High-sensitivity pattern discovery in large, paired multiomic datasets
Abstract
Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features are essential. In experiments featuring multiple high-dimensional datasets collected from the same set of samples, it is useful to identify groups of associated features between the datasets in a way that provides high statistical power and false discovery rate (FDR) control.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 24, 2022
- Source ID
- 10.1093/bioinformatics/btac232
Entities
People
- Ali Rahnavard
- Andrew R Ghazi
- Curtis Huttenhower
- Emma Schwager
- Eric A. Franzosa
- George Weingart
- Jason Lloyd-price
- Kathleen Sucipto
- Lauren J Mciver
- Levi Waldron
- Xochitl C. Morgan
- Yo Sup Moon
Organizations
- Army Research Office
- Broad Institute
- City University of New York
- Harvard University
- National Institutes of Health
- National Science Foundation
- University of Otago