Computational tools for integrative analyses of diverse single-cell experiments are facing
formidable new challenges including dramatic increases in data scale, sample …

Single-cell genomic technologies provide an unprecedented opportunity to define molecular
cell types in a data-driven fashion, but present unique data integration challenges. Many …

Large single-cell atlases are now routinely generated with the aim of serving as reference to
analyse future smaller-scale studies. Yet, learning from reference data is complicated by …

Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets
have been generated that allow for the full transcriptional characterization of cell types …

Motivation Deep learning approaches have empowered single-cell omics data analysis in
many ways and generated new insights from complex cellular systems. As there is an …

Acquiring accurate single-cell multiomics profiles often requires performing unbiased in
silico integration of data matrices generated by different single-cell technologies from the …

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 increasing availability of large-scale single-cell atlases has enabled the detailed
description of cell states. In parallel, advances in deep learning allow rapid analysis of newly …

Recent extensions of single-cell studies to multiple data modalities raise new questions
regarding experimental design. For example, the challenge of sparsity in single-omics data …

Batch effects in single-cell RNA-seq data pose a significant challenge for comparative
analyses across samples, individuals, and conditions. Although batch effect correction …