ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
- Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, Oxford, UK. email@example.com.
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.
|Publication status||Published - 2 Nov 2015|
- Gene Expression Profiling, Models, Statistical, Principal Component Analysis, Sequence Analysis, RNA, Single-Cell Analysis, Software, Journal Article, Research Support, Non-U.S. Gov't