MiRKAT-S: a community-level test of association between the microbiota and survival times.
MiRKAT-S: a community-level test of association between the microbiota and survival times.
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DOI：
10.1186/s40168-017-0239-9
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发表时间：
2017-02-08
期刊：
影响因子：
15.5
通讯作者：
Wu MC
中科院分区：
文献类型：
--
作者：
Plantinga A;Zhan X;Zhao N;Chen J;Jenq RR;Wu MC
对人类微生物群的群落水平分析最终发现了微生物群的整体变化与多种疾病和状况之间的关系。然而，现有的工作主要集中于分析相对简单的二分或定量结果，例如疾病状态或生物标志物水平。最近，人们对微生物群与审查生存结果之间的关系也产生了相当大的兴趣，例如在临床试验中。如何对经过审查的生存结果进行社区层面的分析尚不清楚，因为基于标准差异的测试无法适应经过审查的生存时间，并且不存在替代方法。我们开发了一种新方法 MiRKAT-S，用于对经过审查的生存时间的微生物组数据进行群落级分析。 MiRKAT-S 使用生态信息距离度量（例如 UniFrac 距离）来生成个体分类概况之间的成对距离矩阵。距离矩阵转换为核（相似性）矩阵，用于比较微生物群的相似性与个体之间生存时间的相似性。使用合成微生物群落的模拟研究证明了对 I 型错误的正确控制和足够的功效。我们还应用 MiRKAT-S 来检查肠道微生物群与同种异体血液或骨髓移植后存活率之间的关系。我们提出了 MiRKAT-S，这是一种促进微生物群与生存结果之间关联的社区水平分析的方法，因此提供了一种分析临床试验产生的微生物组数据的新方法。本文的在线版本 (doi:10.1186/s40168-017-0239-9) 包含补充材料，可供授权用户使用。
Community-level analysis of the human microbiota has culminated in the discovery of relationships between overall shifts in the microbiota and a wide range of diseases and conditions. However, existing work has primarily focused on analysis of relatively simple dichotomous or quantitative outcomes, for example, disease status or biomarker levels. Recently, there is also considerable interest in the relationship between the microbiota and censored survival outcomes, such as in clinical trials. How to conduct community-level analysis with censored survival outcomes is unclear, since standard dissimilarity-based tests cannot accommodate censored survival times and no alternative methods exist. We develop a new approach, MiRKAT-S, for community-level analysis of microbiome data with censored survival times. MiRKAT-S uses ecologically informative distance metrics, such as the UniFrac distances, to generate matrices of pairwise distances between individuals’ taxonomic profiles. The distance matrices are transformed into kernel (similarity) matrices, which are used to compare similarity in the microbiota to similarity in survival times between individuals. Simulation studies using synthetic microbial communities demonstrate correct control of type I error and adequate power. We also apply MiRKAT-S to examine the relationship between the gut microbiota and survival after allogeneic blood or bone marrow transplant. We present MiRKAT-S, a method that facilitates community-level analysis of the association between the microbiota and survival outcomes and therefore provides a new approach to analysis of microbiome data arising from clinical trials. The online version of this article (doi:10.1186/s40168-017-0239-9) contains supplementary material, which is available to authorized users.