Reversed graph embedding resolves complex single-cell trajectories

Abstract

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.

Keywords

EmbeddingGraph embeddingComputational biologyCell fate determinationGraphComputer scienceBiologyGeneTranscription factorLineage (genetic)Theoretical computer scienceGeneticsArtificial intelligence

MeSH Terms

AlgorithmsAnimalsCell DifferentiationComputer SimulationGene Expression RegulationDevelopmentalModelsBiologicalMutationTranscription FactorsTranscriptome

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Publication Info

Year: 2017
Type: article
Volume: 14
Issue: 10
Pages: 979-982
Citations: 4834
Access: Closed

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APA Style

                            
                                
                                    Xiaojie Qiu, 
                                
                                    Qi Mao, 
                                
                                    Ying Tang
                                
                                et al.
                            
                            (2017). 
                            Reversed graph embedding resolves complex single-cell trajectories. 
                            Nature Methods
                            , 14
                            (10)
                            , 979-982.
                            https://doi.org/10.1038/nmeth.4402
                        

Identifiers

DOI: 10.1038/nmeth.4402
PMID: 28825705
PMCID: PMC5764547

Data Quality

Data completeness: 86%