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 …

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

Single-cell technologies have transformed our understanding of human tissues. Yet, studies
typically capture only a limited number of donors and disagree on cell type definitions …

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

Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes
methods for preprocessing, visualization, clustering, pseudotime and trajectory inference …

Trajectory inference approaches analyze genome-wide omics data from thousands of single
cells and computationally infer the order of these cells along developmental trajectories …

As a data-driven science, genomics largely utilizes machine learning to capture
dependencies in data and derive novel biological hypotheses. However, the ability to extract …

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene
expression at a cellular resolution. However, noise due to amplification and dropout may …

Accurately modeling cellular response to perturbations is a central goal of computational
biology. While such modeling has been based on statistical, mechanistic and machine …

Recent years have seen a revolution in single-cell RNA-sequencing (scRNA-seq)
technologies, datasets, and analysis methods. Since 2016, the scRNA-tools database has …