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

Maren Büttner, Zhichao Miao, F. Alexander Wolf, Sarah A. Teichmann, Fabian J. Theis

Research output: Contribution to journalArticlepeer-review

251 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)43-49
Number of pages7
JournalNature Methods
Volume16
Issue number1
DOIs
StatePublished - 1 Jan 2019

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