A test metric for assessing single-cell RNA-seq batch correction

By Maren Büttner, Zhichao Miao, F. Alexander Wolf, Sarah A. Teichmann, Fabian J Theis. Nature Methods, 2018

433 citations recorded by the source corpus.

Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations, but as with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch-effect correction is often evaluated by visual inspection of low-dimensional embeddings, which are inherently imprecise. Here we present a user-friendly, robust and sensitive k-nearest-neighbor batch-effect test (kBET; https://github.com/theislab/kBET) for quantification of batch effects. We used kBET to assess commonly used batch-regression and normalization approaches, and to quantify the extent to which they remove batch effects while preserving biological variability. We also demonstrate the application of kBET to data from peripheral blood mononuclear cells (PBMCs) from healthy donors to distinguish cell-type-specific inter-individual variability from changes in relative proportions of cell populations. This has important implications for future data-integration efforts, central to projects such as the Human Cell Atlas. kBET informs attempts at single-cell RNA-seq data integration by quantifying batch effects and determining how well batch regression and normalization approaches remove technical variation while preserving biological variability.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/97bc1f7808ba732210d996fa392c5650e7efd0c1

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Citations in the corpus

Cross-tissue immune cell analysis reveals tissue-specific features in humans (2022, methodology): “Data integration was done using BBKNN (70) and scVI (71), and the results were compared using kBET (72).”
The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution (2020, positive): “Technical hurdles can be addressed with both batch correction methods developed for bulk genomics data (Büttner et al., 2019; Johnson et al., 2007) and newer methods developed for single-cell genomics (Luecken and Theis, 2019).”
Methods and applications for single-cell and spatial multi-omics (2023, background): “In the case of horizontal data integration across different batches, this can lead to overcorrection of true biological variation 158 , especially when studying subtle shifts in cellular state in different experimental conditions.”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “Another local level metric is the LISI proposed by Kor-sunsky et al. [13, 22], which can be used to assesses batch and cell type mixing.”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “Scran has been shown to perform better than other tested normalization methods for batch correction (Buttner et al , 2019) and differential expression analysis (preprint: Vieth et al, 2019).”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “Several methods have been recently designed to mitigate the batch eﬀects in scRNA-seq data, such as MNN (mutual nearest neighbor) (Haghverdi et al., 2018) and kBET (k-nearest neighbor batch eﬀect test) (Buttner et al., 2019).”