runNMDS: Perform non-metric MDS on sample-level data
In FelixErnst/mia: Microbiome analysis

runNMDS

R Documentation

Perform non-metric MDS on sample-level data

Description

Perform non-metric multi-dimensional scaling (nMDS) on samples, based on the data in a SingleCellExperiment object.

Usage

getNMDS(x, ...)

## S4 method for signature 'ANY'
getNMDS(
  x,
  FUN = vegdist,
  nmds.fun = nmdsFUN,
  nmdsFUN = c("isoMDS", "monoMDS"),
  ncomponents = 2,
  ntop = 500,
  subset.row = subset_row,
  subset_row = NULL,
  scale = FALSE,
  transposed = FALSE,
  keep.dist = keep_dist,
  keep_dist = FALSE,
  ...
)

## S4 method for signature 'SummarizedExperiment'
getNMDS(
  x,
  ...,
  assay.type = assay_name,
  assay_name = exprs_values,
  exprs_values = "counts",
  FUN = vegdist
)

## S4 method for signature 'SingleCellExperiment'
getNMDS(
  x,
  ...,
  assay.type = assay_name,
  assay_name = exprs_values,
  exprs_values = "counts",
  dimred = NULL,
  ndimred = n_dimred,
  n_dimred = NULL,
  FUN = vegdist
)

calculateNMDS(x, ...)

addNMDS(x, ..., altexp = NULL, name = "NMDS")

runNMDS(x, ...)

Arguments

`x`	For `getNMDS`, a numeric matrix of expression values where rows are features and columns are cells. Alternatively, a `TreeSummarizedExperiment` containing such a matrix. For `addNMDS` a SingleCellExperiment
`...`	additional arguments to pass to `FUN` and `nmds.fun`.
`FUN`	a `function` or `character` value with a function name returning a `dist` object
`nmds.fun`	a `character` value to choose the scaling implementation, either “isoMDS” for `MASS::isoMDS` or “monoMDS” for `vegan::monoMDS`
`nmdsFUN`	Deprecated. Use `nmds.fun` instead.
`ncomponents`	Numeric scalar indicating the number of NMDS 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.
`subset_row`	Deprecated. Use `subset.row` instead.
`scale`	Logical scalar, should the expression values be standardized?
`transposed`	Logical scalar, is x transposed with cells in rows?
`keep.dist`	Logical scalar indicating whether the `dist` object calculated by `FUN` should be stored as ‘dist’ attribute of the matrix returned/stored by `getNMDS`/ `addNMDS`.
`keep_dist`	Deprecated. Use `keep.dist` instead.
`assay.type`	a single `character` value for specifying which assay to use for calculation.
`assay_name`	a single `character` value for specifying which assay to use for calculation. (Please use `assay.type` instead. At some point `assay_name` will be disabled.)
`exprs_values`	a single `character` value for specifying which assay to use for calculation. (Please use `assay.type` instead.)
`dimred`	String or integer scalar specifying the existing dimensionality reduction results to use.
`ndimred`	Integer scalar or vector specifying the dimensions to use if dimred is specified.
`n_dimred`	Deprecated. Use `ndimred` instead.
`altexp`	String or integer scalar specifying an alternative experiment containing the input data.
`name`	String specifying the name to be used to store the result in the reducedDims of the output.

Details

Either MASS::isoMDS or vegan::monoMDS are used internally to compute the NMDS components. If you supply a custom FUN, make sure that the arguments of FUN and nmds.fun do not collide.

Value

For getNMDS, a matrix is returned containing the MDS coordinates for each sample (row) and dimension (column).

Author(s)

Felix Ernst

Examples

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

#
data(esophagus)
esophagus <- addNMDS(esophagus, FUN = vegan::vegdist, name = "BC")
esophagus <- addNMDS(esophagus, FUN = vegan::vegdist, name = "euclidean",
                     method = "euclidean")
reducedDims(esophagus)

FelixErnst/mia documentation built on June 19, 2024, 3:51 a.m.