BBKNN: fast batch alignment of single cell transcriptomes

TLDR

Large‑scale single‑cell RNA‑seq projects generate a data explosion that requires integration, yet existing batch‑effect removal methods are computationally intensive. The authors developed BBKNN, an extremely fast graph‑based algorithm to integrate large single‑cell RNA‑seq datasets. BBKNN constructs a batch‑corrected nearest‑neighbour graph, is implemented in Python, available on GitHub and pip, and benchmarked on large mouse atlas data. On large mouse atlas data, BBKNN achieves faster run times than competing integration methods while preserving biological structure. Supplementary data are available online at Bioinformatics.

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.