Inference of multiple trajectories in single cell RNA-seq data from RNA velocity

Stem Cell Differentiation ◽

Root Cell ◽

Current State ◽

Synthetic Datasets ◽

Cell Trajectory ◽

Direction Information ◽

Inference Methods ◽

Developmental Dynamics

AbstractTrajectory inference methods are used to infer the developmental dynamics of a continuous biological process such as stem cell differentiation and cancer cell development. Most of the current trajectory inference methods infer cell developmental trajectories based on the transcriptome similarity between cells, using single cell RNA-Sequencing (scRNA-Seq) data. These methods are often restricted to certain trajectory structures like trees or cycles, and the directions of the trajectory can only be partly inferred when the root cell is provided. We present CellPaths, a single cell trajectory inference method that infers developmental trajectories by integrating RNA velocity information. CellPaths is able to find multiple high-resolution trajectories instead of one single trajectory from traditional trajectory inference methods, and the trajectory structure is no longer constrained to be of any specific topology. The direction information provided by RNA-velocity also allows CellPaths to automatically detect root cell and differentiation direction. We evaluate CellPaths on both real and synthetic datasets. The result shows that CellPaths finds more accurate and detailed trajectories compared to current state-of-the-art trajectory inference methods.

A comparison of single-cell trajectory inference methods: towards more accurate and robust tools

10.1101/276907 ◽

2018 ◽

Cited By ~ 44

Author(s):

Wouter Saelens ◽

Robrecht Cannoodt ◽

Helena Todorov ◽

Yvan Saeys

Keyword(s):

Single Cell ◽

Parameter Tuning ◽

Comprehensive Assessment ◽

Large Collection ◽

User Friendliness ◽

Synthetic Datasets ◽

Cell Trajectory ◽

Comparison Of The Results ◽

Inference Methods ◽

Code Quality

AbstractUsing single-cell-omics data, it is now possible to computationally order cells along trajectories, allowing the unbiased study of cellular dynamic processes. Since 2014, more than 50 trajectory inference methods have been developed, each with its own set of methodological characteristics. As a result, choosing a method to infer trajectories is often challenging, since a comprehensive assessment of the performance and robustness of each method is still lacking. In order to facilitate the comparison of the results of these methods to each other and to a gold standard, we developed a global framework to benchmark trajectory inference tools. Using this framework, we compared the trajectories from a total of 29 trajectory inference methods, on a large collection of real and synthetic datasets. We evaluate methods using several metrics, including accuracy of the inferred ordering, correctness of the network topology, code quality and user friendliness. We found that some methods, including Slingshot, TSCAN and Monocle DDRTree, clearly outperform other methods, although their performance depended on the type of trajectory present in the data. Based on our benchmarking results, we therefore developed a set of guidelines for method users. However, our analysis also indicated that there is still a lot of room for improvement, especially for methods detecting complex trajectory topologies. Our evaluation pipeline can therefore be used to spearhead the development of new scalable and more accurate methods, and is available at github.com/dynverse/dynverse.To our knowledge, this is the first comprehensive assessment of trajectory inference methods. For now, we exclusively evaluated the methods on their default parameters, but plan to add a detailed parameter tuning procedure in the future. We gladly welcome any discussion and feedback on key decisions made as part of this study, including the metrics used in the benchmark, the quality control checklist, and the implementation of the method wrappers. These discussions can be held at github.com/dynverse/dynverse/issues.

Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues

Annual Review of Genetics ◽

10.1146/annurev-genet-120417-031247 ◽

2018 ◽

Vol 52 (1) ◽

pp. 203-221 ◽

Cited By ~ 29

Author(s):

Kenneth D. Birnbaum

Keyword(s):

Single Cell ◽

High Throughput ◽

Genetic Screening ◽

Fluorescent Microscopy ◽

The Novel ◽

Rna Seq ◽

Single Cell Rna Sequencing ◽

Developmental Dynamics

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

A comparison of single-cell trajectory inference methods

Nature Biotechnology ◽

10.1038/s41587-019-0071-9 ◽

2019 ◽

Vol 37 (5) ◽

pp. 547-554 ◽

Cited By ~ 265

Author(s):

Wouter Saelens ◽

Robrecht Cannoodt ◽

Helena Todorov ◽

Yvan Saeys

Keyword(s):

Single Cell ◽

Cell Trajectory ◽

Faculty Opinions recommendation of A comparison of single-cell trajectory inference methods.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.735426885.793559368 ◽

2019 ◽

Author(s):

Sushmita Roy

Keyword(s):

Single Cell ◽

Cell Trajectory ◽

cycleX: multi-dimensional pseudotime reveals cell cycle and differentiation relationship of dendritic cell progenitors

10.1101/222372 ◽

2017 ◽

Cited By ~ 1

Author(s):

Yong Kee Tan ◽

Xiaomeng Zhang ◽

Jinmiao Chen

Keyword(s):

Cell Cycle ◽

Dendritic Cell ◽

Single Cell ◽

Cellular Differentiation ◽

Cellular Heterogeneity ◽

Good Representation ◽

Continuous Processes ◽

Inference Methods ◽

Relationship Of

AbstractAdvances in single-cell RNA-sequencing have helped reveal the previously underappreciated level of cellular heterogeneity present during cellular differentiation. A static snapshot of single-cell transcriptomes provides a good representation of the various stages of differentiation as differentiation is rarely synchronized between cells. Data from numerous single-cell analyses has suggested that cellular differentiation and development can be conceptualized as continuous processes. Consequently, computational algorithms have been developed to infer pseudotimes and re-ordered cells along developmental trajectories. However, existing pseudotime inference methods generate one-dimensional pseudotime in an unsupervised manner, which is inadequate to elucidate the effects of individual biological processes such as cell cycle and differentiation and the links between them. Here we present a method called cycleX which infers multi-dimensional pseudotimes to reveal putative relationship between cell cycle and differentiation during dendritic cell development. cycleX can be also applied to generate multi-dimensional pseudotime for the relationship among cell cycle, differentiation, trafficking, activation, metabolism and etc.

Faculty Opinions recommendation of A comparison of single-cell trajectory inference methods.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.735426885.793558521 ◽

2019 ◽

Author(s):

Sara-Jane Dunn

Keyword(s):

Single Cell ◽

Cell Trajectory ◽

Model-based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior

10.1101/2020.12.26.424452 ◽

2020 ◽

Author(s):

Jin-Hong Du ◽

Ming Gao ◽

Jingshu Wang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Probabilistic Method ◽

Synthetic Data ◽

Developmental Trajectory ◽

Projection Neurons ◽

Single Cell Rna Sequencing ◽

Mixture Prior ◽

AbstractTrajectory inference methods analyze thousands of cells from single-cell sequencing technologies and computationally infer their developmental trajectories. Though many tools have been developed for trajectory inference, most of them lack a coherent statistical model and reliable uncertainty quantification. In this paper, we present VITAE, a probabilistic method combining a latent hierarchical mixture model with variational autoencoders to infer trajectories from posterior approximations. VITAE is computationally scalable and can adjust for confounding covariates to integrate multiple datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. We also apply VITAE to jointly analyze two single-cell RNA sequencing datasets on mouse neocortex. Our results suggest that VITAE can successfully uncover a shared developmental trajectory of the projection neurons and reliably order cells from both datasets along the inferred trajectory.