尽管人类微生物组在健康和疾病中发挥着关键作用，但微生物组与其宿主之间相互作用的生物学机制尚不完全清楚。与其他分子分析数据的整合提供了表征微生物组作用和阐明治疗靶点的机会。然而，这对于微生物组分析数据中发现的高维性、组成性和罕见特征仍然具有挑战性。这些挑战需要使用能够在学习跨平台关联模式时实现结构化稀疏性的方法。我们提出树聚合因子回归（TARO）来集成微生物组和代谢组数据。我们利用分类树结构的信息来灵活聚合稀有特征。我们通过模拟研究证明 TARO 可以准确地恢复低秩系数矩阵并识别相关特征。我们将 TARO 应用于从接受结直肠癌筛查的受试者中收集的微生物组和代谢组图谱，以了解肠道微生物如何影响肠道代谢物丰度。实现所提出方法的 R 包 TARO 可在线获取：https://github.com/amishra-stats/ taro-package.© 作者 2024。由牛津大学出版社出版。

Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns.We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances.The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package.© The Author(s) 2024. Published by Oxford University Press.