
                           rowData = df)
#
calculateMDS2(se)