A common feature of many modern technologies used in proteomics - including nuclear magnetic resonance imaging and mass spectrometry - is the generation of large amounts of data for each subject in an experiment. Extracting the signal from the background noise, however, poses significant challenges. One important part of signal extraction is the correct identification of the baseline level of the data. In this article, we propose a new algorithm (the "BXR algorithm") for baseline estimation that can be directly applied to different types of spectroscopic data, but also can be specifically tailored to different technologies. We then show how to adapt the algorithm to a particular technology - matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry - which is rapidly gaining popularity as an analytic tool in proteomics. Finally, we compare the performance of our algorithm to that of existing algorithms for baseline estimation. The BXR algorithm is computationally efficient, robust to the type of one-sided signal that occurs in many modern applications (including NMR and mass spectrometry), and improves on existing baseline estimation algorithms. It is implemented as the function baseline in the R package FTICRMS, available either from the Comprehensive R Archive Network (http://www.r-project.org/) or from the first author.
- Baseline estimation
- Fourier transform ion cyclotron resonance
- Matrix-assisted laser desorption/ionization
ASJC Scopus subject areas
- Analytical Chemistry
- Environmental Chemistry