RNA velocity unraveled

Gorin, Gennady; Fang, Meichen; Chari, Tara; Pachter, Lior

doi:10.1371/journal.pcbi.1010492

Cited by 102 publications

(124 citation statements)

References 174 publications

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“…However, over developmental time these assumptions are unlikely to hold across all developmental transitions, which can lead to the inference of incorrect directionality (Bergen et al 2021). Similarly, when applying RNA velocity algorithms to scRNA-seq data of a known differentiation trajectory, reversed velocities have been reported (Gorin et al 2022). For these reasons, we performed most of our trajectory inference analysis using Monocle3 (Trapnell et al 2014).…”

Section: Discussionmentioning

confidence: 94%

A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates

Lim

Sun

et al. 2022

Preprint

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We present a multiomic cell atlas of human lung development that combines single cell RNA and ATAC sequencing, high throughput spatial transcriptomics and single cell imaging. Coupling single cell methods with spatial analysis has allowed a comprehensive cellular survey of the epithelial, mesenchymal, endothelial and erythrocyte/leukocyte compartments from 5-22 post conception weeks. We identify new cell states in all compartments. These include developmental-specific secretory progenitors that resemble cells in adult fibrotic lungs and a new subtype of neuroendocrine cell related to human small cell lung cancer; observations which strengthen the connections between development and disease/regeneration. Our datasets are available for the community to download and interact with through our web interface (https://fetal-lung.cellgeni.sanger.ac.uk). Finally, to illustrate its general utility, we use our cell atlas to generate predictions about cell-cell signalling and transcription factor hierarchies which we test using organoid models.

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Section: Discussionmentioning

confidence: 94%

A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates

Lim

Sun

et al. 2022

Preprint

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“…Another important difference between Dynamo and RNAForecaster is that Dynamo requires its input data to be smoothed using k-nearest neighbors averaging in order to compute RNA velocity (Qiu et al,2022). This procedure essentially averages the cells that are close together in expression space, which may introduce some distortions or remove important variation (Gorin et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Transcriptomic forecasting with neural ODEs

Erbe

Stein-O’Brien

Fertig

2022

Preprint

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Single-cell RNA-seq (scRNA-seq) technologies can uncover changes in the molecular states that underlie cellular phenotypes. However, to understand dynamic cellular processes, computational tools are needed to extract temporal information from the snapshots of cellular gene expression that scRNA-seq provides. To address this challenge, we have developed a neural ordinary differential equation based method, RNAForecaster, for predicting gene expression states in single cells for multiple future time steps. We demonstrate that RNAForecaster can accurately predict future expression states in simulated scRNA-seq data. We then show that using metabolic labeling scRNA-seq data from constitutively dividing cells, RNAForecaster accurately recapitulates many of the expected changes in gene expression during progression through the cell cycle over a three day period. Finally, we extend RNAForecaster to use unspliced and spliced counts from scRNA-seq to predict the impact of knock down experiments in pancreatic development. Thus, RNAForecaster enables estimation of future expression states in biological systems.

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“…While we have shown LatentVelo is accurate on many datasets, for some it requires additional hyperparameter adjustment in terms of regularization strength. Indeed, all of the RNA velocity methods we compared with also have multiple settings and hyperparameters, in addition to preprocessing steps [14]. More work needs to be done in this area.…”

Section: Discussionmentioning

confidence: 99%

Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics

Farrell

Mani

Goyal

2022

Preprint

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RNA velocity provides directional information for trajectory inference from single-cell RNA-sequencing data. Traditional approaches to computing RNA velocity rely on strict assumptions about the equations describing transcription of unspliced RNA and splicing of unspliced RNA into spliced RNA. This results in issues in scenarios where these assumptions are violated, such as multiple lineages with distinct dynamics and time-dependent rates. In this work we present ""LatentVelo"", a novel approach to computing a low-dimensional representation of RNA velocity with deep learning. Our approach embeds cells into a latent space with a variational auto-encoder, and describes differentiation dynamics on this latent space with neural ordinary differential equations. These more general dynamics enable accurate trajectory inference, and the latent space approach enables the generation of dynamics-based latent embeddings of cell states and batch correction of cell states and of RNA velocity. By performing simultaneous batch correction of RNA velocity dynamics and gene expression, we also outperform traditional batch correction methods that only consider gene expression. The flexible structure of the model enables modelling a variety of regulatory structures and multi-omic data, or incorporating additional information such as cell-type annotations or experimental metadata to improve the latent embedding. LatentVelo will be available at https://github.com/Spencerfar/LatentVelo.

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RNA velocity unraveled

Cited by 102 publications

References 174 publications

A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates

A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates

Transcriptomic forecasting with neural ODEs

Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics

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