Yuan Jiang
Oregon State University, OR·Mathematics & Statistics
Microbes in the gut, soil or water rarely act alone. Many work in groups that rise and fall together over time, and these "functional groups" sit between single species and the whole community.
- Fields
Mathematics & Statistics
Microbiology & Immunology
Ecology & Evolution
- Topics
microbiome
functional groups
cluster analysis
- Grants
- 1 known grant, through Jul 2028
What they work on
- Develop functional cluster analysis methods that group microbes with similar dynamics over time, including scale-invariant and gradient-sign-invariant groups that share a curve shape regardless of size or direction of change.
- Build scalable clustering tools that find functional groups of microbes responding similarly to external factors, linking group profiles to host or ecosystem health.
- Adapt these clustering tools to the compositional, sparse and high-dimensional nature of microbiome data, and apply them to real wildlife and human datasets to find health-related microbial groups.
- Release documented R software packages with tutorials and example datasets, and test them in real-world settings.
More about the research
Such groups may be useful signs of host or ecosystem health, and possible targets for therapies. Yet most microbiome studies look either at individual microbes or at the community as a whole, so these middle-scale groups are easy to miss.
Why it matters
Identifying groups of microbes that change together could give clinicians and ecologists better markers of health in people and wildlife, and point to targets for microbiome-based therapies. Free R packages with tutorials would let other microbiome researchers apply these clustering methods to their own time-series data.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Peng Liu
Iowa State University, IA·Mathematics & Statistics
Develop functional regression models for imaging outcomes built on a distributed learning framework, so that estimation and inference stay accurate yet scalable for large imaging datasets.
Amy Willis
University of Washington, WA·Mathematics & Statistics
Develop robust statistical methods for differential abundance, which test whether microbes are more or less common between groups while accounting for bias and variability introduced by sequencing.
Alexander Aue
University of California-Davis, CA·Mathematics & Statistics
Derive new probability results describing the structure of nonlinear, possibly heavy-tailed functional time series, where each observation is an entire curve.
The school
Oregon State University: 29 labs on PI Pages, $372M a year on research, 238 research doctorates a year.
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