RunNMF: Run Non-negative Matrix Factorization

RunNMFR Documentation

Run Non-negative Matrix Factorization

Description

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).

Usage

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,
  ...
)

Arguments

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


jbergenstrahle/STUtility documentation built on March 14, 2023, 7:15 a.m.