Better ways to analyze human microbiome samples
Statistical methods for analyzing messy microbiome data: detection of hidden artifacts and robust modeling approaches
Creating new statistical tools that find hidden errors and correct biases in microbiome data so researchers can trust findings used to improve health.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Johns Hopkins University NIH-funded |
| Lab location | 1 site (Baltimore, United States) |
| Project ID | NIH-11174408 on NIH RePORTER |
What this research studies
Researchers will develop computer-based methods to detect unmeasured artifacts (like batch effects) and sequencing biases in human microbiome sequencing data. They will adapt surrogate variable analysis and introduce multiple quantile thresholding tailored to the sparse, compositional, and overdispersed nature of microbiome measurements. The team will build robust statistical models that resist experimental bias and test them on large public MGS datasets and clinical cohort data. The work aims to make microbiome study results more reproducible and reliable for future clinical use.
Who could benefit from this research
Good fit: Ideal candidates are people whose microbiome samples (for example stool, oral, or skin swabs) have been or could be collected in research studies or clinical cohorts.
Not a fit: Patients without microbiome-related samples or whose care does not involve microbiome-based tests are unlikely to see direct benefits from this work.
Why it matters
Potential benefit: If successful, this could make microbiome-based findings more reliable, helping speed development of better diagnostics and treatments tied to the microbiome.
How similar studies have performed: Related batch-correction and surrogate-variable methods have improved reproducibility in genomics, but applying and extending them specifically to microbiome sequencing is relatively new and still evolving.
Where this research is happening
Baltimore, United States
- Johns Hopkins University — Baltimore, United States (Active)
Researchers
- Principal investigator: Zhao, Ni — Johns Hopkins University
- Study coordinator: Zhao, Ni
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.