With single-cell genomics datasets, it is possible to assay the transcriptome in thousands to millions of cells as they undergo development, differentiation and external perturbation. These data are ripe for investigation into how regulation of RNA processing governs the myriad cell states comprising these systems.
However, despite our ability to assay these states, single-cell data is...
Single-cell RNA-sequencing (scRNA-seq) data provides a detailed view into the gene regulatory landscape that cells traverse during differentiation from pluripotent to mature cell types. However, analysing these datasets remains challenging due to their sparsity, high dimensionality, lack of temporal information, and high levels of technical noise, necessitating the development of numerous...
Deciphering gene regulatory networks is central to understanding how cells orchestrate gene expression programmes. Single-cell RNA sequencing (scRNA-seq) has enabled the investigation of genome-wide regulatory relationships through pairwise gene correlations. Yet, interpreting these correlations is challenging due to confounding factors such as biological sources of covariation or technical...
RNA velocity has emerged as a popular approach for modeling cellular change along the phenotypic landscape but routinely omits regulatory interactions between genes. Conversely, methods that infer gene regulatory networks (GRNs) do not consider the dynamically changing nature of biological systems. To integrate these two currently disconnected fields, we present RegVelo, an end-to-end dynamic,...
Understanding how gene regulation gives rise to diverse cell states and cell fates during differentiation is a central question in biology. Single-cell RNA sequencing (scRNA-seq), as a rapidly developing and powerful methodology, provides both new insights into this problem and new challenges for mathematical modeling. While scRNA-seq enables measurement of genome-wide gene expression at...
