Bhadra Sahely, Rousu Juho
Indian Institute of Technology, Palakkad, India.
Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
Methods Mol Biol. 2018;1807:141-161. doi: 10.1007/978-1-4939-8561-6_11.
In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and assume the stoichiometric steady state of the metabolic network.We will discuss a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also discuss a sparse variant of PMFA that favors flux modes that contain a small number of reactions. PMFA has several benefits: (1) it can be applied to large metabolic network in efficient way as PMFA does not enumerate elementary modes, (2) the method is more robust to the steady-state violations than competing approaches, and (3) can compactly capture the variation in the data by a few factors. This chapter will describe the detailed steps how to do the above task on experimental data from fluxomic and gene expression measurements.
在代谢分析中，经常使用两种不同但互补的方法：主成分分析（PCA）和化学计量通量分析。PCA能够捕捉一组实验中的主要变异模式，并且对数据无需做太多先验假设，但它本身并未考虑代谢的通量模式结构。另一方面，化学计量通量分析方法，如通量平衡分析（FBA）和基本模式分析，能够捕捉代谢通量模式，然而，它们主要是为一次分析单个样本而设计的，并假设代谢网络处于化学计量稳态。我们将讨论一种新的代谢分析方法，称为主代谢通量模式分析（PMFA），它在一个优雅的正则化优化框架中将PCA和化学计量通量分析方法结合起来。简而言之，该方法结合了PCA的方差最大化目标形式以及一个化学计量正则化项，该正则化项会惩罚那些远离网络任何通量模式的投影。为了便于解释，我们还讨论了PMFA的一种稀疏变体，它倾向于包含少量反应的通量模式。PMFA有几个优点：（1）它可以高效地应用于大型代谢网络，因为PMFA不枚举基本模式；（2）该方法比其他竞争方法对稳态违反情况更具鲁棒性；（3）能够通过少数几个因素紧凑地捕捉数据中的变异。本章将描述如何对来自通量组学和基因表达测量的实验数据执行上述任务的详细步骤。
