Simulation-based inference of differentiation trajectories from RNA velocity fields

Gupta, Revant; Cerletti, Dario; Gut, Gilles; Oxenius, Annette; Claassen, Manfred

doi:10.1016/j.crmeth.2022.100359

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…In the second row of Figure , we show the streamline plot of the original velocity field on cell points in a 2D UMAP representation and its decomposed components obtained by using the Hodge decomposition. The streamlines in the original velocity field clearly illustrate the cell phase direction and also show a bifurcation process that happened in the G1 phase, characterizing the separation of G1 and G1 checkpoint as indicated in …”

Section: Resultsmentioning

confidence: 91%

“…The streamlines in the original velocity field clearly illustrate the cell phase direction and also show a bifurcation process that happened in the G1 phase, characterizing the separation of G1 and G1 checkpoint as indicated in. 41 Due to the separation of G1 and G1-checkpoint in G1 phase and the cell cycle process from G1 to S. The separation of G1 and G1-checkpoint in G1 phase and the cell cycle process from G1 to S is evident in the harmonic component by examining the saddle point located in G1 phase, which shows an unstable state of cells going through the bifurcation process. One can also see the overall direction trend of the streamline plot in the harmonic component from G1 to G2M.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

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Hodge Decomposition of Single-Cell RNA Velocity

Su,

Tong,

Wei

2024

J. Chem. Inf. Model.

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RNA velocity has the ability to capture the cell dynamic information in the biological processes; yet, a comprehensive analysis of the cell state transitions and their associated chemical and biological processes remains a gap. In this work, we provide the Hodge decomposition, coupled with discrete exterior calculus (DEC), to unveil cell dynamics by examining the decomposed curl-free, divergence-free, and harmonic components of the RNA velocity field in a low dimensional representation, such as a UMAP or a t-SNE representation. Decomposition results show that the decomposed components distinctly reveal key cell dynamic features such as cell cycle, bifurcation, and cell lineage differentiation, regardless of the choice of the low-dimensional representations. The consistency across different representations demonstrates that the Hodge decomposition is a reliable and robust way to extract these cell dynamic features, offering unique analysis and insightful visualization of single-cell RNA velocity fields.

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Section: Resultsmentioning

confidence: 91%

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

Hodge Decomposition of Single-Cell RNA Velocity

Su,

Tong,

Wei

2024

J. Chem. Inf. Model.

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“…Cytopath (v0.1.9) is a simulation-based method to infer differentiation trajectories 35 . It takes the cell-to-cell transition matrix (excluding self-transitions), terminal states and initial states data from scVelo as input.…”

Section: Star Methodsmentioning

confidence: 99%

“…The RNA velocity trajectory predicted with high confidence that EE cells give rise to CE cells and pass through the IE cell state to differentiate into TE cells (Figure 2C, S2B). The direction of the trajectory was supported by Cytopath, a simulation-based velocity inference tool (Figure 2D) 35 . Zeb2 was among the top genes with differential velocity across clusters and likely informed trajectory directionality.…”

Section: Population-level Rna Velocity Analysis Reveals Early Effecto...mentioning

confidence: 99%

Hierarchal single-cell lineage tracing reveals differential fate commitment of CD8 T-cell clones in response to acute infection

Abdullah,

Emiliani,

Vaidya

et al. 2024

Preprint

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Generating balanced populations of CD8 effector and memory T cells is necessary for immediate and durable immunity to infections and cancer. Yet, a definitive understanding of CD8 differentiation remains unclear. We used CARLIN, a processive lineage recording mouse model with single-cell RNA-seq and TCR-seq to track endogenous antigen-specific CD8 T cells during acute viral infection. We identified a diverse repertoire of expanded T-cell clones represented by seven transcriptional states. TCR enrichment analysis revealed differential memory- or effector fate biases within clonal populations. Shared Vb segments and amino acid motifs were found within biased categories despite high TCR diversity. Using single-cell CARLIN barcode-seq we tracked multi-generational clones and found that unlike unbiased or memory-biased clones, which stably retain their fate profiles, effector-biased clones could adopt memory- or effector-bias within subclones. Collectively, our study demonstrates that a heterogenous T-cell repertoire specific for a shared antigen is composed of clones with distinct TCR-intrinsic fate-biases.

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“…The pseudotime of cells is calculated by projecting them onto the principal curve which is obtained from lineages of trajectory. Cytopath [ 12 ] utilized RNA velocity to infer the root and terminal states. By combining the cell-to-cell transition probability matrix and cell states, Cytopath constructs multiple simulations of trajectories that are used to assign cell states.…”

Section: Introductionmentioning

confidence: 99%

A robust and accurate single-cell data trajectory inference method using ensemble pseudotime

et al. 2023

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Background The advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in individual cell resolution. In recent years, many trajectory inference methods have been developed. They have focused on using the graph method to infer the trajectory using single-cell data, and then calculate the geodesic distance as the pseudotime. However, these methods are vulnerable to errors caused by the inferred trajectory. Therefore, the calculated pseudotime suffers from such errors. Results We proposed a novel framework for trajectory inference called the single-cell data Trajectory inference method using Ensemble Pseudotime inference (scTEP). scTEP utilizes multiple clustering results to infer robust pseudotime and then uses the pseudotime to fine-tune the learned trajectory. We evaluated the scTEP using 41 real scRNA-seq data sets, all of which had the ground truth development trajectory. We compared the scTEP with state-of-the-art methods using the aforementioned data sets. Experiments on real linear and non-linear data sets demonstrate that our scTEP performed superior on more data sets than any other method. The scTEP also achieved a higher average and lower variance on most metrics than other state-of-the-art methods. In terms of trajectory inference capacity, the scTEP outperforms those methods. In addition, the scTEP is more robust to the unavoidable errors resulting from clustering and dimension reduction. Conclusion The scTEP demonstrates that utilizing multiple clustering results for the pseudotime inference procedure enhances its robustness. Furthermore, robust pseudotime strengthens the accuracy of trajectory inference, which is the most crucial component in the pipeline. scTEP is available at https://cran.r-project.org/package=scTEP.

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Simulation-based inference of differentiation trajectories from RNA velocity fields

Cited by 7 publications

References 32 publications

Hodge Decomposition of Single-Cell RNA Velocity

Hodge Decomposition of Single-Cell RNA Velocity

Hierarchal single-cell lineage tracing reveals differential fate commitment of CD8 T-cell clones in response to acute infection

A robust and accurate single-cell data trajectory inference method using ensemble pseudotime

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