setGexpDev: Set needed absolute gene expression deviance to be able to...

In JEFworks/HoneyBADGER: HMM-integrated Bayesian approach for detecting CNV and LOH events using single-cell RNA-seq data

Description

Set needed absolute gene expression deviance to be able to distinguish neutral from amplified or deletion regions

Usage

setGexpDev(gexp.norm, alpha = 0.25, n = 100, seed = 0, plot = FALSE,
  verbose = FALSE)

Arguments

gexp.norm	Normalized gene expression matrix
alpha	Alpha level (default: 0.05)
n	Number of repeats for estimating parameter (default: 100)
seed	Random seed
plot	Plotting
verbose	Verbosity

Examples

data(gexp)
data(ref)
require(biomaRt) ## for gene coordinates
mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
    dataset = 'hsapiens_gene_ensembl', 
    host = "jul2015.archive.ensembl.org")
gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
dev <- setGexpDev(gexp.mats$gexp.norm)

JEFworks/HoneyBADGER documentation built on July 24, 2021, 3:01 p.m.