Wishbone identifies bifurcating developmental trajectories from single-cell data

Setty, Manu; Tadmor, Michelle D.; Reich-Zeliger, Shlomit; Angel, Omer; Salame, Tomer Meir; Kathail, Pooja; Choi, Kristy; Bendall, Sean C.; Friedman, Nir; Pe’er, Dana

doi:10.1038/nbt.3569

Cited by 553 publications

(595 citation statements)

References 36 publications

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“…We combined clusters 1, 2, 3, 4, 5, 6 as erythroid (1095), clusters 7, 8, 9, 10 as CMP (451), clusters 12, 13, 14, 15, 16, 17, 18 as GMP (1123), cluster 11 as DC (30) and cluster 19 as lymphoid (31), as suggested in the original study [1]. To ensure better comparison with other published work [24,25], we used all informative genes (3004 genes) identified in the original study [1] for cell ordering. But we also run dpFeature to select ordering genes (top 1, 000 DEGs are used) on the transcript counts in Figure SI1.…”

Section: Analysis Of Mar-seq Datamentioning

confidence: 99%

Reversed graph embedding resolves complex single-cell developmental trajectories

Qiu

Q²,

Tang

et al. 2017

Preprint

108

126

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Organizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.

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Section: Analysis Of Mar-seq Datamentioning

confidence: 99%

Reversed graph embedding resolves complex single-cell developmental trajectories

Qiu

Q²,

Tang

et al. 2017

Preprint

108

126

View full text Add to dashboard Cite

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“…1C). In addition, the coordinates of the data in the diffusion map provide more than a visualization, as distances in diffusion space represent a measure of similarity between cells that avoids some of the effects of noise present in single-cell expression measurements (11,20). individual expression profiles (21,22).…”

Section: Resultsmentioning

confidence: 99%

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Hamey

Nestorowa

Kinston

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-bycell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyteerythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.gene regulatory networks | hematopoiesis | single cell | Boolean network | stem progenitor cells

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“…d Cartoon of cell types in maturation of T cells in thymus. e Force-directed graph layout of mass cytometry thymus data (30 antibodies used 15 ), pre-processed with diffusion maps (Online Methods) 17 . Point with highest score (black) is reported branch point, although more than one point may have a significant score.…”

Section: Figure 1: Treetop Methodology and Demonstrationmentioning

confidence: 99%

“…Both Monocle and SPADE by definition impose a tree topology, regardless of the actual topology of the data. Wishbone 15 and diffusion pseudotime 16 both use an embedding whose distances correspond to those along the underlying low-dimensional manifold representing the data via diffusion maps 17 . Distinct branches are then identified via anti-correlations in graph distances to a selected root point that has to be sensibly defined a priori.…”

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confidence: 99%

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Tree-ensemble analysis tests for presence of multifurcations in single cell data

Macnair

Roditi

Ganscha

et al. 2017

Preprint

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We introduce TreeTop, an algorithm for single-cell data analysis to identify and assess statistical significance of branch points in biological processes with possibly multi-level branching hierarchies. We demonstrate branch point identification for processes with varying topologies, including T cell maturation, B cell differentiation and hematopoiesis. Our analyses are consistent with recent experimental studies suggesting a shallow hierarchy of differentiation events in hematopoiesis, rather than the classical multi-level hierarchy. MainMany important biological processes, such as differentiation in developmental and immune biology, and clonal evolution in cancer, can be conceived of as bi-or multi-furcated cellular state trajectories. Hematopoiesis is such a process, where hematopoietic stem cells (HSCs) give rise to multiple distinct mature blood cell types via a sequence of lineage commitments. The exact sequence is still debated 1 , either assuming a hierarchical architecture of multiple fate decisions via distinct oligopotent progenitor cell states 2-4 , or a flat hierarchy of hematopoiesis, which does not include oligopotent progenitors, and where HSCs differentiate directly into committed lineages 5-7 .High dimensional single-cell technologies, such as single-cell RNA sequencing 8 and mass cytometry 9 , constitute increasingly widely used tools to investigate such opposing models of differentiation, and other branching processes. These technologies allow the evaluation of the state of single cells, i.e. the transcriptional or proteomic abundance profile in the case of single cell RNA sequencing or mass cytometry, respectively. Biological processes can be conceived of as trajectories through state space: temporal sequences of cellular states that can either be derived from time series or reconstructed from non-time series single-cell data 10 . We define a branch point as the location in state space where three or more distinct . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/200923 doi: bioRxiv preprint first posted online Oct. 10, 2017; cellular state trajectories meet. Branch points dissect these trajectories into distinct state trajectory branches.Identifying branch points is challenging because for each single cell measurement, both branch membership and ordering within each branch must be learned simultaneously. Existing approaches include pseudotime ordering, which learns a latent time variable along a mean trajectory through state space is limited to non-branching processes [11][12] . SPADE overcomes this limitation by fitting a single minimum spanning tree to non-deterministically clustered data 13 . Monocle 11,14 fits smoothed trees to a low dimensional representation of single cell data, where branch points in the tree are assumed to correspond to branch points in the data. Both Monocle and SPADE by definition impose a tree topology, regardless ...

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Wishbone identifies bifurcating developmental trajectories from single-cell data

Cited by 553 publications

References 36 publications

Reversed graph embedding resolves complex single-cell developmental trajectories

Reversed graph embedding resolves complex single-cell developmental trajectories

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Tree-ensemble analysis tests for presence of multifurcations in single cell data

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