BBKNN: fast batch alignment of single cell transcriptomes
Bioinformatics, 2020•academic.oup.com
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 …
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 …
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.
Oxford University Press