R/utilities.R
AddModuleScore.RdCalculate 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",
...
)Seurat object
A list of vectors of features for expression programs; each entry should be a vector of feature names
List of features to check expression levels against, defaults to
rownames(x = object)
Number of bins of aggregate expression levels for all analyzed features
Number of control features selected from the same bin per analyzed feature
Use feature clusters returned from DoKMeans
Name of assay to use
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)
Set a random seed. If NULL, seed is not set.
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 to calculate score values off of. Defaults to data slot (i.e log-normalized counts)
Extra parameters passed to UpdateSymbolList
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)
if (FALSE) {
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[])
}