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Genome Biol. 2016 May 23;17(1):106. doi: 10.1186/s13059-016-0975-3.

SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data.

Author information

1
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
2
Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
3
Department of Computer Science, Duke University, Durham, NC, 27708, USA.
4
Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA.
5
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. prins@cs.unc.edu.
6
Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. prins@cs.unc.edu.

Abstract

Single cell experiments provide an unprecedented opportunity to reconstruct a sequence of changes in a biological process from individual "snapshots" of cells. However, nonlinear gene expression changes, genes unrelated to the process, and the possibility of branching trajectories make this a challenging problem. We develop SLICER (Selective Locally Linear Inference of Cellular Expression Relationships) to address these challenges. SLICER can infer highly nonlinear trajectories, select genes without prior knowledge of the process, and automatically determine the location and number of branches and loops. SLICER recovers the ordering of points along simulated trajectories more accurately than existing methods. We demonstrate the effectiveness of SLICER on previously published data from mouse lung cells and neural stem cells.

KEYWORDS:

Manifold learning; Single cell RNA-seq; Time series

PMID:
27215581
PMCID:
PMC4877799
DOI:
10.1186/s13059-016-0975-3
[Indexed for MEDLINE]
Free PMC Article

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