IEEE/ACM Trans Comput Biol Bioinform. 2021 Nov-Dec;18(6):2659-2670. doi: 10.1109/TCBB.2020.2980816. Epub 2021 Dec 8.
To understand the underlying biological mechanisms of gene expression data, it is important to discover the groups of genes that have similar expression patterns under certain subsets of conditions. Biclustering algorithms have been effective in analyzing large-scale gene expression data. Recently, traditional biclustering has been improved by introducing biological knowledge along with the expression data during the biclustering process. In this paper, we propose the Pathway-based Order Preserving Biclustering (POPBic) algorithm by incorporating Kyoto Encyclopedia of Genes and Genomes (KEGG) based on the hypothesis that two genes sharing similar pathways are likely to be similar. The basic principle of the POPBic approach is to apply the concept of Longest Common Subsequence between a pair of genes which have a high number of common pathways. The algorithm identifies the expression patterns from data using two major steps: (i) selection of significant seed genes and (ii) extraction of biclusters. We performe exhaustive experimentation with the POPBic algorithm using synthetic dataset to evaluate the bicluster model, finding its robustness in the presence of noise and identifying overlapping biclusters. We demonstrate that POPBic is able to discover biologically significant biclusters for four cancer microarray gene expression datasets. POPBic has been found to perform consistently well in comparison to its closest competitors.
为了理解基因表达数据的潜在生物学机制，发现具有相似表达模式的基因在某些条件子集下的分组非常重要。双聚类算法在分析大规模基因表达数据方面非常有效。最近，通过在双聚类过程中引入生物学知识以及表达数据，对传统的双聚类进行了改进。在本文中，我们提出了基于通路的有序保留双聚类（POPBic）算法，其假设是具有相似通路的两个基因可能相似，这是基于京都基因与基因组百科全书（KEGG）的。POPBic 方法的基本原理是应用一对具有大量共同通路的基因之间的最长公共序列的概念。该算法使用两个主要步骤从数据中识别表达模式：（i）选择重要的种子基因，（ii）提取双聚类。我们使用合成数据集对 POPBic 算法进行了详尽的实验，以评估双聚类模型，发现其在噪声存在下的稳健性，并识别重叠的双聚类。我们证明了 POPBic 能够发现四个癌症微阵列基因表达数据集的具有生物学意义的双聚类。与最接近的竞争对手相比，POPBic 的性能一直很稳定。
