Run partial singular value decomposition using irlba
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,
...
)
# 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,
...
)
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)
#> Running SVD
#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.
#> Scaling cell embeddings
#> Warning: No assay specified, setting assay as RNA by default.
#> A dimensional reduction object with key LSI_
#> Number of dimensions: 9
#> Projected dimensional reduction calculated: FALSE
#> Jackstraw run: FALSE
#> Computed using assay: RNA
RunSVD(atac_small[['peaks']])
#> Running SVD
#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.
#> Scaling cell embeddings
#> Warning: No assay specified, setting assay as RNA by default.
#> A dimensional reduction object with key LSI_
#> Number of dimensions: 50
#> Projected dimensional reduction calculated: FALSE
#> Jackstraw run: FALSE
#> Computed using assay: RNA
RunSVD(atac_small)
#> Running SVD
#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.
#> Scaling cell embeddings
#> An object of class Seurat
#> 1323 features across 100 samples within 3 assays
#> Active assay: peaks (323 features, 323 variable features)
#> 2 other assays present: bins, RNA
#> 2 dimensional reductions calculated: lsi, umap