Description Usage Arguments Details Value
View source: R/GeneSelection.R
Identifies genes that are outliers on a 'mean variability plot'. First, calculates average expression and dispersion for each gene. Next, divides genes into num.bin (deafult 20) bins based on their average expression, and calculates z-scores for dispersion within each bin. The purpose of this is to identify variable genes while controlling for the strong relationship between variability and average expression.
1 2 3 | seurat_variable_genes(ExprMatrix, bin.size = 1000, x.low.cutoff = 0.1,
x.high.cutoff = 8, y.cutoff = 1, y.high.cutoff = Inf,
num.bin = 20)
|
ExprMatrix |
cells x genes expression matrix to be normalized |
bin.size |
The bin size to use for the gene histogram |
x.low.cutoff |
Bottom cutoff on x-axis for identifying variable genes |
x.high.cutoff |
Top cutoff on x-axis for identifying variable genes |
y.cutoff |
Bottom cutoff on y-axis for identifying variable genes |
y.high.cutoff |
Top cutoff on y-axis for identifying variable genes |
num.bin |
Total number of bins to use in the scaled analysis (default is 20) |
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 genes 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.
ScaffoldTre object with the structure and connectivity of the Scaffold Tree
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