Computer Science and Engineering Department, University of Connecticut, 371 Fairfield Way, Unit 4155, Storrs, 06269, USA.
Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Unit 3092, Storrs, USA.
BMC Genomics. 2020 Mar 5;21(Suppl 1):834. doi: 10.1186/s12864-019-6155-6.
Pathway analysis is one of the later stage data analysis steps essential in interpreting high-throughput gene expression data. We propose a set of algorithms which given gene expression data can recognize which portion of sub-pathways are actively utilized in the biological system being studied. The degree of activation is measured by conditional probability of the input expression data based on the Bayesian Network model constructed from the topological pathway.
We demonstrate the effectiveness of our pathway analysis method by conducting two case studies. The first one applies our method to a well-studied temporal microarray data set for the cell cycle using the KEGG Cell Cycle pathway. Our method closely reproduces the biological claims associated with the data sets, but unlike the original work ours can produce how pathway routes interact with each other above and beyond merely identifying which pathway routes are involved in the process. The second study applies the method to the p53 mutation microarray data to perform a comparative study.
We show that our method achieves comparable performance against all other pathway analysis systems included in this study in identifying p53 altered pathways. Our method could pave a new way of carrying out next generation pathway analysis.
通路分析是解释高通量基因表达数据的关键后期数据分析步骤之一。我们提出了一组算法，给定基因表达数据，可以识别出生物系统中哪些部分的子通路被积极利用。激活程度是通过基于拓扑通路构建的贝叶斯网络模型来测量输入表达数据的条件概率来衡量的。
我们通过两个案例研究证明了我们的通路分析方法的有效性。第一个案例将我们的方法应用于细胞周期的 KEGG 细胞周期通路的一个经过充分研究的时间微阵列数据集。我们的方法密切再现了与数据集相关的生物学主张，但与原始工作不同，我们的方法可以产生通路路线如何相互作用，而不仅仅是识别参与该过程的通路路线。第二个研究将该方法应用于 p53 突变微阵列数据进行比较研究。
我们表明，我们的方法在识别 p53 改变的途径方面与本研究中包含的所有其他途径分析系统的性能相当。我们的方法可以为下一代途径分析开辟新的途径。
