DEG (Gene Expression Markers) — degGeneExpressionMarkers • SCWorkflow

This function performs a DEG (differential expression of genes) analysis on a merged Seurat object to identify expression markers between different groups of cells (contrasts).

degGeneExpressionMarkers(
  object,
  samples,
  contrasts,
  parameter.to.test = "orig_ident",
  test.to.use = "MAST",
  log.fc.threshold = 0.25,
  use.spark = FALSE,
  assay.to.use = "SCT"
)

Arguments

object: Seurat-class object
samples: Samples to be included in the analysis
contrasts: Contrasts in the "A-B" format
parameter.to.test: Select the metadata column that you would like to use to perform your DEG analysis and construct your contrasts from. Default is "orig_ident"
test.to.use: The kind of algorithm you would like to use to perform your DEG analysis. Default is the MAST algorithm (wilcox,bimod,roc,t,negbinom,poisson,LR,MAST,DESeq2).
log.fc.threshold: The minimum log fold-change between contrasts that you would like to analyze. Default is 0.25
use.spark: Opt to use Spark to parallelize computations. Default is FALSE
assay.to.use: The assay to use for your DEG analysis. Default is SCT, but can use linearly scaled data by selecting RNA instead

Value

a dataframe with DEG.

Details

The recommended input is a merged Seurat object with SingleR annotations, along with its associated sample names and metadata