RT Journal Article
SR Electronic
T1 Fast Batch Alignment of Single Cell Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 397042
DO 10.1101/397042
A1 Jong-Eun Park
A1 Krzysztof Polański
A1 Kerstin Meyer
A1 Sarah A. Teichmann
YR 2018
UL http://biorxiv.org/content/early/2018/08/22/397042.abstract
AB 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. Therefore, efficient computational tools for combining diverse datasets are crucial for biology in the single cell genomics era. A number of methods have been developed to assist data integration by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration method. We illustrate the power of BBKNN for dimensionalityreduced visualisation and clustering in multiple biological scenarios, including a massive integrative study over several murine atlases. BBKNN successfully connects cell populations across experimentally heterogeneous mouse scRNA-Seq datasets, which reveals global markers of cell type and organspecificity and provides the foundation for inferring the underlying transcription factor network. BBKNN is available at https://github.com/Teichlab/bbknn.