Fig. 1
From: A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Benchmarking 14 methods on ten datasets using five evaluation metrics. a Benchmarking workflow. We evaluated the performance of 14 batch correcting algorithms in terms of their ability to integrate batches while maintaining accuracy in terms of cell type separation. We employed t-SNE and UMAP visualizations in conjunction with the kBET, LISI, ASW, ARI, and DEG benchmarking metrics to evaluate the batch correction results. b Description of the ten datasets on which the batch correction algorithms were tested