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

• Published in: Nature methods Vol. 16; no. 1; pp. 43 - 49
• Main Authors: Büttner, Maren, Miao, Zhichao, Wolf, F. Alexander, Teichmann, Sarah A., Theis, Fabian J.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2019; Nature Publishing Group
• Subjects: 631/114/2401; 631/114/794; 631/208/199; Algorithms; Analysis; Bias; Bioinformatics; Biological effects; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Cells (Biology); Cluster Analysis; Data analysis; Data integration; Data processing; Datasets; Experiments; Gene expression; Genomes; Genomics; Hypotheses; Integration; Laboratories; Leukocytes (mononuclear); Life Sciences; Nearest-neighbor; Neighborhoods; Peripheral blood mononuclear cells; Populations; Principal components analysis; Proteomics; Ribonucleic acid; RNA; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/s41592-018-0254-1

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.