ScMC Learns Biological Variation Through the Alignment of Multiple Single-Cell Genomics Datasets

The study introduced scMC, a method for integrating single-cell genomics datasets that effectively distinguished between technical and biological variations. scMC utilized variance analysis to remove technical variation while preserving biological signals, outperforming existing methods like LIGER, Seurat V3, and Harmony, especially in datasets with imbalanced cell populations. It was tested on both simulated and real datasets, including human PBMCs and mouse skin scRNA-seq data, where it successfully identified biologically meaningful subpopulations, such as fibroblasts involved in wound healing. scMC facilitated improved downstream analyses and was made available as an R package, proving to be a valuable tool for analyzing complex single-cell datasets.