Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development

Bendall, Sean C.; Davis, Kara L.; Amir, El-ad David; Tadmor, Michelle D.; Simonds, Erin F.; Chen, Tiffany J.; Shenfeld, Daniel K.; Nolan, Garry P.; Pe’er, Dana

doi:10.1016/j.cell.2014.04.005

Cited by 863 publications

(949 citation statements)

References 17 publications

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“…The layout shows three clearly distinct branches (Fig 1e), and the markers identified as showing the greatest difference between branches are consistent with biological expectations (Fig 1f-g, Supp Fig 3). Application of TreeTop to B cell maturation mass cytometry data 9 , a linear process, did not find any nodes with significant branch scores, consistent with the expected sequential evolution of marker abundances. TreeTop's layout also shows a linear structure (Supp Fig 4).…”

supporting

confidence: 60%

“…Mass / flow cytometry data is arcsinh-transformed (with cofactors of 5 for mass cytometry 9 and 150, or otherwise depending on the fluorescent tag, for flow cytometry). Where the data is a mixture of non-overlapping components, the dimensionality reduction technique diffusion maps 17 results in embeddings of the mixture components which are approximately orthogonal, with one diffusion component per mixture component 25 .…”

Section: Data Preprocessingmentioning

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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supporting

confidence: 60%

Section: Data Preprocessingmentioning

confidence: 99%

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

Macnair

Roditi

Ganscha

et al. 2017

Preprint

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“…In order to achieve this, future single-cell analysis methods need to further explore reliable prior biological knowledge. As previously mentioned there has been an attempt to describe linear trajectories (15) however, differentiation processes are not strictly linear and it is important to be able to describe bifurcated trajectories as well. Furthermore, other processes that display heterogeneity may be of interest beyond differentiation, and description of cellular processes should also encompass topologies which are bifurcated and cyclic.…”

Section: Current Methods and Challenges In Analyzing Single-cell Datamentioning

confidence: 99%

Current Challenges in Cell-Type Discovery Through Single-Cell Data

Roditi

Macnair

Claassen

2015

ITM Web of Conferences

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Abstract. Single cell sequencing and proteome profiling efforts in the past few years have revealed widespread genetic and proteomic heterogeneity among tumor cells. However, sensible cell-type definition of such heterogeneous cell populations has so far been a challenging task. Single cell technologies such as RNA sequencing and mass cytometry provide information precluded by conventional bulk measurements and have achieved significant improvements in multiparametricity at high cellular throughput. By combining these technologies with computational and mathematical techniques it is possible to quantitatively define cellular heterogeneity, uncovering distinct phenotypic profiles that can be utilized to, for example, characterize tumor heterogeneity with the potential to develop and improve therapeutic strategies. Current Methods and Challenges in Analyzing Single-Cell dataVarious approaches have been used to characterize cellular heterogeneity from single cell data (1,2), based typically on a definition of a cell type as a characteristic abundance profile of a set of molecular markers (gates). These profiles have been either defined from prior knowledge (3) or derived in a data driven fashion by means of conventional clustering techniques (4). Other techniques addressing this task cover generic dimensionality reduction (5) and clustering techniques that account for differentiation mechanisms in an algorithmic fashion (6). However, the high dimensionality and complex biological variability of these single cell data require development of novel computational approaches for quantification and interpretation of the results.To date such approaches have not sufficiently taken into account the non-linear continuous nature of biological processes, which give rise to intermediate cell states observed during transition between persistent cell states. Conventional clustering techniques (7) make rigid implicit assumptions on the shape of cell subpopulations and not only ignore but are confused by the occurrence of such intermediate states(8). Recent computational approaches took advantage of intermediate cell states to define cell types from single cell data for linear and general bifurcated processes (8,9). While the latter approaches permit a wide spectrum of geometric arrangements of heterogeneous cell populations, these approaches lack robustness and reproducibility due to the stochastic nature of the underlying algorithm. Dimensionality reduction techniques such as principal component analysis and t-distributed Stochastic Neighbor Embedding (t-SNE) shift the interpretation of nonlinearities and cell population a

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“…Other analysis methods have been published that examine and visualize the phenotypic changes in cells as they differentiate and mature in the marrow and other tissues (1)(2)(3). Some of these methods have reported reproducibility issues and nonbiological branches (1,2).…”

Section: Introductionmentioning

confidence: 99%

Probability state modeling theory

Bagwell¹,

Hunsberger²,

Herbert³

et al. 2015

Cytometry Pt A

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As the technology of cytometry matures, there is mounting pressure to address two major issues with data analyses. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other issue is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these issues. The theory and important algorithms associated with PSM are presented along with simple examples and general strategies for autonomous analyses. PSM is leveraged to better understand B-cell ontogeny in bone marrow in a companion Cytometry Part B manuscript. Three short relevant videos are available in the online supporting information for both of these papers. PSM avoids the dimensionality barrier normally associated with high-dimensionality modeling by using broadened quantile functions instead of frequency functions to represent the modulation of cellular epitopes as cells differentiate. Since modeling programs ultimately minimize or maximize one or more objective functions, they are particularly amenable to automation and, therefore, represent a viable alternative to subjective and inaccurate gating approaches. V C 2015 International Society for Advancement of Cytometry

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Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development

Cited by 863 publications

References 17 publications

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

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

Current Challenges in Cell-Type Discovery Through Single-Cell Data

Probability state modeling theory

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