AddModuleScore: Calculate module scores for feature expression programs in...

In lambdamoses/SeuratBasics: Slimmed down version of Seurat

Description

Calculate the average expression levels of each program (cluster) on single cell level, subtracted by the aggregated expression of control feature sets. All analyzed features are binned based on averaged expression, and the control features are randomly selected from each bin.

Usage

AddModuleScore(
  object,
  features,
  pool = NULL,
  nbin = 24,
  ctrl = 100,
  k = FALSE,
  assay = NULL,
  name = "Cluster",
  seed = 1
)

Arguments

object	Seurat object
features	Feature expression programs in list
pool	List of features to check expression levels agains, defaults to rownames(x = object)
nbin	Number of bins of aggregate expression levels for all analyzed features
ctrl	Number of control features selected from the same bin per analyzed feature
k	Use feature clusters returned from DoKMeans
assay	Name of assay to use
name	Name for the expression programs
seed	Set a random seed. If NULL, seed is not set.

Value

Returns a Seurat object with module scores added to object meta data

References

Tirosh et al, Science (2016)

Examples

## Not run: 
cd_features <- list(c(
  'CD79B',
  'CD79A',
  'CD19',
  'CD180',
  'CD200',
  'CD3D',
  'CD2',
  'CD3E',
  'CD7',
  'CD8A',
  'CD14',
  'CD1C',
  'CD68',
  'CD9',
  'CD247'
))
pbmc_small <- AddModuleScore(
  object = pbmc_small,
  features = cd_features,
  ctrl = 5,
  name = 'CD_Features'
)
head(x = pbmc_small[])

## End(Not run)

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