The introduction of RNA velocity in single cells has opened up new ways of studying cellular differentiation. The originally proposed framework obtains velocities as the deviation of the observed ratio of spliced and unspliced mRNA from an inferred steady state. Errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. With scVelo (https://scvelo.org), we address these restrictions by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to a wide variety of systems comprising transient cell states, which are common in development and in response to perturbations. We infer gene-specific rates of transcription, splicing and degradation, and recover the latent time of the underlying cellular processes. This latent time represents the cell's internal clock and is based only on its transcriptional dynamics. Moreover, scVelo allows us to identify regimes of regulatory changes such as stages of cell fate commitment and, therein, systematically detects putative driver genes. We demonstrate that scVelo enables disentangling heterogeneous subpopulation kinetics with unprecedented resolution in hippocampal dentate gyrus neurogenesis and pancreatic endocrinogenesis. We anticipate that scVelo will greatly facilitate the study of lineage decisions, gene regulation, and pathway activity identification.

Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism to date, including 7,824 adult individuals from two European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity regarding more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information regarding gene expression, heritability, overlap with known drug targets, previous association with complex disorders and inborn errors of metabolism. We further developed a database and web-based resources for data mining and results visualization. Our findings contribute to a greater understanding of the role of inherited variation in blood metabolic diversity, and identify potential new opportunities for pharmacologic development and disease understanding.

Single‐cell RNA ‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one's data. Here, we detail the steps of a typical single‐cell RNA ‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. We formulate current best‐practice recommendations for these steps based on independent comparison studies. We have integrated these best‐practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.Single-cell measurements of gene expression, using imaging techniques such as RNA-FiSH (fluorescence in situ hybridization), have provided important insights into the kinetics of transcription and cell-to-cell variation in gene expression [1][2][3] . However, such approaches can examine the expression of only a small number of genes in each experiment, thus restricting our ability to examine co-expression patterns and to robustly identify subpopulations of cells. Protocols have been developed to overcome these limitations by amplifying small quantities of mRNA 4,5 , which, in combination with microfluidics approaches for isolating individual cells 6,7 , have been used to analyze the co-expression of tens to hundreds of genes in single cells 8,9 . These protocols also allow the entire transcriptome of large numbers of single cells to be assayed in an unbiased way. This was initially done using microarrays 10,11 but is more often now done using next-generation sequencing [12][13][14][15] . Such approaches have been used to model early embryogenesis in the mouse 16 and to investigate bimodality in gene expression patterns of differentiating immune cell types 17 .After the generation of single-cell RNA-sequencing (RNA-seq) profiles from hundreds of cells, one goal to identify subpopulations that share a common gene-expression profile. Some of these subpopulations may represent previously unidentified cell types. Additionally, by studying patterns of gene expression in different single cells, insights into the regulatory landscape of each cell population can be obtained.However, methods for identifying subpopulations of cells and modeling their gene regulatory landscapes are only now beginning to emerge 18,19 . To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity. Although the importance of accounting for unobserved factors is well established in bulk RNA-seq studies [21][22][23] , robust approaches to detect and account for confounding f...