Seurat (version 2.2.0)

MASTDETest: Differential expression using MAST

Description

Identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST package to run the DE testing.

Usage

MASTDETest(object, cells.1, cells.2, min.cells = 3, genes.use = NULL,
  latent.vars = NULL, assay.type = "RNA", ...)

Arguments

object

Seurat object

cells.1

Group 1 cells

cells.2

Group 2 cells

min.cells

Minimum number of cells expressing the gene in at least one of the two groups

genes.use

Genes to use for test

latent.vars

Confounding variables to adjust for in DE test. Default is "nUMI", which adjusts for cellular depth (i.e. cellular detection rate). For non-UMI based data, set to nGene instead.

assay.type

Type of assay to fetch data for (default is RNA)

…

Additional parameters to zero-inflated regression (zlm) function in MAST

Value

Returns a p-value ranked matrix of putative differentially expressed genes.

Details

To use this method, please install MAST, using instructions at https://github.com/RGLab/MAST/

References

Andrew McDavid, Greg Finak and Masanao Yajima (2017). MAST: Model-based Analysis of Single Cell Transcriptomics. R package version 1.2.1. https://github.com/RGLab/MAST/

Examples

# NOT RUN {
  pbmc_small
  MASTDETest(pbmc_small, cells.1 = WhichCells(object = pbmc_small, ident = 1),
              cells.2 = WhichCells(object = pbmc_small, ident = 2))
# }
# NOT RUN {
# }