RNA velocity—current challenges and future perspectives

Bergen, Volker; Soldatov, Ruslan A.; Kharchenko, Peter V.; Theis, Fabian J.

doi:10.15252/msb.202110282

Cited by 173 publications

(266 citation statements)

References 26 publications

(38 reference statements)

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“…Notably, the accuracy of RNA velocity estimation can be improved by incorporating both gene expression and chromatin accessibility data [60]. Moreover, there is currently no consensus on how to appropriately batch effect correct linked gene expression modalities [57]. We chose to jointly correct spliced and unspliced count matrices according to the three metrics and two methods outlined in this study; however, we note that this challenge may bias or limit the interpretation of our results.…”

Section: Discussionmentioning

confidence: 99%

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

Ranek

Stanley

Purvis

2022

Preprint

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Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark eight integration approaches on eight datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. This work provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.

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Section: Discussionmentioning

confidence: 99%

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

Ranek

Stanley

Purvis

2022

Preprint

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“…α, β , and γ all smoothly vary over a finite time horizon according to an arbitrary function; u and s are continuous (Fig. 3 of [22]).…”

Section: Logic and Methodologymentioning

confidence: 99%

“…8b of [1]): only ELAVL4 appears to show a symmetric pulse of expression. Finally, even when this model does apply, the assumption of constant splicing and degradation rates across the entire lineage is a potentially severe source of error, with no simple way to diagnose it [22].…”

Section: Logic and Methodologymentioning

confidence: 99%

RNA velocity unraveled

Gorin

Fang

Chari

et al. 2022

Preprint

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We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.

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“…Myofibroblasts produce different components of the ECM such as collagen and fibronectin. RNA velocity captures the ratio of unspliced to spliced RNA on a per-gene basis, and thus provides temporal information (37,38). RNA velocities across many genes are used to infer a latent temporal axis on which cells undergoing a dynamic process such as differentiation can be ordered on.…”

Section: Cell State Dynamics Can Be Inferred From a Single Time Pointmentioning

confidence: 99%

The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans

Quake

2021

Preprint

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In recent years there has been tremendous progress towards deep molecular characterization of cell types using single cell transcriptome sequencing. Here we report a single cell transcriptomic atlas comprising nearly 500,000 cells from 24 different human tissues and organs. In several instances multiple organs were analyzed from the same donor. Analyzing organs from the same individual controls for genetic background, age, environment, and epigenetic effects, and enables a detailed comparison of cell types that are shared between tissues. This resource provides a rich molecular characterization of more than 400 cell types, their distribution across tissues, and detailed information about tissue specific variation in gene expression. We have used the fact that multiple tissues came from the same donor to study the clonal distribution of T cells between tissues, to understand the tissue specific mutation rate in B cells, and to analyze the cell cycle state and proliferative potential of shared cell types across tissues. Finally, we have also used this data to characterize cell type specific RNA splicing and how such splicing varies across tissues within an individual.

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RNA velocity—current challenges and future perspectives

Cited by 173 publications

References 26 publications

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

RNA velocity unraveled

The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans

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