Run GLMPCA (generalized version of principal components analysis)

Usage

RunGLMPCA(object, ...)

# S3 method for class 'Seurat'
RunGLMPCA(
  object,
  assay = NULL,
  layer = "counts",
  features = NULL,
  L = 5,
  fam = c("poi", "nb", "nb2", "binom", "mult", "bern"),
  rev.gmlpca = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.name = "glmpca",
  reduction.key = "GLMPC_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# S3 method for class 'Assay'
RunGLMPCA(
  object,
  assay = NULL,
  layer = "counts",
  features = NULL,
  L = 5,
  fam = c("poi", "nb", "nb2", "binom", "mult", "bern"),
  rev.gmlpca = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "GLMPC_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# S3 method for class 'Assay5'
RunGLMPCA(
  object,
  assay = NULL,
  layer = "counts",
  features = NULL,
  L = 5,
  fam = c("poi", "nb", "nb2", "binom", "mult", "bern"),
  rev.gmlpca = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "GLMPC_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# Default S3 method
RunGLMPCA(
  object,
  assay = NULL,
  layer = "counts",
  features = NULL,
  L = 5,
  fam = c("poi", "nb", "nb2", "binom", "mult", "bern"),
  rev.gmlpca = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "GLMPC_",
  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 the glmpca::glmpca function.
assay: The assay to be used for the analysis. Default is NULL.
layer: The layer to be used for the analysis. Default is "counts".
features: The features to be used for the analysis. Default is NULL, which uses all variable features.
L: The number of components to be computed. Default is 5.
fam: The family of the generalized linear model to be used. Currently supported values are "poi", "nb", "nb2", "binom", "mult", and "bern". Default is "poi".
rev.gmlpca: Whether to perform reverse GLMPCA (i.e., transpose the input matrix) before running the analysis. Default is FALSE.
ndims.print: The dimensions (number of components) 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 "glmpca".
reduction.key: The prefix for the column names of the basis vectors. Default is "GLMPC_".
verbose: Whether to print the message. Default is TRUE.
seed.use: The random seed to be used. Default is 11.

Examples

pancreas_sub <- RunGLMPCA(object = pancreas_sub)
#> 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 = "glmpca"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.