BBKNN: fast batch alignment of single cell transcriptomes

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.

In vitroandin vivodevelopment of the human intestinal niche at single cell resolution

“…Fetal tissue samples were batch corrected using BBKNN prior to dimensional reduction by principal component calculation and UMAP (McInnes et al, 2018; Polański et al, 2019). Genes were not included in the analysis if they were not sufficiently statistically invariable between cells.…”

Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development

“…Diffusion Pseudotime was used to reconstruct the trajectory from least mature cells to final cell fate(s) as previously described (Haghverdi et al, 2016; Wolf et al, 2018). Batch effect corrections, if necessary, were conducted prior to determination of highly variable genes, unless the BBKNN test was used (Polański et al, 2019). The parameters used for assigning pseudotime values within Scanpy are listed in Table S3.…”

Single-cell sequencing of developing human gut reveals transcriptional links to childhood Crohn’s disease

“…To remove variation of each 10X Genomics run and maintain the development related biological variation, we used batch balanced k nearest neighbour (BBKNN) method (Polański et al, 2019) on 40 principal components and trim parameter set to 20. Dimensionality reduction was performed on remaining highly variable genes and cells were visualised using Uniform Manifold Approximation and Projection (UMAP) plots (Becht et al, 2018).…”