Abstract
Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.
Original language | English |
---|---|
Article number | 241 |
Journal | Genome Biology |
Volume | 16 |
DOIs | |
Publication status | Published - 2 Nov 2015 |
Keywords
- Gene Expression Profiling
- Models, Statistical
- Principal Component Analysis
- Sequence Analysis, RNA
- Single-Cell Analysis
- Software
- Journal Article
- Research Support, Non-U.S. Gov't