seurat_variable_genes: Seurat variable genes, according to the Seurat's package

In soedinglab/merlot: MERLoT

Description

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.

Usage

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)

Arguments

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)

Details

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.

Value

ScaffoldTre object with the structure and connectivity of the Scaffold Tree

soedinglab/merlot documentation built on June 28, 2020, 9:36 a.m.