betaBinPosteriors: Beta binomial posteriors

In genomaths/MethylIT: Methylation Analysis Based on Signal Detection

Beta binomial posteriors

Description

Here, beta binomial posteriors are estimated in the context of the methylation level estimation. The direct use of counts for methylation level estimation is not recommended, since there is external source of noise coming from the sample manipulation, the sequencing machine and sequence alignment that could alter the counts and the number of methylated cytosines with low or zero counts (mC = 0) is generally high. The low counts and noise imply that read counts of methylated (mC) and non-methylated (uC) cytosines must not be used directly in the estimation of methylation levels.

In a Bayesian framework, methylated read counts are modeled by a beta- binomial distribution, which accounts for both, the biological and sampling variations (1-3). In our case we adopted the Bayesian approach suggested in reference (3)(Chapter 3). Naive distribution q (methylation levels). In a Bayesian framework with uniform priors, the methylation level can be defined as: meth_level = ( mC + 1 )/( mC + uC + 2 ). However, the most natural statistical model for replicated BS-seq DNA methylation measurements is beta-binomial (the beta distribution is a prior conjugate of binomial distribution), we consider the p parameter (methylation level) in the binomial distribution as randomly drawn from a beta distribution. The hyper-parameters alpha ('a') and beta ('b') from the beta-binomial distribution are interpreted as pseudo-counts.

Usage

.betaBinPosteriors(success, trials, a, b)

Arguments

success	number of successful events
trials	total number of events
a	previous number of successful events
b	previous number of unsuccessful events

Details

The posterior methylation levels are estimated as (a + success)/(a + b + trials), where 'a' and 'b' are the shape parameters of the beta distribution, alpha and beta parameters, respectively.

Value

a probability

References

1. Hebestreit K, Dugas M, Klein H-U (2013) Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29: 1647-1653. Available: http://www.ncbi.nlm.nih.gov/pubmed/23658421. 2. Robinson MD, Kahraman A, Law CW, Lindsay H, Nowicka M, et al. (2014) Statistical methods for detecting differentially methylated loci and regions. Front Genet 5: 324. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4165320/. Baldi P, Brunak S (2001) Bioinformatics: the machine learning approach. Second. Cambridge: MIT Press. 452 p.

Examples

MethylIT:::.betaBinPosteriors(2, 8, 2, 8)

genomaths/MethylIT documentation built on Feb. 3, 2024, 1:24 a.m.