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 …

The increasing generation of population-level single-cell atlases has the potential to link
sample metadata with cellular data. Constructing such references requires integration of …

The growing number of available single-cell gene expression datasets from different species
creates opportunities to explore evolutionary relationships between cell types across …

Single-cell multiomics data continues to grow at an unprecedented pace. Although several
methods have demonstrated promising results in integrating several data modalities from …

As the scale of single-cell genomics experiments grows into the millions, the computational
requirements to process this data are beyond the reach of many. Herein we present Scarf, a …

Recent advances in single-cell technologies and integration algorithms make it possible to
construct comprehensive reference atlases encompassing many donors, studies, disease …

Motivation Increasing numbers of large scale single cell RNA-Seq projects are leading to a
data explosion, which can only be fully exploited through data integration. A number 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 …

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 …

Single-cell datasets continue to grow in size, posing computational challenges for dealing
with expanded scale, extended modality and inevitable batch effects. Deep learning-based …