Choudhery Sanjeevani, Ioerger Thomas R
Department of Computer Science and Computer Engineering, Texas A&M University, College Station, TX, 77840.
ACM BCB. 2023 Sep;2023. doi: 10.1145/3584371.3612968. Epub 2023 Oct 4.
One of the challenges in RNA-Seq studies is finding subsets of genes that share a common mechanism of action or are associated with a regulon/pathway. Existing approaches often extract modules that reflect quantitative similarities (such as genes with correlated log-fold-changes) but do not adequately capture biological significance. In this work, we propose the Dual ICA methodology, which provides an way to extract "interacting modules" composed of sets of genes and conditions that exhibit strong associations. Dual ICA involves performing Independent Component Analysis (ICA) twice, once on the genes and once on the conditions. Using the resulting signal matrices, we extract respective sets of genes and conditions. The interaction between these sets is quantified using the coefficients from a linear regression and significance is determined through the Wald test and Z-score filtering. These coefficients are equivalent to the outer product of independent components obtained from the two signal matrices. Not only do the gene sets extracted align with known regulons, but the significant interacting modules they instantiate also encompass conditions that influence the expression of these regulons through shared mechanisms of action. Compared to traditional unsupervised clustering methods, Dual ICA demonstrates superior performance and provides explicit gene-condition sets for exploring functional relationships.
RNA测序研究中的挑战之一是找到具有共同作用机制或与调控子/通路相关的基因子集。现有方法通常提取反映定量相似性的模块（例如对数倍变化相关的基因），但不能充分捕捉生物学意义。在这项工作中，我们提出了双独立成分分析（Dual ICA）方法，该方法提供了一种提取由表现出强关联的基因集和条件集组成的“相互作用模块”的方法。双独立成分分析包括两次执行独立成分分析（ICA），一次对基因进行，一次对条件进行。使用得到的信号矩阵，我们提取相应的基因集和条件集。这些集合之间的相互作用通过线性回归的系数进行量化，并通过Wald检验和Z分数过滤确定显著性。这些系数等同于从两个信号矩阵获得的独立成分的外积。不仅提取的基因集与已知调控子一致，而且它们实例化的显著相互作用模块还包括通过共享作用机制影响这些调控子表达的条件。与传统的无监督聚类方法相比，双独立成分分析表现出卓越的性能，并为探索功能关系提供了明确的基因-条件集。
