Fig. 6
From: scMC learns biological variation through the alignment of multiple single-cell genomics datasets
scMC is able to integrate a complex mouse lung scRNA-seq dataset across 16 batches. a UMAP of the corrected data from LIGER, Seurat V3, Harmony, and scMC on a mouse lung scRNA-seq dataset across 16 batches. Cells are colored by known sample origins (top panel) and annotated cell labels (bottom panel), respectively. b Evaluation of integration methods using 16 metrics, grouped into two categories: batch effect removal (i.e., Batch correction) and biological variation conservation (i.e., Bio conservation). LISI-derived F1 score is a summarized metric assessing both batch mixing and cell type separation. c Comparison of over scores among different methods