Discovering a sparse set of pairwise discriminating features in high-dimensional data
Abstract
Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 30, 2020
- Source ID
- 10.1093/bioinformatics/btaa690
Entities
People
- Samuel Melton
- Sharad Ramanathan
Organizations
- Defense Advanced Research Projects Agency
- Harvard University
- National Institutes of Health