Children's Oncology Group, 440 E. Huntington Drive Suite 402, Arcadia, CA 91006, USA.
Anal Chim Acta. 2010 Jan 11;657(2):191-7. doi: 10.1016/j.aca.2009.10.043.
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.
许多用于蛋白质组学的现代技术（包括磁共振成像和质谱）的一个共同特点是，为实验中的每个对象生成大量数据。然而，从背景噪声中提取信号带来了重大挑战。信号提取的一个重要部分是正确识别数据的基线水平。在本文中，我们提出了一种新的基线估计算法（“BXR 算法”），该算法可直接应用于不同类型的光谱数据，也可专门针对不同的技术进行定制。然后，我们展示如何将该算法应用于一种特定的技术——基质辅助激光解吸/电离傅里叶变换离子回旋共振质谱，该技术作为蛋白质组学中的分析工具迅速流行起来。最后，我们将我们的算法与现有的基线估计算法的性能进行了比较。BXR 算法计算效率高，对许多现代应用中出现的单边信号类型具有鲁棒性（包括 NMR 和质谱），并且优于现有的基线估计算法。它被实现为 R 包 FTICRMS 中的函数 baseline，可从 Comprehensive R Archive Network（http://www.r-project.org/）或第一作者处获得。
