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Seurat (version 2.3.4)

AddModuleScore: Calculate module scores for gene expression programs in single cells

Description

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

Usage

AddModuleScore(object, genes.list = NULL, genes.pool = NULL, n.bin = 25,
  seed.use = 1, ctrl.size = 100, use.k = FALSE, enrich.name = "Cluster",
  random.seed = 1)

Arguments

object

Seurat object

genes.list

Gene expression programs in list

genes.pool

List of genes to check expression levels agains, defaults to rownames(x = object@data)

n.bin

Number of bins of aggregate expression levels for all analyzed genes

seed.use

Random seed for sampling

ctrl.size

Number of control genes selected from the same bin per analyzed gene

use.k

Use gene clusters returned from DoKMeans()

enrich.name

Name for the expression programs

random.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

Run this code
# NOT RUN {
cd_genes <- list(c(
  'CD79B',
  'CD79A',
  'CD19',
  'CD180',
  'CD200',
  'CD3D',
  'CD2',
  'CD3E',
  'CD7',
  'CD8A',
  'CD14',
  'CD1C',
  'CD68',
  'CD9',
  'CD247'
))
pbmc_small <- AddModuleScore(
  object = pbmc_small,
  genes.list = cd_genes,
  ctrl.size = 5,
  enrich.name = 'CD_Genes'
)
head(x = pbmc_small@meta.data)

# }

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