Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Autor/innen
- L. Haghverdi
- A.T.L. Lun
- M.D. Morgan
- J.C. Marioni
Journal
- Nature Biotechnology
Quellenangabe
- Nat Biotechnol 36 (5): 421-427
Zusammenfassung
Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.