Testing microbiome associations with survival times at both the community and individual taxon levels

Yingtian Hu, Yunxiao Li, Glen A Satten and Yi-Juan Hu

PLOS Computational Biology, 2022, vol. 18, issue 9, 1-17

Abstract: Background: Finding microbiome associations with possibly censored survival times is an important problem, especially as specific taxa could serve as biomarkers for disease prognosis or as targets for therapeutic interventions. The two existing methods for survival outcomes, MiRKAT-S and OMiSA, are restricted to testing associations at the community level and do not provide results at the individual taxon level. An ad hoc approach testing each taxon with a survival outcome using the Cox proportional hazard model may not perform well in the microbiome setting with sparse count data and small sample sizes. Methods: We have previously developed the linear decomposition model (LDM) for testing continuous or discrete outcomes that unifies community-level and taxon-level tests into one framework. Here we extend the LDM to test survival outcomes. We propose to use the Martingale residuals or the deviance residuals obtained from the Cox model as continuous covariates in the LDM. We further construct tests that combine the results of analyzing each set of residuals separately. Finally, we extend PERMANOVA, the most commonly used distance-based method for testing community-level hypotheses, to handle survival outcomes in a similar manner. Results: Using simulated data, we showed that the LDM-based tests preserved the false discovery rate for testing individual taxa and had good sensitivity. The LDM-based community-level tests and PERMANOVA-based tests had comparable or better power than MiRKAT-S and OMiSA. An analysis of data on the association of the gut microbiome and the time to acute graft-versus-host disease revealed several dozen associated taxa that would not have been achievable by any community-level test, as well as improved community-level tests by the LDM and PERMANOVA over those obtained using MiRKAT-S and OMiSA. Conclusions: Unlike existing methods, our new methods are capable of discovering individual taxa that are associated with survival times, which could be of important use in clinical settings. Author summary: High-throughput sequencing of 16S gene or metagenomes provides an unprecedented opportunity to discover microbial associations with traits such as clinical outcomes or environmental factors. Detecting individual taxa associated with survival times has significant implications: the taxa could serve as biomarkers for disease prognosis or as targets for therapeutic interventions. However, the taxon data are highly complex because they are high-dimensional, sparse (having 50–90% zero counts), and highly overdispersed. Existing methods for microbial associations with survival outcomes are restricted to testing associations at the community level and do not provide results at the individual taxon level. An ad hoc approach testing each taxon with a survival outcome using the Cox proportional hazard model may not perform well in the microbiome setting with sparse count data. We present an approach that can be used by the LDM and PERMANOVA for testing microbial associations with survival outcomes at both the community and individual taxon levels. In particular, we provide the first test at the individual taxon level. Therefore, our work represents a major advance in analytical methods for microbial association studies and will have a strong impact on current and future microbiome research in clinical settings.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010509 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 10509&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010509

DOI: 10.1371/journal.pcbi.1010509

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().