ArticleBMC genomics2018
A highly adaptive microbiome-based association test for survival traits.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- MiSurv: an Integrative Web Cloud Platform for User-Friendly Microbiome Data Analysis with Survival Responses.Microbiology spectrum · 2023Trial
- Quantifying Metagenomic Strain Associations from Microbiomes with Anpan.bioRxiv : the preprint server for biology · 2025Article
- Associating somatic mutation with clinical outcomes through kernel regression and optimal transport.Biometrics · 2023Article
- A microbial causal mediation analytic tool for health disparity and applications in body mass index.Microbiome · 2023Article
- A microbial causal mediation analytic tool for health disparity and applications in body mass index.Research square · 2023Article
- Testing microbiome associations with survival times at both the community and individual taxon levels.PLoS computational biology · 2022Article
- Microbial risk score for capturing microbial characteristics, integrating multi-omics data, and predicting disease risk.Microbiome · 2022Article
- Duodenal Microbiome and Serum Metabolites Predict Hepatocellular Carcinoma in a Multicenter Cohort of Patients with Cirrhosis.Digestive diseases and sciences · 2022Article
- A Bayesian method for identifying associations between response variables and bacterial community composition.PLoS computational biology · 2022Article
- Microbial Risk Score for Capturing Microbial Characteristics, Integrating Multi-omics Data, and Predicting Disease Risk.bioRxiv : the preprint server for biology · 2022Article
- Novel Application of Survival Models for Predicting Microbial Community Transitions with Variable Selection for Environmental DNA.Applied and environmental microbiology · 2022Article
- Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study.BMC genomics · 2021Article
- MiRKAT: kernel machine regression-based global association tests for the microbiome.Bioinformatics (Oxford, England) · 2021Article
- Meta-analysis methods for multiple related markers: Applications to microbiome studies with the results on multiple α-diversity indices.Statistics in medicine · 2021Article
- Analysis of Salivary Mycobiome in a Cohort of Oral Squamous Cell Carcinoma Patients From Sudan Identifies Higher Salivary Carriage ofFrontiers in cellular and infection microbiology · 2021Article
- A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease.PLoS computational biology · 2020Article
- A powerful microbial group association test based on the higher criticism analysis for sparse microbial association signals.Microbiome · 2020Article
- Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients.Genome medicine · 2019Article
- pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis.Bioinformatics (Oxford, England) · 2019Article
- The Microbiome in Lung Cancer Tissue and Recurrence-Free Survival.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019Article
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4 authors.
Funding
Abstract
backgroundThere has been increasing interest in discovering microbial taxa that are associated with human health or disease, gathering momentum through the advances in next-generation sequencing technologies. Investigators have also increasingly employed prospective study designs to survey survival (i.e., time-to-event) outcomes, but current item-by-item statistical methods have limitations due to the unknown true association pattern. Here, we propose a new adaptive microbiome-based association test for survival outcomes, namely, optimal microbiome-based survival analysis (OMiSA). OMiSA approximates to the most powerful association test in two domains: 1) microbiome-based survival analysis using linear and non-linear bases of OTUs (MiSALN) which weighs rare, mid-abundant, and abundant OTUs, respectively, and 2) microbiome regression-based kernel association test for survival traits (MiRKAT-S) which incorporates different distance metrics (e.g., unique fraction (UniFrac) distance and Bray-Curtis dissimilarity), respectively.
resultsWe illustrate that OMiSA powerfully discovers microbial taxa whether their underlying associated lineages are rare or abundant and phylogenetically related or not. OMiSA is a semi-parametric method based on a variance-component score test and a re-sampling method; hence, it is free from any distributional assumption on the effect of microbial composition and advantageous to robustly control type I error rates. Our extensive simulations demonstrate the highly robust performance of OMiSA. We also present the use of OMiSA with real data applications.
conclusionsOMiSA is attractive in practice as the true association pattern is unpredictable in advance and, for survival outcomes, no adaptive microbiome-based association test is currently available.
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