Abstract

Abstract 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 methods have been developed to combine diverse datasets 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 algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods. Availability and implementation BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip. Supplementary information Supplementary data are available at Bioinformatics online.

Keywords

Computer scienceBenchmark (surveying)DocumentationGraphData integrationData miningScale (ratio)Theoretical computer scienceProgramming language

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Publication Info

Year
2019
Type
article
Volume
36
Issue
3
Pages
964-965
Citations
907
Access
Closed

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Cite This

Krzysztof Polański, Matthew D. Young, Zhichao Miao et al. (2019). BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics , 36 (3) , 964-965. https://doi.org/10.1093/bioinformatics/btz625

Identifiers

DOI
10.1093/bioinformatics/btz625