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

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 …

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 …

Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow
us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary …

The ability to profile hundreds of thousands to millions of single cells using scRNA-
sequencing has revolutionized the fields of cell and developmental biology, providing …

Distinguishing biological from technical variation is crucial when integrating and comparing
single-cell genomics datasets across different experiments. Existing methods lack the …

Efficient integration of heterogeneous and increasingly large single-cell RNA sequencing
data poses a major challenge for analysis and, in particular, comprehensive atlasing efforts …

The ability to align individual cellular information from multiple experimental sources is
fundamental for a systems-level understanding of biological processes. However, currently …

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 …

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 …