AddModuleScore | R Documentation |
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.
AddModuleScore(
object,
features,
pool = NULL,
nbin = 24,
ctrl = 100,
k = FALSE,
assay = NULL,
name = "Cluster",
seed = 1,
search = FALSE,
slot = "data",
...
)
object |
Seurat object |
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
|
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 |
seed |
Set a random seed. If NULL, seed is not set. |
search |
Search for symbol synonyms for features in |
slot |
Slot to calculate score values off of. Defaults to data slot (i.e log-normalized counts) |
... |
Extra parameters passed to |
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
Tirosh et al, Science (2016)
## Not run:
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[])
## End(Not run)
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