Journal
GENOME BIOLOGY
Volume 16, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-015-0805-z
Keywords
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Funding
- Rhodes Trust
- UK Medical Research Council New Investigator Research Grant [MR/L001411/1]
- Wellcome Trust Core Award Grant [090532/Z/09/Z]
- John Fell Oxford University Press Research Fund
- Li Ka Shing Foundation via a Oxford-Stanford Big Data in Human Health Seed Grant
- MRC [MR/M00919X/1, MC_PC_14131, MR/L001411/1] Funding Source: UKRI
- Medical Research Council [MR/L001411/1, MC_PC_14131, MR/M00919X/1] Funding Source: researchfish
- Wellcome Trust [090532/Z/09/Z] Funding Source: Wellcome Trust
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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.
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