calcAlleleCnvProb: Run BADGER allele-only model to assess posterior probability...

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

Description

Run BADGER allele-only model to assess posterior probability of CNVs given allele information only

Usage

calcAlleleCnvProb(r, cov.sc, l, cov.bulk, region, gtf, mono = 0.7,
  pe = 0.01, filter = TRUE, likelihood = FALSE, n.iter = 1000,
  quiet = TRUE, delim = ":")

Arguments

r	Matrix of alt allele count in single cells
cov.sc	Matrix of coverage in single cells
l	Vector of alt allele count in bulk
cov.bulk	Vector of coverage in bulk
region	Region of interest such as expected CNV boundaries
gtf	GTF file contents for mapping SNPs to genes
mono	Rate of mono-allelic expression. Default: 0.7
pe	Effective error rate to capture error from sequencing, etc. Default: 0.01
filter	Boolean for whether to filter out SNP sites with no coverage. Default: TRUE
likelihood	Boolean for whether to use likelihood based estimate of posterior. Default: FALSE
n.iter	Number of iterations in MCMC. Default: 1000
quiet	Boolean of whether to suppress progress bar. Default: TRUE
delim	Delimiter for names of SNPs as Chromosome[delim]Position. Default: ":" ex. chr1:283838897

Value

Posterior probability of deletion or LOH

Examples

## Single cell data
data(snpsHet_MM16ScSample)
## Bulk exome
data(snpsHet_MM16BulkSample)
## Not run: 
gtfFile <- 'data-raw/Homo_sapiens.GRCh37.75.gtf'
gtf <- read.table(gtfFile, header=F, stringsAsFactors=F, sep='\t')

## End(Not run)
region <- data.frame('chr'=2, start=0, end=1e9) # deletion region
## Not run: 
results <- calcAlleleCnvProb(r, cov.sc, l, cov.bulk, region, gtf)

## End(Not run)
region <- data.frame('chr'=3, start=0, end=1e9) # neutral region
## Not run: 
results <- calcAlleleCnvProb(r, cov.sc, l, cov.bulk, region, gtf)

## End(Not run)

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