RunSVD: Run singular value decomposition

In Signac: Analysis of Single-Cell Chromatin Data

Run singular value decomposition

Description

Run partial singular value decomposition using irlba

Usage

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'

Value

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)

Signac documentation built on Sept. 11, 2024, 9:30 p.m.