ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysisReport as inadecuate




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Genome Biology

, 16:241

First Online: 02 November 2015Received: 14 June 2015Accepted: 14 October 2015

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, Zero Inflated Factor Analysis ZIFA, which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.

AbbreviationsEMExpectation-maximization

FAFactor analysis

PCAPrincipal components analysis

PPCAProbabilistic principal components analysis

ScRNA-seqSingle-cell RNA expression analysis

ZIFAZero-inflated factor analysis

Electronic supplementary materialThe online version of this article doi:10.1186-s13059-015-0805-z contains supplementary material, which is available to authorized users.

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Author: Emma Pierson - Christopher Yau

Source: https://link.springer.com/



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