ArticlePLoS computational biology2022
Testing microbiome associations with survival times at both the community and individual taxon levels.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL.Cell reports methods · 2024Article
- Enhanced Feature Selection for Microbiome Data using FLORAL: Scalable Log-ratio Lasso Regression.bioRxiv : the preprint server for biology · 2023Article
- Compositional analysis of microbiome data using the linear decomposition model (LDM).Bioinformatics (Oxford, England) · 2023Article
- A new approach to testing mediation of the microbiome at both the community and individual taxon levels.Bioinformatics (Oxford, England) · 2022Article
- Article
- Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM.Bioinformatics (Oxford, England) · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundFinding 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.
methodsWe 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.
resultsUsing 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.
conclusionsUnlike 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.