AverageExpression: Averaged feature expression by identity class

In lambdamoses/SeuratBasics: Slimmed down version of Seurat

Description

Returns expression for an 'average' single cell in each identity class

Usage

AverageExpression(
  object,
  assays = NULL,
  features = NULL,
  return.seurat = FALSE,
  add.ident = NULL,
  slot = "data",
  use.scale = FALSE,
  use.counts = FALSE,
  verbose = TRUE,
  ...
)

Arguments

object	Seurat object
assays	Which assays to use. Default is all assays
features	Features to analyze. Default is all features in the assay
return.seurat	Whether to return the data as a Seurat object. Default is FALSE
add.ident	Place an additional label on each cell prior to averaging (very useful if you want to observe cluster averages, separated by replicate, for example)
slot	Slot to use; will be overriden by use.scale and use.counts
use.scale	Use scaled values for feature expression
use.counts	Use count values for feature expression
verbose	Print messages and show progress bar
...	Arguments to be passed to methods such as CreateSeuratObject

Details

Output is in log-space when return.seurat = TRUE, otherwise it's in non-log space. Averaging is done in non-log space.

Value

Returns a matrix with genes as rows, identity classes as columns. If return.seurat is TRUE, returns an object of class Seurat.

Examples

head(AverageExpression(object = pbmc_small))

lambdamoses/SeuratBasics documentation built on May 6, 2020, 9:32 a.m.