Emanuele Vincent A, Gurbaxani Brian M
Chronic Viral Diseases Branch, Nation Center for Zoonotic, Vector-born and Enteric Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Proteomics. 2009 Apr;9(7):1754-62. doi: 10.1002/pmic.200701171.
SELDI protein profiling experiments can be used as a first step in studying the pathogenesis of various diseases such as cancer. There are a plethora of software packages available for doing the preprocessing of SELDI data, each with many options and written from different signal processing perspectives, offering many researchers choices they may not have the background or desire to make. Moreover, several studies have shown that mistakes in the preprocessing of the data can bias the biological interpretation of the study. For this reason, we conduct a large scale evaluation of available signal processing techniques to establish which are most effective. We use data generated from a standard, published simulation engine so that "truth" is known. We select the top algorithms by considering two logical performance metrics, and give our recommendations for research directions that are likely to be most promising. There is considerable opportunity for future contributions improving the signal processing of SELDI spectra.
表面增强激光解吸电离飞行时间质谱（SELDI）蛋白质谱分析实验可作为研究各种疾病（如癌症）发病机制的第一步。有大量软件包可用于SELDI数据的预处理，每个软件包都有许多选项，并且是从不同的信号处理角度编写的，这给许多研究人员提供了他们可能没有背景知识或意愿去做出的选择。此外，多项研究表明，数据预处理中的错误会使研究的生物学解释产生偏差。因此，我们对可用的信号处理技术进行了大规模评估，以确定哪些技术最有效。我们使用从一个标准的、已发表的模拟引擎生成的数据，以便“真相”是已知的。我们通过考虑两个逻辑性能指标来选择顶级算法，并为最有前景的研究方向给出我们的建议。未来在改进SELDI光谱信号处理方面有相当大的贡献机会。
