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Genome Biology

, 16:241

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


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.


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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Autor: Emma Pierson - Christopher Yau

Fuente: https://link.springer.com/

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