Identifies features that are outliers on a 'mean variability plot'.
FindVariableFeatures(object, ...) # S3 method for default FindVariableFeatures( object, selection.method = "vst", loess.span = 0.3, clip.max = "auto", mean.function = FastExpMean, dispersion.function = FastLogVMR, num.bin = 20, binning.method = "equal_width", verbose = TRUE, ... ) # S3 method for Assay FindVariableFeatures( object, selection.method = "vst", loess.span = 0.3, clip.max = "auto", mean.function = FastExpMean, dispersion.function = FastLogVMR, num.bin = 20, binning.method = "equal_width", nfeatures = 2000, mean.cutoff = c(0.1, 8), dispersion.cutoff = c(1, Inf), verbose = TRUE, ... ) # S3 method for SCTAssay FindVariableFeatures(object, nfeatures = 2000, ...) # S3 method for Seurat FindVariableFeatures( object, assay = NULL, selection.method = "vst", loess.span = 0.3, clip.max = "auto", mean.function = FastExpMean, dispersion.function = FastLogVMR, num.bin = 20, binning.method = "equal_width", nfeatures = 2000, mean.cutoff = c(0.1, 8), dispersion.cutoff = c(1, Inf), verbose = TRUE, ... )
object  An object 

...  Arguments passed to other methods 
selection.method  How to choose top variable features. Choose one of :

loess.span  (vst method) Loess span parameter used when fitting the variancemean relationship 
clip.max  (vst method) 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 
mean.function  Function to compute xaxis value (average expression). Default is to take the mean of the detected (i.e. nonzero) values 
dispersion.function  Function to compute yaxis value (dispersion). Default is to take the standard deviation of all values 
num.bin  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  show progress bar for calculations 
nfeatures  Number of features to select as top variable features; only used when 
mean.cutoff  A twolength numeric vector with low and highcutoffs for feature means 
dispersion.cutoff  A twolength numeric vector with low and highcutoffs for feature dispersions 
assay  Assay to use 
For the mean.var.plot method: Exact parameter settings may vary empirically from dataset to dataset, and based on visual inspection of the plot. Setting the y.cutoff parameter to 2 identifies features that are more than two standard deviations away from the average dispersion within a bin. The default Xaxis function is the mean expression level, and for Yaxis it is the log(Variance/mean). All mean/variance calculations are not performed in logspace, but the results are reported in logspace  see relevant functions for exact details.