Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Nikita Harvey, Beatriz Jiménez, Kazuhiro Sonomura, Taka-Aki Sato, Fumihiko Matsuda, Pierre Zalloua, Dominique Gauguier, Jeremy K. Nicholson and Marc-Emmanuel Dumas
Bioinformatics 35(11):1916–1922
Motivation Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra.
Results The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model buildingAvailability and implementation The algorithm is implemented using the MWASTools R/Bioconductor package.
