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

被引：409

作者：

Pierson, Emma ^{[1
]}

Yau, Christopher ^{[1
,2
]}

机构：

[1] Univ Oxford, Dept Stat, Oxford OX1 3TG, England

[2] Univ Oxford, Wellcome Trust Ctr Human Genet, Oxford OX3 7BN, England

来源：

GENOME BIOLOGY | 2015年 / 16卷

基金：

英国医学研究理事会; 英国惠康基金;

关键词：

HETEROGENEITY;

D O I：

10.1186/s13059-015-0805-z

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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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