Reversed graph embedding resolves complex single-cell trajectories

By Xiaojie Qiu, Qi Mao, Ying Tang, Li Wang, Raghav Chawla, Hannah A. Pliner, Cole Trapnell. Nature Methods, 2017

4,110 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5058f06cd24b82d2753dc21555d33a2995e21916

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Citations in the corpus

Comprehensive integration of single-cell data (2018, neutral): “In contrast, Seurat v3 has the ability to return a corrected expression matrix or to impute query features from a reference dataset—both of which can be used as input to additional methods such as pseudotime or network reconstruction (Qiu et al., 2017; Langfelder and Horvath, 2008).”
Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, background): “The authors of Monocle 2 report a runtime of 9min for 8 000 cells [34] and a linear”
Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry (2018, methodology): “81 Developmental trajectories were created using Monocle versions 2 and 3 (32-34), the 82 latter using Uniform Manifold Approximation and Projection for Dimension Reduction 83 (UMAP) (35).”
Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming (2019, neutral): “: This program (Qiu et al., 2017) computes a graph embedding of scRNA-seq data.”
SCANPY: large-scale single-cell gene expression data analysis (2018, result): “de 1Helmholtz Zentrum München – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany 2Department of Mathematics, Technische Universität München, Munich, Germany to PHENOGRAPH [18–20], identification of marker genes for clusters via differen”