Calculate module scores for feature expression programs in single cells

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, ...)

# S3 method for class 'Seurat'
AddModuleScore(
  object,
  features,
  pool = NULL,
  nbin = 24,
  ctrl = 100,
  k = FALSE,
  assay = NULL,
  name = "Cluster",
  seed = 1,
  search = FALSE,
  slot = "data",
  ...
)

# S3 method for class 'StdAssay'
AddModuleScore(
  object,
  features,
  kmeans.obj,
  pool = NULL,
  nbin = 24,
  ctrl = 100,
  k = FALSE,
  name = "Cluster",
  seed = 1,
  search = FALSE,
  slot = "data",
  ...
)

# S3 method for class 'Assay'
AddModuleScore(
  object,
  features,
  kmeans.obj,
  pool = NULL,
  nbin = 24,
  ctrl = 100,
  k = FALSE,
  name = "Cluster",
  seed = 1,
  search = FALSE,
  slot = "data",
  ...
)

Arguments

object: Seurat object
...: Extra parameters passed to UpdateSymbolList
features: A list of vectors of features for expression programs; each entry should be a vector of feature names
pool: List of features to check expression levels against, 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; will append a number to the end for each entry in features (eg. if features has three programs, the results will be stored as name1, name2, name3, respectively)
seed: Set a random seed. If NULL, seed is not set.
search: Search for symbol synonyms for features in features that don't match features in object? Searches the HGNC's gene names database; see UpdateSymbolList for more details
slot: Slot to calculate score values off of. Defaults to data slot (i.e log-normalized counts)
kmeans.obj: A DoKMeans output used to define feature clusters when k = TRUE; ignored if k = FALSE.

Value

Returns a Seurat object with module scores added to object meta data; each module is stored as name# for each module program present in features

References

Tirosh et al, Science (2016)

Examples

if (FALSE) { # \dontrun{
data("pbmc_small")
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[])
} # }