setGexpMats: Functions related to gene expression model Set gene...

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

Description

Functions related to gene expression model Set gene expression matrices, normalizes, and maps genes to genomic coordinates

Usage

setGexpMats(gexp.sc.init, gexp.ref.init, mart.obj, filter = TRUE,
  minMeanBoth = 0, minMeanTest = mean(gexp.sc.init[gexp.sc.init != 0]),
  minMeanRef = mean(gexp.ref.init[gexp.ref.init != 0]), scale = TRUE,
  id = "hgnc_symbol", verbose = TRUE)

Arguments

gexp.sc.init	Single cell gene expression matrix
gexp.ref.init	Reference gene expression matrix such as from GTEX or a match normal
mart.obj	Biomart object used for mapping genes to genomic positions
filter	Boolean of whether or not to filter genes (default: TRUE)
minMeanBoth	Minimum mean gene expression in both the single cell and reference matrices (default: 4.5)
minMeanTest	Minimum mean gene expression for the single cell expression matrix (default: 6)
minMeanRef	Minimum mean gene expression for the reference expression matrix (default: 8)
scale	Boolean of whether or not to scale by library size (default: TRUE)
id	biomaRt ID for genes c(default: 'hgnc_symbol')
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)

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