calcGexpCnvProb: Run BADGER expression-only model to assess posterior...

In JEFworks/badger: Bayesian approach for detecting copy number alterations from single cell RNA-seq data

Description

Run BADGER expression-only model to assess posterior probability of CNVs given normalized expression data only

Usage

calcGexpCnvProb(gexp, fits, m, region, gos, quiet = TRUE)

Arguments

gexp	Normalized gene expression matrix
fits	Fit for variance around mean
m	Expected mean deviation due to copy number change
region	Region of interest such as expected CNV boundaries
gos	Gene position table
quiet	Boolean for whether to suppress progress display

Value

List of posterior probabilities for amplification and deletion

Examples

data('MM16.counts')
mat <- log2(MM16.counts + 1)
data('Normal.counts')
mat.ref <- log2(Normal.counts + 1)
mats <- normalizedExpression(mat, mat.ref)
gexp <- mats[[1]]
fits <- mvFit(gexp)
region <- data.frame('chr'=1, start=0, end=1e9)
set.seed(0)
library(biomaRt) ## for gene coordinates
mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
gos <- getBM(values=rownames(gexp),attributes=c("ensembl_gene_id","chromosome_name","start_position","end_position"),filters=c("ensembl_gene_id"),mart=mart.obj)
gos$pos <- (gos$start_position + gos$end_position)/2
results <- calcGexpCnvProb(gexp, fits, 0.15, region, gos)

JEFworks/badger documentation built on May 7, 2019, 7:40 a.m.