Motivation Multi-omics data integration enables the global analysis of biological systems and discovery of new biological insights. Multi-omics experimental designs have been further extended with a longitudinal dimension to study dynamic relationships between molecules. However, methods that integrate longitudinal multi-omics data are still in their infancy. Results We introduce the R package timeOmics, a generic analytical framework for the integration of longitudinal multi-omics data. The framework includes pre-processing, modeling and clustering to identify molecular features strongly associated with time. We illustrate this framework in a case study to detect seasonal patterns of mRNA, metabolites, gut taxa and clinical variables in patients with diabetes mellitus from the integrative Human Microbiome Project. Availabilityand implementation timeOmics is available on Bioconductor and github.com/abodein/timeOmics. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

---

timeOmics：用于纵向多组学数据集成的 R 包

动机 多组学数据集成可以对生物系统进行全局分析并发现新的生物学见解。多组学实验设计已进一步扩展为纵向维度，以研究分子之间的动态关系。然而，整合纵向多组学数据的方法仍处于起步阶段。结果 我们介绍了 R 包 timeOmics，这是一个用于整合纵向多组学数据的通用分析框架。该框架包括预处理、建模和聚类，以识别与时间密切相关的分子特征。我们在一个案例研究中说明了这个框架，以检测来自综合人类微生物组计划的糖尿病患者的 mRNA、代谢物、肠道分类群和临床变量的季节性模式。可用性和实施​​ timeOmics 可在 Bioconductor 和 github.com/abodein/timeOmics 上获得。补充信息 补充数据可在 Bioinformatics 在线获取。