Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.
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We thank I. Tirosh for discussions on marker-based ordering, F. Theis and F.A. Wolf for discussions on the data analysis with DPT from Paul et al.9, and members of the Trapnell laboratory for comments on the manuscript. This work was supported by US National Institutes of Health (NIH) grants DP2 HD088158 (C.T.) and U54 DK107979 (C.T.); C.T. is partly supported by a Dale. F. Frey Award for Breakthrough Scientists and an Alfred P. Sloan Foundation Research Fellowship; and H.A.P. is supported by a National Science Foundation (NSF) Graduate Research Fellowship (DGE-1256082).
The authors declare no competing financial interests.
Supplementary Figures 1–20 and Supplementary Note. (PDF 20516 kb)
Life Sciences Reporting Summary. (PDF 129 kb)
Zipped file for the neuron simulation data. (ZIP 35169 kb)
Zipped file for the least action path data. (ZIP 401 kb)
Zipped file for the complicate tree structure data. (ZIP 3 kb)
Software and analysis code used in this study which can reproduce all results. (ZIP 15812 kb)
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Qiu, X., Mao, Q., Tang, Y. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 14, 979–982 (2017). https://doi.org/10.1038/nmeth.4402
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