calcGexpCnvBoundaries: HMM to identify CNV boundaries using normalized gene...

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

Description

HMM to identify CNV boundaries using normalized gene expression data

Usage

calcGexpCnvBoundaries(gexp.norm, genes, m = 0.15, chrs = paste0("chr",
  c(1:22)), min.traverse = 3, t = 1e-06, min.num.genes = 3, trim = 0.1,
  verbose = FALSE)

Arguments

gexp.norm	Normalized gene expression matrix
genes	GRanges annotation of gene names and coordinates
m	Expression magnitude deviation needed to distinguish CNV from neutral
chrs	List of chromosome names. Genes not mapping to these chromosomes will be excluded. Default autosomes only: paste0('chr', c(1:22))
min.traverse	Depth traversal to look for subclonal CNVs. Higher depth, potentially smaller subclones detectable. (default: 2)
t	HMM transition parameter. Higher number, more transitions. (default: 1e-6)
min.num.genes	Minimum number of genes within a CNV. (default: 3)
trim	Trim boundary SNPs
verbose	Verbosity (default: FALSE)

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