PT - JOURNAL ARTICLE AU - Emma Pierson AU - Christopher Yau TI - ZIFA: Dimensionality reduction for zero-inflated single cell gene expression analysis AID - 10.1101/019141 DP - 2015 Jan 01 TA - bioRxiv PG - 019141 4099 - http://biorxiv.org/content/early/2015/06/14/019141.short 4100 - http://biorxiv.org/content/early/2015/06/14/019141.full AB - Single cell RNA-seq data allows insight into normal cellular function and diseases including cancer through the molecular characterisation of cellular state at the single-cell level. Dimensionality reduction of such high-dimensional datasets is essential for visualization and analysis, but single-cell RNA-seq data is challenging for classical dimensionality reduction methods because of the prevalence of dropout events leading 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 modelling accuracy on simulated and biological datasets.