normalizeVariance: Normalize gene expression variance relative to...

In JEFworks/MUDAN: Multi-sample Unified Discriminant ANalysis

Description

Normalizes gene expression magnitudes to with respect to its ratio to the transcriptome-wide expectation as determined by local regression on expression magnitude

Usage

normalizeVariance(counts, gam.k = 5, alpha = 0.05, plot = FALSE,
  use.unadjusted.pvals = FALSE, do.par = TRUE,
  max.adjusted.variance = 1000, min.adjusted.variance = 0.001,
  verbose = TRUE, details = FALSE)

Arguments

counts	Read count matrix. The rows correspond to genes, columns correspond to individual cells
gam.k	Generalized additive model parameter; the dimension of the basis used to represent the smooth term (default: 5)
alpha	Significance threshold (default: 0.05)
plot	Whether to plot the results (default: FALSE)
use.unadjusted.pvals	If true, will apply BH correction (default: FALSE)
do.par	Whether to adjust par for plotting if plotting (default: TRUE)
max.adjusted.variance	Ceiling on maximum variance after normalization to prevent infinites (default: 1e3)
min.adjusted.variance	Floor on minimum variance after normalization (default: 1e-3)
verbose	Verbosity (default: TRUE)
details	If true, will return data frame of normalization parameters. Else will return normalized matrix.(default: FALSE)

Value

If details is true, will return data frame of normalization parameters. Else will return normalized matrix.

Examples

{
data(pbmcA)
cd <- pbmcA[, 1:500]
mat <- cleanCounts(cd)
mat <- normalizeVariance(mat)
}

JEFworks/MUDAN documentation built on June 19, 2021, 6:46 a.m.