multiNMF {GeneNMF}

Run NMF on a list of Seurat objects

Description

Given a list of Seurat objects, run non-negative matrix factorization on each sample individually, over a range of target NMF components (k).

Usage

multiNMF(
  obj.list,
  assay = "RNA",
  slot = "data",
  k = 5:6,
  hvg = NULL,
  nfeatures = 2000,
  L1 = c(0, 0),
  min.exp = 0.01,
  max.exp = 3,
  center = FALSE,
  scale = FALSE,
  min.cells.per.sample = 10,
  hvg.blocklist = NULL,
  seed = 123
)

Arguments

obj.list

A list of Seurat objects

assay

Get data matrix from this assay

slot

Get data matrix from this slot (=layer)

k

Number of target components for NMF (can be a vector)

hvg

List of pre-calculated variable genes to subset the matrix. If hvg=NULL it calculates them automatically

nfeatures

Number of HVG, if calculate_hvg=TRUE

L1

L1 regularization term for NMF

min.exp

Minimum average log-expression value for retaining genes

max.exp

Maximum average log-expression value for retaining genes

center

Whether to center the data matrix

scale

Whether to scale the data matrix

min.cells.per.sample

Minimum numer of cells per sample (smaller samples will be ignored)

hvg.blocklist

Optionally takes a vector or list of vectors of gene names. These genes will be ignored for HVG detection. This is useful to mitigateeffect of genes associated with technical artifacts and batch effects (e.g. mitochondrial), and to exclude TCR and BCR adaptive immune(clone-specific) receptors. If set to 'NULL' no genes will be excluded

seed

Random seed

Value

Returns a list of NMF programs, one for each sample and for each value of 'k'. The format of each program in the list follosw the structure of nmf factorization models.

Examples

library(Seurat)
data(sampleObj)
geneNMF_programs <- multiNMF(list(sampleObj), k=5)