BBKNN: fast batch alignment of single cell transcriptomes
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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.
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BBKNN was used to integrate data across samples and correct for potential sample-wise batch effects.
“We then used the Python implementation of BBKNN (v.1.5.1), a fast batch correction algorithm suitable for large datasets to integrate data across the samples45. BBKNN calculates a batch-corrected neighbourhood graph from the imputed principal components. We set the individual sample IDs as batch labels to correct for potential sample-wise batch effects.”
From APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia (2024) · cites this paper as [45]
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- Authors
- Polański, K., Young, M. D., Miao, Z., Meyer, K. B., Teichmann, S. A., & Park, J.-E.
- Affiliations
- Wellcome Sanger Institute (GB)
- European Bioinformatics Institute (GB)
- University of Cambridge (GB)
- Corresponding author
- Teichmann, Sarah A
- Park, Jong-Eun
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-10
- Volume
- 36
- Issue
- 3
- Pages
- 964-965
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC9883685
- OpenAlex
- W2967177832
- Cited by
- 932
- References
- 12
- Citations by year
- 2026: 125 · 2025: 165 · 2024: 178 · 2023: 138 · 2022: 127 · 2021: 112 · 2020: 63 · 2019: 20 · 2018: 2
- FWCI
- 27.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Sanger core funding (WT206194)
- Wellcome (211276/Z/18/Z)
- EMBO Long-Term Fellowship
- Wellcome Trust (108437/Z/15/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- single-cell RNA sequencing, batch effect correction, data integration, computational efficiency, single-cell transcriptomics
- MeSH terms
- Exome Sequencing, RNA-Seq, Algorithms, Animals, Mice, Transcriptome
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