ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

External organisations

  • Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, Oxford, UK. emma.pierson@st-annes.ox.ac.uk.

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.

Details

Original languageEnglish
Article number241
JournalGenome Biology
Volume16
Publication statusPublished - 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