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

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 …

The temporal order of differentiating cells is intrinsically encoded in their single-cell expression
profiles. We describe an efficient way to robustly estimate this order according to diffusion …

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and
continuous cell transitions. Partition-based graph abstraction (PAGA) provides an …

INTRODUCTION Understanding the differentiation of stem cells into the vast amount of cell
types that form the human body is a central problem of basic and medical science. The recent …

Single-cell omics technologies have enabled the creation of comprehensive cell atlases
across tissues and species and delivered key insights into the biological mechanisms …

A quantum many-body system that is prepared in the ground state of an integrable
Hamiltonian does not directly thermalize after a sudden small parameter quench away from …

Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations,
but as with all genomics experiments, batch effects can hamper data integration and …

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

We show that deep convolutional neural networks combined with nonlinear dimension reduction
enable reconstructing biological processes based on raw image data. We demonstrate …