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

In atakanekiz/Seurat3.0: Tools for Single Cell Genomics

Description

Calculate the average expression levels of each program (cluster) on single cell level, subtracted by the aggregated expression of control featre sets. All analyzed featres are binned based on averaged expression, and the control featres 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	Featre 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

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)

atakanekiz/Seurat3.0 documentation built on May 26, 2019, 2:33 a.m.