Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors (original) (raw)

“…This approach will confound the batch effect with biological differences between cell types or states that are not shared among datasets. Data integration methods such as Canonical Correlation Analysis (CCA; Butler et al , ), Mutual Nearest Neighbours (MNN; Haghverdi et al , ), Scanorama (preprint: Hie et al , ), RISC (preprint: Liu et al , ), scGen (preprint: Lotfollahi et al , ), LIGER (preprint: Welch et al , ), BBKNN (preprint: Park et al , ), and Harmony (preprint: Korsunsky et al , ) have been developed to overcome this issue. While data integration methods can also be applied to simple batch correction problems, we recommend to be wary of over‐correction given the increased degrees of freedom of non‐linear data integration approaches.…”