Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
Stat Med. 2021 Jun 15;40(13):3053-3065. doi: 10.1002/sim.8957. Epub 2021 Mar 26.
We propose a top-down approach for pathway analysis of longitudinal metabolite data. We apply a score test based on a shared latent process mixed model which can identify pathways with differentially progressing metabolites. The strength of our approach is that it can handle unbalanced designs, deals with potential missing values in the longitudinal markers, and gives valid results even with small sample sizes. Contrary to bottom-up approaches, correlations between metabolites are explicitly modeled leveraging power gains. For large pathway sizes, a computationally efficient solution is proposed based on pseudo-likelihood methodology. We demonstrate the advantages of the proposed method in identification of differentially expressed pathways through simulation studies. Finally, longitudinal metabolite data from a mice experiment is analyzed to demonstrate our methodology.
我们提出了一种用于纵向代谢物数据途径分析的自上而下的方法。我们应用了一种基于共享潜在过程混合模型的得分检验，可以识别具有不同代谢物进展的途径。我们方法的优势在于它可以处理不平衡设计，处理纵向标记物中的潜在缺失值，并在样本量较小时也能给出有效的结果。与自下而上的方法不同，我们明确地利用相关性建模来获得代谢物之间的相关性，以获得增益。对于大的途径大小，我们提出了一种基于伪似然方法的计算高效解决方案。我们通过模拟研究证明了所提出方法在识别差异表达途径方面的优势。最后，我们分析了来自小鼠实验的纵向代谢物数据，以验证我们的方法。
