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 transcriptomics has broadened our understanding of cellular diversity and gene
expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has …

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 …

The innate immune compartment of the human central nervous system (CNS) is highly
diverse and includes several immune-cell populations such as macrophages that are …

Single-cell atlases often include samples that span locations, laboratories and conditions,
leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets …

Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex
systems. However, a comprehensive single-cell atlas has not been achieved for humans …

The rapid progress of protocols for sequencing single-cell transcriptomes over the past
decade has been accompanied by equally impressive advances in the computational …

Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses
challenges for studying and targeting senescent cells. We present here a machine learning …

The human liver is an essential multifunctional organ. The incidence of liver diseases is
rising and there are limited treatment options. However, the cellular composition of the liver …

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular
differentiation trajectories. However, inferring both the state and direction of differentiation is …