Batch effect correction is an essential step in the integrative analysis of multiple single cell RNA-seq (scRNA-seq) data. One state-of-the-art strategy for batch effect correction is via unsupervised or supervised detection of mutual nearest neighbors (MNNs). However, both two kinds of methods only detect MNNs across batches on the top of uncorrected data, where the large batch effect may affect the MNN search. To address this issue, we presented iSMNN, a batch effect correction approach via iterative supervised MNN refinement across data after correction. Our benchmarking on both simulation and real datasets showed the advantages of the iterative refinement of MNNs on the performance of correction. Compared to popular alternative methods, our iSMNN is able to better mix the cells of the same cell type across batches. In addition, iSMNN can also facilitate the identification of differentially expression genes (DEGs) relevant to the biological function of certain cell types. These results indicated that iSMNN will be a valuable method for integrating multiple scRNA-seq datasets that can facilitate biological and medical studies at single-cell level.
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