Signac (version 0.2.4)

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 = "SVD_",
  scale.max = NULL,
  verbose = TRUE,
  irlba.work = n + 50,
  ...
)
# S3 method for Assay
RunSVD(
  object,
  assay = NULL,
  features = NULL,
  n = 50,
  reduction.key = "SVD_",
  scale.max = NULL,
  verbose = TRUE,
  ...
)
# S3 method for Seurat
RunSVD(
  object,
  assay = NULL,
  features = NULL,
  n = 50,
  reduction.key = "SVD_",
  reduction.name = "svd",
  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.

features

Which features to use. If NULL, use variable features

reduction.name

Name for stored dimension reduction object. Default 'svd'

Value

Returns a Seurat object

Examples

# NOT RUN {
x <- matrix(data = rnorm(100), ncol = 10)
RunSVD(x)
RunSVD(atac_small[['peaks']])
RunSVD(atac_small)
# }

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