MiRKAT-S: a community-level test of association between the microbiota and survival times. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Plantinga, Anna; Zhan, Xiang; Zhao, Ni; Chen, Jun; Jenq, Robert; Wu, Michael
• Is Part Of: Microbiome. Volume 5:Issue 1(2017)
• Page Start: 1
• Page End: 13
• Publication Date: 2017-12
• Subjects: Survival data -- Kernel machine regression -- Distance-based analysis -- Community-level analysis; Microbiology -- Periodicals; Microorganisms -- Periodicals; Medical microbiology -- Periodicals; Soil microbiology -- Periodicals; Microbiological Phenomena -- Periodicals; Environmental Microbiology -- Periodicals; Medical microbiology; Microbiology; Microorganisms; Soil microbiology; Periodicals; Electronic journals; 579.17
• DOI: 10.1186/s40168-017-0239-9
• Languages: English
• ISSNs: 2049-2618

Abstract

Abstract Background 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. Methods 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. Results 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. Conclusions 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.