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 |
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... | Arguments passed to other methods |
selection.method | How to choose top variable features. Choose one of :
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loess.span | (vst method) Loess span parameter used when fitting the variance-mean 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 x-axis value (average expression). Default is to take the mean of the detected (i.e. non-zero) values |
dispersion.function | Function to compute y-axis 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:
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verbose | show progress bar for calculations |
nfeatures | Number of features to select as top variable features; only used when |
mean.cutoff | A two-length numeric vector with low- and high-cutoffs for feature means |
dispersion.cutoff | A two-length numeric vector with low- and high-cutoffs 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 X-axis function is the mean expression level, and for Y-axis it is the log(Variance/mean). All mean/variance calculations are not performed in log-space, but the results are reported in log-space - see relevant functions for exact details.