calcGexpCnvProb: Calculate posterior probability of CNVs using normalized...

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

Description

Calculate posterior probability of CNVs using normalized expression data

Usage

calcGexpCnvProb(gexp.norm, genes, mvFit, m = 0.15, region = NULL,
  verbose = FALSE)

Arguments

gexp.norm	Normalized gene expression matrix.
genes	GRanges annotation of gene names and coordinates
mvFit	Mean variance fit
m	Expression deviation due to copy number change (default: 0.15)
region	Optional GenomicRanges region of interest such as expected CNV boundaries. (default: NULL)
verbose	Verbosity (default: FALSE)

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$gexp.norm)
results <- calcGexpCnvProb(gexp.mats$gexp.norm, gexp.mats$genes, 
    mvFit, region=GenomicRanges::GRanges('chr10', IRanges::IRanges(0,1e9)), verbose=TRUE)

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