RunSVD function

Run singular value decomposition

Run partial singular value decomposition using irlba

RunSVD(object, ...)

## Default S3 method:
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 class 'Assay'
RunSVD(
  object,
  assay = NULL,
  features = NULL,
  n = 50,
  reduction.key = "LSI_",
  scale.max = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'StdAssay'
RunSVD(
  object,
  assay = NULL,
  features = NULL,
  n = 50,
  reduction.key = "LSI_",
  scale.max = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'Seurat'
RunSVD(
  object,
  assay = NULL,
  features = NULL,
  n = 50,
  reduction.key = "LSI_",
  reduction.name = "lsi",
  scale.max = NULL,
  verbose = TRUE,
  ...
)

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'

Returns

Returns a Seurat object

Examples

x <- matrix(data = rnorm(100), ncol = 10)
RunSVD(x)
## Not run:

RunSVD(atac_small[['peaks']])
## End(Not run)

## Not run:

RunSVD(atac_small[['peaks']])
## End(Not run)

## Not run:

RunSVD(atac_small)
## End(Not run)