Slingshot: Cell Lineage and Pseudotime Inference for Single-cell Transcriptomics

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2018 Jun 20

PMID 29914354

Citations 1111

Authors

Kelly Street

Davide Risso

Russell B Fletcher

Diya Das

John Ngai

Nir Yosef

Elizabeth Purdom

Sandrine Dudoit

Affiliations

Soon will be listed here.

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.

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References

Bendall S, Davis K, Amir E, Tadmor M, Simonds E, Chen T . Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell. 2014; 157(3):714-25. PMC: 4045247. DOI: 10.1016/j.cell.2014.04.005. View

Risso D, Purvis L, Fletcher R, Das D, Ngai J, Dudoit S . clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets. PLoS Comput Biol. 2018; 14(9):e1006378. PMC: 6138422. DOI: 10.1371/journal.pcbi.1006378. View

Cole M, Risso D, Wagner A, DeTomaso D, Ngai J, Purdom E . Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq. Cell Syst. 2019; 8(4):315-328.e8. PMC: 6544759. DOI: 10.1016/j.cels.2019.03.010. View

Kolodziejczyk A, Kim J, Svensson V, Marioni J, Teichmann S . The technology and biology of single-cell RNA sequencing. Mol Cell. 2015; 58(4):610-20. DOI: 10.1016/j.molcel.2015.04.005. View

Bacher R, Kendziorski C . Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol. 2016; 17:63. PMC: 4823857. DOI: 10.1186/s13059-016-0927-y. View

Campbell K, Yau C . Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference. PLoS Comput Biol. 2016; 12(11):e1005212. PMC: 5117567. DOI: 10.1371/journal.pcbi.1005212. View

Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J . A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun. 2018; 9(1):284. PMC: 5773593. DOI: 10.1038/s41467-017-02554-5. View

Haghverdi L, Buttner M, Wolf F, Buettner F, Theis F . Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods. 2016; 13(10):845-8. DOI: 10.1038/nmeth.3971. View

Stevant I, Neirijnck Y, Borel C, Escoffier J, Smith L, Antonarakis S . Deciphering Cell Lineage Specification during Male Sex Determination with Single-Cell RNA Sequencing. Cell Rep. 2018; 22(6):1589-1599. DOI: 10.1016/j.celrep.2018.01.043. View

10.

Reid J, Wernisch L . Pseudotime estimation: deconfounding single cell time series. Bioinformatics. 2016; 32(19):2973-80. PMC: 5039927. DOI: 10.1093/bioinformatics/btw372. View

11.

Zappia L, Phipson B, Oshlack A . Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 2017; 18(1):174. PMC: 5596896. DOI: 10.1186/s13059-017-1305-0. View

12.

Chubb J, Trcek T, Shenoy S, Singer R . Transcriptional pulsing of a developmental gene. Curr Biol. 2006; 16(10):1018-25. PMC: 4764056. DOI: 10.1016/j.cub.2006.03.092. View

13.

Scrucca L, Fop M, Murphy T, Raftery A . mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R J. 2016; 8(1):289-317. PMC: 5096736. View

14.

Wagner A, Regev A, Yosef N . Revealing the vectors of cellular identity with single-cell genomics. Nat Biotechnol. 2016; 34(11):1145-1160. PMC: 5465644. DOI: 10.1038/nbt.3711. View

15.

Qiu X, Hill A, Packer J, Lin D, Ma Y, Trapnell C . Single-cell mRNA quantification and differential analysis with Census. Nat Methods. 2017; 14(3):309-315. PMC: 5330805. DOI: 10.1038/nmeth.4150. View

16.

Shin J, Berg D, Zhu Y, Shin J, Song J, Bonaguidi M . Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis. Cell Stem Cell. 2015; 17(3):360-72. PMC: 8638014. DOI: 10.1016/j.stem.2015.07.013. View

17.

Perraudeau F, Risso D, Street K, Purdom E, Dudoit S . Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference. F1000Res. 2017; 6:1158. PMC: 5558107. DOI: 10.12688/f1000research.12122.1. View

18.

Kharchenko P, Silberstein L, Scadden D . Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014; 11(7):740-2. PMC: 4112276. DOI: 10.1038/nmeth.2967. View

19.

Ji Z, Ji H . TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 2016; 44(13):e117. PMC: 4994863. DOI: 10.1093/nar/gkw430. View

20.

Setty M, Tadmor M, Reich-Zeliger S, Angel O, Salame T, Kathail P . Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat Biotechnol. 2016; 34(6):637-45. PMC: 4900897. DOI: 10.1038/nbt.3569. View