Selected Publication

23.12.2018

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

In datasets consisting of several experimental batches, kBET compares the local composition of cell labels to the global composition to detect shifts across batches.

Maren Büttner and Fabian J. Theis from the ICB developed a user-friendly, robust and sensitive k-nearest neighbour batch effect test (kBET, https://github.com/theislab/kBET) to quantify the bias caused by batch effects in single-cell RNA-seq experiments. Together with their collaboration partners Chichao Miao and Sarah A. Teichmann from the Wellcome Trust Sanger Institute in Hinxton, UK, they compared common normalization and batch correction tools for their ability to remove batch effects. 

Furthermore, kBET allows distinguishing 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. 

For more information, see the original publication: Büttner, M. et al. (2018): A test metric for assessing single-cell RNA-seq batch correction, Nature Methods.

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