Recent advances in single-cell technologies have enabled high-throughput molecular
profiling of cells across modalities and locations. Single-cell transcriptomics data can now …

Single‐cell RNA sequencing (scRNA‐seq) technology has become the state‐of‐the‐art
approach for unravelling the heterogeneity and complexity of RNA transcripts within …

Cardiovascular disease is the leading cause of death worldwide. Advanced insights into
disease mechanisms and therapeutic strategies require a deeper understanding of the …

Generative pretrained models have achieved remarkable success in various domains such
as language and computer vision. Specifically, the combination of large-scale diverse …

CRISPR screens are a powerful source of biological discovery, enabling the unbiased
interrogation of gene function in a wide range of applications and species. In pooled …

Understanding cellular responses to genetic perturbation is central to numerous biomedical
applications, from identifying genetic interactions involved in cancer to develo** methods …

RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-
sequencing data. It describes the rate of gene expression change for an individual gene at a …

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 …

Large single-cell atlases are now routinely generated to serve as references for analysis of
smaller-scale studies. Yet learning from reference data is complicated by batch effects …

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods,
together with associated computational tools and the growing availability of public data …