Run NMF (non-negative matrix factorization)

Usage

RunNMF(object, ...)

# S3 method for class 'Seurat'
RunNMF(
  object,
  assay = NULL,
  layer = "data",
  features = NULL,
  nbes = 50,
  nmf.method = "RcppML",
  tol = 1e-05,
  maxit = 100,
  rev.nmf = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.name = "nmf",
  reduction.key = "BE_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# S3 method for class 'Assay'
RunNMF(
  object,
  assay = NULL,
  layer = "data",
  features = NULL,
  nbes = 50,
  nmf.method = "RcppML",
  tol = 1e-05,
  maxit = 100,
  rev.nmf = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "BE_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# S3 method for class 'Assay5'
RunNMF(
  object,
  assay = NULL,
  layer = "data",
  features = NULL,
  nbes = 50,
  nmf.method = "RcppML",
  tol = 1e-05,
  maxit = 100,
  rev.nmf = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "BE_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# Default S3 method
RunNMF(
  object,
  assay = NULL,
  layer = "data",
  nbes = 50,
  nmf.method = "RcppML",
  tol = 1e-05,
  maxit = 100,
  rev.nmf = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "BE_",
  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 passed to RcppML::nmf or NMF::nmf.
assay: The assay to be used for the analysis. Default is NULL.
layer: The layer to be used for the analysis. Default is "data".
features: The features to be used for the analysis. Default is NULL, which uses all variable features.
nbes: The number of basis vectors (components) to be computed. Default is 50.
nmf.method: The NMF algorithm to be used. Currently supported values are "RcppML" and "NMF". Default is "RcppML".
tol: The tolerance for convergence (only applicable when nmf.method is "RcppML"). Default is 1e-5.
maxit: The maximum number of iterations for convergence (only applicable when nmf.method is "RcppML"). Default is 100.
rev.nmf: Whether to perform reverse NMF (i.e., transpose the input matrix) before running the analysis. Default is FALSE.
ndims.print: The dimensions (number of basis vectors) to print in the output. Default is 1:5.
nfeatures.print: The number of features to print in the output. Default is 30.
reduction.name: The name of the reduction to be stored in the Seurat object. Default is "nmf".
reduction.key: The prefix for the column names of the basis vectors. Default is "BE_".
verbose: Whether to print the message. Default is TRUE.
seed.use: The random seed to be used. Default is 11.

Examples

library(Matrix)
#> 
#> Attaching package: ‘Matrix’
#> The following object is masked from ‘package:S4Vectors’:
#> 
#>     expand
pancreas_sub <- RunNMF(object = pancreas_sub)
#> Warning: Layer ‘data’ is empty
#> Error in apply(X = data_use[features, ], MARGIN = 1, FUN = stats::var): dim(X) must have a positive length
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "nmf"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.