multiPCA {GeneNMF}

Run PCA on a list of Seurat objects

Description

Given a list of Seurat objects, run non-negative PCA factorization on each sample individually.

Usage

multiPCA(
  obj.list,
  assay = "RNA",
  slot = "data",
  k = 4:5,
  hvg = NULL,
  nfeatures = 500,
  min.exp = 0.01,
  max.exp = 3,
  min.cells.per.sample = 10,
  center = FALSE,
  scale = FALSE,
  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 PCA

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

min.exp

Minimum average log-expression value for retaining genes

max.exp

Maximum average log-expression value for retaining genes

min.cells.per.sample

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

center

Whether to center the data matrix

scale

Whether to scale the data matrix

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 non-negative PCA programs, one for each sample. The format of each program in the list follows the structure of nmf factorization models.

Examples

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