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

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.

• W
  Wellcome

  Award: 090532/Z/09/Z
• LK
  Li Ka Shing Foundation
• WT
  Wellcome Trust

  Awards: 090532, 090532/Z/09/Z, 090532/Z/09/
• UO
  University of Oxford
• MR
  Medical Research Council

  Awards: MC_PC_14131, MR/L001411/1, MR/M00919X/1, MR/L001411/1