An R package for estimating metabolite concentrations from Nuclear Magnetic Resonance spectral data using a specialised MCMC algorithm. BATMAN deconvolutes peaks from 1-dimensional NMR spectra, automatically assigns them to specific metabolites from a target list and obtains concentration estimates. The Bayesian model incorporates information on characteristic peak patterns of metabolites and is able to account for shifts in the position of peaks commonly seen in NMR spectra of biological samples. It applies a Markov Chain Monte Carlo (MCMC) algorithm to sample from a joint posterior distribution of the model parameters and obtains concentration estimates with reduced error compared with conventional numerical integration and comparable to manual deconvolution by experienced spectroscopists.
Provides a method that supports fully automated and quantitative nuclear magnetic resonance (NMR)-based metabolomics of complex mixtures. Bayesil was developed to divide the spectrum into small blocks and represents the sparse dependencies between these blocks. It then performs approximate inference over this model as a surrogate for spectral profiling, yielding the most probable metabolic profile.
Provides several tools for the analysis of nuclear magnetic resonance (NMR), mass spectrometry (MS), optical and chromatographic data in one interface. Spectrus Processor can process spectra and generate journal-formatted multiplet reports. It can create crisp simulated spectra to produce problem sets. It is compatible with most of data formats from major instrument vendors.