BBKNN: fast batch alignment of single cell transcriptomes

By Krzysztof Polanski, Matthew D. Young, Zhichao Miao, Kerstin B. Meyer, Sarah A. Teichmann, Jong-Eun Park. Bioinform., 2019

758 citations recorded by the source corpus.

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.

Subfields: Geology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e6368f1cb43fb26b0f006ebd711984d5a8cd530f

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In Molecular Biology

Citations in the corpus

A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “BBKNN is another method that first computes the k - nearest neighbors in a dimensionally reduced principal component space [10].”
Multiple early factors anticipate post-acute COVID-19 sequelae (2022, positive): “PCs were used for batch-corrected (using sequencing batch) neighborhood graph, bbkNN (Pola (cid:1) nski et al., 2020) construction then UMAP and Leiden (unbiased clustering) cluster calculations were conducted.”
Large Scale Foundation Model on Single-cell Transcriptomics (2023, positive): “Given that batch effects or modality differences may encompass a range of variations, we took the strategy in scFoundation to consider only read depth and leave other possible differences to cooperative methods on downstream tasks, such as BBKNN and SCAD.”
Computational principles and challenges in single-cell data integration (2021, neutral): “BBKNN performs correction on a neighborhood graph, which results in much faster computation at the expense of losing single-cell resolution.”
Cross-tissue immune cell analysis reveals tissue-specific features in humans (2022, methodology): “BBKNN: fast batch alignment of single cell transcriptomes.”