setMvFit: Model expected gene expression variance as a function of...

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

Description

Model expected gene expression variance as a function of number of genes

Usage

setMvFit(gexp.norm, num.genes = seq(5, 100, by = 5), rep = 50,
  plot = FALSE, verbose = TRUE)

Arguments

gexp.norm	Normalized gene expression matrix
num.genes	Number of random genes sampled (default: seq(5, 100, by=5))
rep	Number of repeats/resampling (default: 50)
plot	Whether to plot (default: FALSE)
verbose	Verbosity (default: TRUE)

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)
mvFit <- setMvFit(gexp.mats$gexp.norm)

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