RunNMF | R Documentation |
Decompose an expression matrix A with non-negative elements into matrices WxH, also with non-negative elements. W is the feature loading matrix (features x factors) and H is the low dimensional embedding of the spots (factors x spots).
RunNMF( object, assay = NULL, slot = "scale.data", features = NULL, nfactors = 20, rescale = TRUE, reduction.name = "NMF", reduction.key = "factor_", n.cores = NULL, order.by.spcor = FALSE, sort.spcor.by.var = FALSE, ... )
object |
Seurat object |
assay |
Assay Name of Assay NMF is being run on |
slot |
Slot to pull data from. |
features |
Features to compute the NMF for. Note that these features must be present in the slot used to compute the NMF. By default, the 'features' is set to 'VariableFeatures(object)' to include the most variable features selected in the normalization step. |
nfactors |
Total Number of factors to compute and store (20 by default) |
rescale |
Rescale data to make sure that values of the input matrix are non-n |
reduction.name |
Dimensional reduction name, "NMF" by default |
reduction.key |
Dimensional reduction key, specifies the prefix of the factor ids, e.g. "factor_1", "factor_2", etc. |
n.cores |
Number of threads to use in computation |
order.by.spcor |
Order factors by spatial correlation |
sort.spcor.by.var |
Sort factors by decreasing variance |
... |
Additional parameters |
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