RunSVD: Run singular value decomposition

Description

Run partial singular value decomposition using irlba

Usage

RunSVD(object, ...)
# S3 method for default
RunSVD(
  object,
  assay = NULL,
  n = 50,
  scale.embeddings = TRUE,
  reduction.key = "LSI_",
  scale.max = NULL,
  verbose = TRUE,
  irlba.work = n * 3,
  tol = 1e-05,
  ...
)
# S3 method for Assay
RunSVD(
  object,
  assay = NULL,
  features = NULL,
  n = 50,
  reduction.key = "LSI_",
  scale.max = NULL,
  verbose = TRUE,
  ...
)
# S3 method for Seurat
RunSVD(
  object,
  assay = NULL,
  features = NULL,
  n = 50,
  reduction.key = "LSI_",
  reduction.name = "lsi",
  scale.max = NULL,
  verbose = TRUE,
  ...
)

Value

Returns a Seurat object

Arguments

object: A Seurat object
...: Arguments passed to other methods
assay: Which assay to use. If NULL, use the default assay
n: Number of singular values to compute
scale.embeddings: Scale cell embeddings within each component to mean 0 and SD 1 (default TRUE).
reduction.key: Key for dimension reduction object
scale.max: Clipping value for cell embeddings. Default (NULL) is no clipping.
verbose: Print messages
irlba.work: work parameter for irlba. Working subspace dimension, larger values can speed convergence at the cost of more memory use.
tol: Tolerance (tol) parameter for irlba. Larger values speed up convergence due to greater amount of allowed error.
features: Which features to use. If NULL, use variable features
reduction.name: Name for stored dimension reduction object. Default 'svd'

Examples

Run this code

x <- matrix(data = rnorm(100), ncol = 10)
RunSVD(x)
RunSVD(atac_small[['peaks']])
RunSVD(atac_small)

Run the code above in your browser using DataLab