Diffusion pseudotime robustly reconstructs lineage branching

Haghverdi, Laleh; Büttner, Maren; Wolf, F. Alexander; Buettner, Florian; Theis, Fabian J.

doi:10.1038/nmeth.3971

Cited by 1,135 publications

(1,075 citation statements)

References 24 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

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“…It was found in many studies that development proceeds in an asynchronous fashion in populations 4,5 .…”

Section: (Introductory Paragraph)mentioning

confidence: 99%

“…(1) population growth dynamics such as population bursts and selection, (2) an approximation of the developmental potential function including stability information of cell states, (3) an exact mapping of developmental checkpoints on a trajectory given mutant data, (4) imputation of the population density in cell state at a missing time point for experiment planning, and (5) model selection between multiple dynamic models such as not peer-reviewed) is the author/funder. All rights reserved.…”

Section: (Introductory Paragraph)mentioning

confidence: 99%

Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series

Fischer

Fiedler

Kernfeld

et al. 2017

Preprint

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(Abstract)Cellular development has traditionally been described as a series of transitions between discrete cell states, such as the sequence of double negative, double positive and single positive stages in Tcell development. Recent advances in single cell transcriptomics suggest an alternative description of development, in which cells follow continuous transcriptomic trajectories. A cell's state along such a trajectory can be captured with pseudotemporal ordering, which however is not able to predict development of the system in real time. We present pseudodynamics, a mathematical framework that integrates timeseries and genetic knockout information with such transcriptomebased descriptions in order to describe and analyze the realtime evolution of the system. Pseudodynamics models the distribution of a cell population across a continuous cell state coordinate over time based on a stochastic differential equation along developmental trajectories and random switching between trajectories in branching regions. To illustrate feasibility, we use pseudodynamics to estimate cellstatedependent growth and differentiation of thymic Tcell development. The model approximates a developmental potential function (Waddington's landscape) and suggests that thymic Tcell development is biphasic and not strictly deterministic before betaselection. Pseudodynamics generalizes classical discrete population models to continuous states and thus opens possibilities such as probabilistic model selection to single cell genomics.not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. (Fig. 1b). Here, we present pseudodynamics (Fig. 1a), a mathematical framework tailored to developmental trajectories which accounts both for the continuous population structure and the nonsteady state dynamics of the system to understand population growth and differentiation characteristics along trajectories.As an example, we establish an unbiased view of Tcell development based on a branching pseudotemporal ordering of cells observed with singlecell RNAseq data and validate it with previous findings. Secondly, we show that the population growth rate can be fit as a transcriptomic state dependent function which maps out selection pressure during Tcell maturation on specific transcriptomic states. Thirdly, we show how pseudodynamics facilitates the integration of wildtype and mutant data to annotate developmental trajectories with developmental checkpoints using the example of developmental arrest of Tcells at betaselection in Rag1 and Rag2 knockout mice. Our model extends previous efforts on modelling gene expression distributions in time 10 by population size dynamics and by the notion of developmental trajectories in transcriptome space. Pseudodynamics is independent of the method with which the pseudotemporal ordering is generated. In summary, pseudodynamics adds the following layers of information to a lineage trajectory:(1) population growth dynamics such as population bursts and selection, (2) an approxima...

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“…Here we address this task for imaging studies of developing tissues, where patterns of cell fates are established by complex regulatory networks [6][7][8]. Advances in live imaging continue to provide new insights into the dynamics of individual components in these networks, but imaging more than three reporters at the same time is still challenging and limited to model genetic organisms [9,10].…”

Section: Introductionmentioning

confidence: 99%

Synthesizing developmental trajectories

Villoutreix

Andén

Lim

et al. 2017

Preprint

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Dynamical processes in biology are studied using an ever-increasing number of techniques, each of which brings out unique features of the system. One of the current challenges is to develop systematic approaches for fusing heterogeneous datasets into an integrated view of multivariable dynamics. We demonstrate that heterogeneous data fusion can be successfully implemented within a semi-supervised learning framework that exploits the intrinsic geometry of high-dimensional datasets. We illustrate our approach using a dataset from studies of pattern formation in Drosophila. The result is a continuous trajectory that reveals the joint dynamics of gene expression, subcellular protein localization, protein phosphorylation, and tissue morphogenesis. Our approach can be readily adapted to other imaging modalities and forms a starting point for further steps of data analytics and modeling of biological dynamics. Author summaryA wide range of problems in biology require analysis of multivariable dynamics in space and time. As a rule, the multiscale nature and complexity of real systems precludes simultaneous monitoring of all the relevant variables, and multivariable dynamics must be synthesized from partial views provided by different experimental techniques. We present a formal framework for accomplishing this task in the context of imaging studies of pattern formation in developing tissues.

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Diffusion pseudotime robustly reconstructs lineage branching

Cited by 1,135 publications

References 24 publications

Reversed graph embedding resolves complex single-cell developmental trajectories

Reversed graph embedding resolves complex single-cell developmental trajectories

Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series

Synthesizing developmental trajectories

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