A novel R-package graphic user interface for the analysis of metabonomic profiles

Abstract

Background: Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries.

HRMAS-nuclear magnetic resonance spectroscopy characterization of tomato “flavor varieties” from Almería (Spain)

“…The NMR descriptors used in the multivariate analysis were generated by binning each spectrum, in the 0.8-5.5 ppm range, with a bin width of 0.02 ppm using the "Metabonomic" R package (Izquierdo-García et al, 2009). The spectral region of 4.70-5.05 ppm was excluded to avoid variability due to water suppression and cross-saturation effects.…”

A metabolomic approach for diagnosis of experimental sepsis

“…Similarly, the chemical shift region from 0 to 0.04 ppm containing the internal reference (TSP) was excluded from the statistical analyzes. Baseline correction (Rocke and Xi method [28]) was performed automatically using the ''baseline correction'' tool of the ''Metabonomic'' R package [29]. The ''Metabonomic'' package is a graphical environment for metabolomic analysis developed and maintained by our group in the public-domain R framework (rel.…”

A pilot study of the onset of hepatic encephalopathy (OHE) in mice induced by thioacetamide and the protective effect of taurine by holistic metabolic characterization

“…With the removal of signals of water and its neighboring regions (4.6-5.58 ppm), the NMR spectra were aligned by a peak alignment graphical application (Pre-processing/Peak Alignment) in R, a freely available, open-source software (R Development Core Team, http://cran.r-project.org/) using PROcess package (Izquierdo-García et al 2009) and regions between 0.7 and 9.4 ppm were binned using an adaptive binning approach based on the code (Anderson et al 2011) implemented in Matlab (version 7.3, MathWorks, Natick, MA, USA). The binned data were probabilistic quotient normalized (Dieterle et al 2006), mean-centered and Pareto scaled in R prior to multivariate statistical analysis (the code was attached in supporting information).…”