Experimental approaches for measuring single-cell gene expression can observe each cell at only one time point, requiring computational approaches for reconstructing the dynamics of gene expression during cell fate transitions. RNA velocity is a promising computational approach for this problem, but existing inference methods fail to capture key aspects of real data, limiting their utility. To address these limitations, we developed VeloVAE, a Bayesian model for RNA velocity inference. VeloVAE uses variational Bayesian inference to estimate the posterior distribution of latent time, latent cell state, and kinetic rate parameters for each cell. Our approach can incorporate prior distributions on rate parameters and time points; model lineage bifurcations using branching differential equations; and directly model discrete count data. We show that VeloVAE significantly outperforms previous approaches in terms of data fit, accuracy of inferred differentiation directions, and transcription rate estimation. These improvements allow VeloVAE to accurately model gene expression dynamics in complex biological systems, including hematopoiesis, induced pluripotent stem cell reprogramming, the developing mouse brain, and the entire mouse embryo. We find that the latent time automatically inferred using all cells can even outperform pseudotime inferred using manually chosen cell subsets and root cells. Our work provides important new tools for modeling sequential changes in gene expression from single-cell expression data.

SUMMARY: Cells differentiate to their final fates along unique trajectories, often involving multi-potent progenitors that can produce multiple terminally differentiated cell types. Recent developments in single-cell transcriptomic and epigenomic measurement provide tremendous opportunities for mapping these trajectories. The visualization of single-cell data often relies on dimension reduction methods such as UMAP to simplify high-dimensional single-cell data down to an understandable 2D form. However, these dimension reduction methods are not constructed to allow direct interpretation of the reduced dimensions in terms of cell differentiation. To address these limitations, we developed a new approach that places each cell from a single-cell dataset within a simplex whose vertices correspond to terminally differentiated cell types. Our approach can quantify and visualize current cell fate commitment and future cell potential. We developed CytoSimplex, a standalone open-source package implemented in R and Python that provides simple and intuitive visualizations of cell differentiation in 2D ternary and 3D quaternary plots. We believe that CytoSimplex can help researchers gain a better understanding of cell type transitions in specific tissues and characterize developmental processes. AVAILABILITY AND IMPLEMENTATION: The R version of CytoSimplex is available on Github at https://github.com/welch-lab/CytoSimplex. The Python version of CytoSimplex is available on Github at https://github.com/welch-lab/pyCytoSimplex.

A key problem in computational biology is discovering the gene expression changes that regulate cell fate transitions, in which one cell type turns into another. However, each individual cell cannot be tracked longitudinally, and cells at the same point in real time may be at different stages of the transition process. This can be viewed as a problem of learning the behavior of a dynamical system from observations whose times are unknown. Additionally, a single progenitor cell type often bifurcates into multiple child cell types, further complicating the problem of modeling the dynamics. To address this problem, we developed an approach called variational mixtures of ordinary differential equations. By using a simple family of ODEs informed by the biochemistry of gene expression to constrain the likelihood of a deep generative model, we can simultaneously infer the latent time and latent state of each cell and predict its future gene expression state. The model can be interpreted as a mixture of ODEs whose parameters vary continuously across a latent space of cell states. Our approach dramatically improves data fit, latent time inference, and future cell state estimation of single-cell gene expression data compared to previous approaches.

Latches have the advantages of timing-borrowing, smaller cell area, lower input capacitance, and lower power compared to flip-flops (FFs). This article presents a CAD flow that converts any arbitrarily complex single-clock-domain FF-based RTL design into an efficient 3-phase latch-based design. The flow includes a novel 3-phase aware retiming algorithm for power and area optimization. Post place-and-route results demonstrate that our new 3-phase designs achieve 23.5% and 23.9% average power reductions compared to more traditional FF and master–slave-based alternatives across a board range of benchmarks with no degradation in performance and on average less area.

Latches are smaller and lower power than flip-flops (FFs) and are typically used in a time-borrowing master-slave configuration. This paper presents an automatic flow for converting arbitrarily-complex single-clock-domain FF-based RTL designs to efficient 3-phase latch-based designs with reduced number of required latches, saving both register and clock-tree power. Post place-and-route results demonstrate that our 3-phase latch-based designs save an average of 15.5% and 18.5% power on a variety of ISCAS, CEP, and CPU benchmark circuits, compared to their more traditional FF and master-slave based alternatives.