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, ... ) ## 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 'Seurat' RunSVD( object, assay = NULL, features = NULL, n = 50, reduction.key = "LSI_", reduction.name = "lsi", scale.max = NULL, verbose = TRUE, ... )
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 |
features |
Which features to use. If NULL, use variable features |
reduction.name |
Name for stored dimension reduction object. Default 'svd' |
Returns a Seurat object
x <- matrix(data = rnorm(100), ncol = 10) RunSVD(x) RunSVD(atac_small[['peaks']]) RunSVD(atac_small)
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