cKBET: assessing goodness of batch effect correction for single-cell RNA-seq

Abstract

Single-cell RNA sequencing reveals the gene structure and gene expression status of a single cell, which can reflect the heterogeneity between cells. However, batch effects caused by non-biological factors may hinder data integration and downstream analysis. Although the batch effect can be evaluated by visualizing the data, which actually is subjective and inaccurate. In this work, we propose a quantitative method cKBET, which considers the batch and cell type information simultaneously. The cKBET method accesses batch effects by comparing the global and local fraction of cells of different batches in different cell types. We verify the performance of our cKBET method on simulated and real biological data sets. The experimental results show that our cKBET method is superior to existing methods in most cases. In general, our cKBET method can detect batch effect with either balanced or unbalanced cell types, and thus evaluate batch correction methods.

Graphical abstract

Keywords

single-cell RNA-seq dataset / batch effect assessment / cKBET method

Yameng Zhao obtained her Bachelor degree from Hunan University, China in 2020. She is currently a doctoral candidate at School of Mathematics and Statistics in Xi’an Jiaotong University, China. Her research interest is the applications in bioinformatics

Yin Guo obtained her Bachelor degree from Minzu University of China, China in 2018. She is currently a doctoral candidate at School of Mathematics and Statistics in Xi’an Jiaotong University, China. Her research interest is the applications in bioinformatics

Limin Li obtained her Bachelor and Master degrees from Zhejiang University, China in 2004 and 2006, respectively. She got her PhD degree in mathematics at the University of Hong Kong, China in 2010. She then worked as a postdoctoral fellow in Max Planck Institute of Intelligent System. She is currently a professor at School of Mathematics and Statistics in Xi'an Jiaotong University, China. Her research interests include machine learning and the applications in bioinformatics

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Acknowledgements

The work was supported by the NSFC projects (Grant No. 11631012).

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