Bensmail Halima, Golek Jennifer, Moody Michelle M, Semmes John O, Haoudi Abdelali
Department of Statistics, University of Tennessee, 334 Stokely Management Building, Knoxville, TN 37996-0532, USA.
Bioinformatics. 2005 May 15;21(10):2210-24. doi: 10.1093/bioinformatics/bti383. Epub 2005 Mar 15.
Bioinformatics clustering tools are useful at all levels of proteomic data analysis. Proteomics studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. The high dimensionality of data generated from these studies requires the development of improved bioinformatics tools for efficient and accurate data analyses. For proteome profiling of a particular system or organism, a number of specialized software tools are needed. Indeed, significant advances in the informatics and software tools necessary to support the analysis and management of these massive amounts of data are needed. Clustering algorithms based on probabilistic and Bayesian models provide an alternative to heuristic algorithms. The number of clusters (diseased and non-diseased groups) is reduced to the choice of the number of components of a mixture of underlying probability. The Bayesian approach is a tool for including information from the data to the analysis. It offers an estimation of the uncertainties of the data and the parameters involved.
We present novel algorithms that can organize, cluster and derive meaningful patterns of expression from large-scaled proteomics experiments. We processed raw data using a graphical-based algorithm by transforming it from a real space data-expression to a complex space data-expression using discrete Fourier transformation; then we used a thresholding approach to denoise and reduce the length of each spectrum. Bayesian clustering was applied to the reconstructed data. In comparison with several other algorithms used in this study including K-means, (Kohonen self-organizing map (SOM), and linear discriminant analysis, the Bayesian-Fourier model-based approach displayed superior performances consistently, in selecting the correct model and the number of clusters, thus providing a novel approach for accurate diagnosis of the disease. Using this approach, we were able to successfully denoise proteomic spectra and reach up to a 99% total reduction of the number of peaks compared to the original data. In addition, the Bayesian-based approach generated a better classification rate in comparison with other classification algorithms. This new finding will allow us to apply the Fourier transformation for the selection of the protein profile for each sample, and to develop a novel bioinformatic strategy based on Bayesian clustering for biomarker discovery and optimal diagnosis.
生物信息学聚类工具在蛋白质组数据分析的各个层面都很有用。蛋白质组学研究能够提供丰富的信息，并通过对生物样本的分析迅速生成大量数据。这些研究产生的数据具有高维度性，这就需要开发改进的生物信息学工具来进行高效且准确的数据分析。对于特定系统或生物体的蛋白质组分析，需要许多专门的软件工具。实际上，支持对这些海量数据进行分析和管理所需的信息学和软件工具需要取得重大进展。基于概率和贝叶斯模型的聚类算法为启发式算法提供了一种替代方案。聚类的数量（患病组和非患病组）可归结为对潜在概率混合的成分数量的选择。贝叶斯方法是一种将数据中的信息纳入分析的工具。它能对数据及相关参数的不确定性进行估计。
我们提出了新的算法，这些算法能够对大规模蛋白质组学实验的数据进行组织、聚类并得出有意义的表达模式。我们使用基于图形的算法处理原始数据，通过离散傅里叶变换将其从实空间数据表达转换为复空间数据表达；然后我们采用阈值化方法进行去噪并缩短每个谱的长度。将贝叶斯聚类应用于重构后的数据。与本研究中使用的其他几种算法（包括K均值算法、（科霍宁自组织映射（SOM））和线性判别分析）相比，基于贝叶斯 - 傅里叶模型的方法在选择正确模型和聚类数量方面始终表现出卓越的性能，从而为疾病的准确诊断提供了一种新方法。使用这种方法，我们能够成功地对蛋白质组谱进行去噪，与原始数据相比，峰数量最多可减少99%。此外，与其他分类算法相比，基于贝叶斯的方法产生了更高的分类率。这一新发现将使我们能够应用傅里叶变换来选择每个样本的蛋白质谱，并基于贝叶斯聚类开发一种新的生物信息学策略用于生物标志物发现和优化诊断。
