Submodular sketches of single-cell RNA-seq measurements
| Autor: | William Stafford Noble, Jeff A. Bilmes, Wei Yang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Submodular maximization business.industry Computer science genetic processes RNA-Seq 02 engineering and technology Machine learning computer.software_genre Sketch Submodular set function 03 medical and health sciences 020204 information systems 0202 electrical engineering electronic engineering information engineering Benchmark (computing) natural sciences Artificial intelligence business computer 030304 developmental biology |
| Zdroj: | BCB |
| DOI: | 10.1145/3388440.3412409 |
| Popis: | Single-cell RNA-seq (scRNA-seq) datasets now routinely profile tens of thousands to millions of cells. These data are invaluable for finding important subpopulations of cells and for closely studying the mechanics of gene expression. However, as these datasets become larger, they become more difficult to analyze. Analyzing and sharing massive single-cell RNA-seq datasets can be facilitated by creating a "sketch" of the data---a selected subset of cells that accurately represent the full dataset. In this work, we use an existing benchmark to demonstrate the utility of submodular optimization in efficiently creating high quality sketches of scRNA-seq data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|