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BMC Genomics. 2018 Jun 19;19(1):477. doi: 10.1186/s12864-018-4772-0.

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

Street K1,2, Risso D3, Fletcher RB4, Das D4,5, Ngai J4,6,7, Yosef N8,2, Purdom E9,2, Dudoit S10,11,12,13.

Author information

1
Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
2
Center for Computational Biology, University of California, Berkeley, CA, USA.
3
Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, 407 E 61st St, New York, 10065, NY, USA.
4
Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
5
Berkeley Institute for Data Science, University of California, Berkeley, CA, USA.
6
Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
7
QB3 Berkeley Functional Genomics Laboratory, Berkeley, CA, USA.
8
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
9
Department of Statistics, University of California, Berkeley, CA, USA.
10
Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA. sandrine@stat.berkeley.edu.
11
Department of Statistics, University of California, Berkeley, CA, USA. sandrine@stat.berkeley.edu.
12
Center for Computational Biology, University of California, Berkeley, CA, USA. sandrine@stat.berkeley.edu.
13
Berkeley Institute for Data Science, University of California, Berkeley, CA, USA. sandrine@stat.berkeley.edu.

Abstract

BACKGROUND:

Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.

RESULTS:

We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.

CONCLUSIONS:

Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.

KEYWORDS:

Lineage inference; Pseudotime inference; RNA-Seq; Single cell

PMID:
29914354
PMCID:
PMC6007078
DOI:
10.1186/s12864-018-4772-0
[Indexed for MEDLINE]
Free PMC Article

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