Wishbone identifies bifurcating developmental trajectories from single-cell data

By Manu N. Setty, Michelle D. Tadmor, S. Reich-Zeliger, Omer Angel, T. Salame, Pooja Kathail, K. Choi, Sean Bendall, Nir Friedman, Dana Pe'er. Nature Biotechnology, 2016

570 citations recorded by the source corpus.

Recent single-cell analysis technologies offer an unprecedented opportunity to elucidate developmental pathways. Here we present Wishbone, an algorithm for positioning single cells along bifurcating developmental trajectories with high resolution. Wishbone uses multi-dimensional single-cell data, such as mass cytometry or RNA-Seq data, as input and orders cells according to their developmental progression, and it pinpoints bifurcation points by labeling each cell as pre-bifurcation or as one of two post-bifurcation cell fates. Using 30-channel mass cytometry data, we show that Wishbone accurately recovers the known stages of T-cell development in the mouse thymus, including the bifurcation point. We also apply the algorithm to mouse myeloid differentiation and demonstrate its generalization to additional lineages. A comparison of Wishbone to diffusion maps, SCUBA and Monocle shows that it outperforms these methods both in the accuracy of ordering cells and in the correct identification of branch points.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e27ec639c7dd0b5f71985e287e73749bd16713a0

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Citations in the corpus

Characterization of cell fate probabilities in single-cell data with Palantir (2019, unknown): “Diffusion maps have been previously used to study single-cell data 2, 3 and are particularly adept at capturing differentiation trajectories 3, 15 .”
Reversed graph embedding resolves complex single-cell developmental trajectories (2017, methodology): “Wishbone, which uses a k-nearest-neighbor graph to approximate the cell manifold, assumes a single branch and uses a supervised dimensionality reduction scheme, with “ordering genes” selected based on Gene Ontology 2 .”
Reversed graph embedding resolves complex single-cell trajectories (2017, unknown): “In contrast to other methods, Monocle 2 also accurately learned a complex tree with five branches in a fully automatic fashion (Supplementary Figure 6B, Supplementary Data 3).”
CellRank for directed single-cell fate mapping (2020, unknown): “2, 8 and 9).”
Systems Biology of Cancer Metastasis (2019, background): “Considerable theoretical work has produced numerous useful data analysis techniques for single-cell genomics, transcriptomics, and proteomics, and the subsequent incorporation of these data into predictive computational models is now beginning(Liu et al., 2017a; Marcotte et al., 2016; Setty et al., ”
PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2019, unknown): “In the context of the recent advances of the study of simple biological processes that involve a single branching [6, 7], PAGA provides a similarly robust framework for arbitrarily complex topologies.”
Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria (2017, unknown): “Notably, these bifurcation trends could not be identified by other published methods for reconstructing bifurcating single-cell trajectories (18–22) (Comp. Supp.”
Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline (2016, background): “Wishbone extended the ability of Wanderlust to capture bifurcating developmental trajectories through introducing waypoints and identifying branch points [20].”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, background): “…the assumptions made by pseudotem-poral ordering algorithms (Trapnell et al. 2014, Bendall et al. 2014, Haghverdi et al. 2016, Reid & Wernisch 2016, Setty et al. 2016, Moris et al. 2016) are justified, we can potentially use this information to infer causality and directionality of gene…”