FindVariableFeaturesMix {mixhvg} | R Documentation |
FindVariableFeaturesMix
Description
FindVariableFeaturesMix
Usage
FindVariableFeaturesMix(
object,
method.names = c("scran", "scran_pos", "seuratv1"),
nfeatures = 2000,
loess.span = 0.3,
clip.max = "auto",
num.bin = 20,
binning.method = "equal_width",
verbose = FALSE
)
Arguments
object |
An object, SeuratObject and matrix(including sparse matrix) are both acceptable |
method.names |
The following methods can be directly used for highly variable feature selection. The mixture of methods take a vector of method list, e.g. c("scran","scran_pos","seuratv1"), which is also default.
|
nfeatures |
Number of features to select as top variable features. |
loess.span |
(Only work for logmv based methods like seuratv3). Loess span parameter used when fitting the variance-mean relationship |
clip.max |
(Only work for logmv based methods like seuratv3). After standardization values larger than clip.max will be set to clip.max; default is 'auto' which sets this value to the square root of the number of cells |
num.bin |
(Only work for logmv or dispersion based methods)Total number of bins to use in the scaled analysis (default is 20) |
binning.method |
Specifies how the bins should be computed. Available methods are:
|
verbose |
Whether to show progress bar for calculations. Default is FALSE. |
Details
The function inherits from FindVariableFeatures function of Seurat Package. Refer to https://github.com/RuzhangZhao/mixhvg for user manual.
Value
object: If the input is SeuratObject, the return is also SeuratObject; if the input is matrix(including sparse matrix), the return is the highly variable feature names.
Examples
if(0){
simple_matrix<-matrix(1:2e4,nrow=4000,ncol=5)
rownames(simple_matrix)<-1:nrow(simple_matrix)
colnames(simple_matrix)<-1:ncol(simple_matrix)
simple_matrix_HVG<-FindVariableFeaturesMix(simple_matrix)
}