Amy Willis
University of Washington, WA·Mathematics & Statistics
The communities of microbes living in and on the human body, known as the microbiome, play a role in cancer, autoimmune diseases, sexually transmitted infections and other conditions. Scientists usually study these communities by reading their DNA sequences.
- Fields
Mathematics & Statistics
Microbiology & Immunology
Public Health & Epidemiology
- Topics
microbiome
microbial communities
high-throughput sequencing
- Grants
- 1 known grant, through Jun 2029
What they work on
- 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.
- Build integrated statistical models of multiple biological units in microbiome data, including interactions within and across kingdoms of life.
- Create methods for emerging data types, such as samples with spike-in cells or communities, and deliver them as free open-source software with tutorials.
- Develop experimental design recommendations that maximize statistical power to detect true microbiome discoveries while minimizing sequencing and labor costs.
More about the research
Sequencing, however, adds bias and random noise. Differences between samples and between studies can be larger than the differences caused by disease or treatment, which makes real discoveries hard to trust or repeat.
Why it matters
Noisy, biased sequencing data can lead microbiome studies to false or unrepeatable conclusions about disease. More reliable statistical tools and cost-aware study designs could help researchers find real links between microbes and conditions such as cancer, autoimmune disease and sexually transmitted infections. These tools could eventually support better diagnosis and treatment of these diseases.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Justin Silverman
Pennsylvania State University, PA·Mathematics & Statistics
Build a theoretical framework for objectively evaluating existing analysis methods for sequence count data in light of scale limitations and measurement bias, the two problems that can dominate study conclusions.
Hok Chio Lai
University of Southern California, CA·Psychology·Early-career
Develop a robust optimization algorithm for data harmonization that puts different instruments measuring the same concept on a common scale while also correcting for measurement bias across groups.
Yuan Jiang
Oregon State University, OR·Mathematics & Statistics
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.
The school
University of Washington: 81 labs on PI Pages, $1.7B a year on research, 694 research doctorates a year.
Reach out
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