Bayesian Methods to detect dye labelled DNA oligonucleotides in multiplexed raman spectra

Zhong, M., Girolami, M., Faulds, K. and Graham, D. (2011) Bayesian Methods to detect dye labelled DNA oligonucleotides in multiplexed raman spectra. Journal of the Royal Statistical Society: Series C (Applied Statistics), 60(2), pp. 187-206. (doi: 10.1111/j.1467-9876.2010.00744.x)

Abstract

Recent advances in the development of technology based on Raman scattering as a chemical analytical technique have made it possible to quantitatively detect spectral mixtures of multiple DNA sequences. However, to fully exploit these techniques inferential methodologies are required which can deconvolute the observed mixture and infer the composition of distinct DNA sequences present in the overall composite. Inferring the spectral decomposition is posed as a model selection problem for a bilinear statistical model, and the required Markov chain Monte Carlo inferential methodology is developed. In particular a Gibbs sampler and Reversible Jump Markov chain Monte Carlo methods are presented along with techniques based on estimation of the marginal likelihood. The results reported in this paper are particularly encouraging highlighting that for multiplexed Raman spectra, inference of the composition of original sequences present in the mixture is possible to acceptable levels of accuracy. This statistical methodology makes the exploitation of multiplexed surface enhanced resonance Raman scattering spectra in disease identification a reality. A website containing supplementary material, the spectral data used in the paper as well as Matlab scripts implementing the proposed statistical methods is available at http://www.dcs.gla.ac.uk/inference/SERRS

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhong, Dr Mingjun and Graham, Professor Duncan and Girolami, Prof Mark
• Authors: Zhong, M., Girolami, M., Faulds, K., and Graham, D.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Journal of the Royal Statistical Society: Series C (Applied Statistics)
• ISSN: 0035-9254
• ISSN (Online): 1467-9876
• Published Online: 13 January 2011