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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.
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)
Seurat object
Gene expression programs in list
List of genes to check expression levels agains, defaults to rownames(x = object@data)
Number of bins of aggregate expression levels for all analyzed genes
Random seed for sampling
Number of control genes selected from the same bin per analyzed gene
Use gene clusters returned from DoKMeans()
Name for the expression programs
Set a random seed
Returns a Seurat object with module scores added to object@meta.data
Tirosh et al, Science (2016)
# 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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