Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, United States.
Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA 30322, United States.
Bioinformatics. 2023 Nov 1;39(11). doi: 10.1093/bioinformatics/btad668.
There are compelling reasons to test compositional hypotheses about microbiome data. We present here linear decomposition model-centered log ratio (LDM-clr), an extension of our LDM approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, this extension enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis.
LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.
有充分的理由来检验关于微生物组数据的组成假设。我们在这里提出线性分解模型中心化对数比（LDM-clr），这是我们的 LDM 方法的扩展，允许对中心化对数比转换后的分类群计数数据拟合线性模型。由于 LDM-clr 是在现有的 LDM 程序内部实现的，因此这个扩展享有 LDM 支持的所有功能，包括在分类群和群落水平上进行差异丰度的组成分析，同时允许对关联或中介分析使用广泛的协变量和研究设计。
LDM-clr 已被添加到 R 包 LDM 中，该包可在 GitHub 上的 https://github.com/yijuanhu/LDM 获得。
