一种新的方法来检测微生物组在群落和个体分类群水平上的中介作用。
A new approach to testing mediation of the microbiome at both the community and individual taxon levels.
机构信息
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
出版信息
Bioinformatics. 2022 Jun 13;38(12):3173-3180. doi: 10.1093/bioinformatics/btac310.
MOTIVATION
Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null, most existing methods treat the microbes as if they were all under the same type of null, leading to excessive false positive results.
RESULTS
We propose a new approach based on inverse regression that regresses the microbiome data at each taxon on the exposure and the exposure-adjusted outcome. Then, the P-values for testing the coefficients are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method LDM-med, implemented in the LDM framework, enjoys all the features of the LDM, e.g. allowing an arbitrary number of taxa to be tested simultaneously, supporting continuous, discrete, or multivariate exposures and outcomes (including survival outcomes), and so on. Using extensive simulations, we showed that LDM-med always preserved the FDR of testing individual taxa and had adequate sensitivity; LDM-med always controlled the type I error of the global test and had compelling power over existing methods. The flexibility of LDM-med for a variety of mediation analyses is illustrated by an application to a murine microbiome dataset, which identified several plausible mediating taxa.
AVAILABILITY AND IMPLEMENTATION
Our new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
了解暴露和疾病结果之间是否存在以及哪些微生物发挥了中介作用，对于研究人员通过调节微生物来开发治疗疾病的临床干预措施至关重要。现有的微生物组中介分析方法通常仅限于对群落水平中介或选择中介微生物进行全局检验，而不控制假发现率（FDR）。此外，虽然每个微生物处没有中介的零假设是由三种类型的零假设组成的复合零假设，但大多数现有的方法将微生物视为处于相同类型的零假设下，导致过度的假阳性结果。
结果
我们提出了一种基于逆回归的新方法，该方法将每个分类群的微生物组数据回归到暴露和暴露调整后的结果上。然后，使用检验系数的 P 值来检验群落和个体分类群水平的中介作用。这种方法很好地适用于我们的线性分解模型（LDM）框架，因此我们的新方法 LDM-med，在 LDM 框架中实现，享有 LDM 的所有特性，例如可以同时测试任意数量的分类群，支持连续、离散或多变量的暴露和结果（包括生存结果）等。通过广泛的模拟，我们表明 LDM-med 始终保持了检验个体分类群的 FDR，并且具有足够的敏感性；LDM-med 始终控制了全局检验的Ⅰ型错误，并且比现有的方法更具说服力。LDM-med 对各种中介分析的灵活性通过对一个鼠类微生物组数据集的应用得到了说明，该应用确定了几个可能的中介分类群。
可用性和实现
我们的新方法已添加到我们的 R 包 LDM 中，该包可在 GitHub 上获得，网址为 https://github.com/yijuanhu/LDM。
补充信息
补充数据可在 Bioinformatics 在线获得。
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