Cells are the fundamental units of biological systems and exhibit unique development
trajectories and molecular features. Our exploration of how the genomes orchestrate the …

Spatial transcriptomics (ST) technologies are revolutionizing the way to explore the spatial
architecture of tissues. Currently, ST data analysis is often restricted to a single two …

Pluripotent stem cells underpin a growing sector that leverages their differentiation potential
for research, industry, and clinical applications. This review evaluates the landscape of …

Computational methods for integrating single-cell transcriptomic data from multiple samples
and conditions do not generally account for imbalances in the cell types measured in …

Mouse lemurs are the smallest, fastest reproducing, and among the most abundant
primates, and an emerging model organism for primate biology, behavior, health and …

Here, we present AtacAnnoR, a two-round annotation method for scATAC-seq data using
well-annotated scRNA-seq data as reference. We evaluate AtacAnnoR's performance …

The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for
integrative analysis. Though many methods for data integration have been developed, few …

Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular
ecosystems and molecular interactions in various biomedical research. Hence, identifying …

Large-scale comprehensive single-cell experiments are often resource-intensive and
require the involvement of many laboratories and/or taking measurements at various times …

Integrating multiple single‐cell datasets is essential for the comprehensive understanding of
cell heterogeneity. Batch effect is the undesired systematic variations among technologies or …