title: ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis.
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.
authors.affiliation: Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, Oxford, UK. emma.pierson@st-annes.ox.ac.uk.
authors.forename: Emma
authors.initials: E
authors.lastname: Pierson
authors.affiliation: Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, Oxford, UK. cyau@well.ox.ac.uk.|Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, OX3 7BN, Oxford, UK. cyau@well.ox.ac.uk.
authors.forename: Christopher
authors.initials: C
authors.lastname: Yau
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D025341:Principal Component Analysis; D017423:Sequence Analysis, RNA; D059010:Single-Cell Analysis; D012984:Software