Bayesian Quantification
 

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Bayesian Quantification (BQuant) is an R package using a probabilistic approach for fully automated database-based identification and quantification of metabolites in local regions of 1H Nuclear Magnetic Resonance (NMR) spectra. The approach is based on a linear mixed model, which accounts for technological characteristics of NMR spectra, and represents the spectra as mixtures of reference profiles from a database. Identities and abundances of metabolites in the spectra are then inferred by Bayesian model selection, implemented in an efficient Gibbs sampling scheme.

The package provides an easy specification of a set of local region of NMR spectra and a database of reference spectra as input. It outputs a list of identified metabolites associated with their inclusion probability and their measures of abundances in each spectrum. This package be used by researchers with a limited background in statistics and minimal programming experience.

The beta version of the package is currently available and can be downloaded from Installation.

Authors

The methods implemented in the package were jointly developed by:

  • Cheng Zheng
  • Dr. Olga Vitek (Departments of Statistics and of Computer Science, Purdue University)

The maintainer of the package is Cheng Zheng. For comments or suggestions regarding the package, please send an e-mail to Cheng Zheng at zhengcheng@gmail.com or Dr. Olga Vitek at ovitek@purdue.edu.

References

C. Zheng, S. Zhang, S. Ragg, D. Raftery, O. Vitek. "Identification and quantification of metabolites in 1H NMR spectra by Bayesian model selection". Bioinformatics, 27, p. 1637-1644, 2011. [URL]