Reference-based deep examination of single-cell RNA-seq clusters
Identifying the biological state of each single cell is arguably the most challenging task in scRNA-seq data analysis. Doing so requires bridging the gap between the current dataset and prior biological knowledge, and the latter is not always available in a consistent manner. To improve this process, we’ve developed reference datasets for mouse and human containing over 60,000 samples of over 200 cell types each, from publicly available studies. Using these reference datasets, we’ve developed a refined version of our previously published scRNA-seq annotation tool SingleR, which allows us to discover new findings within scRNA-seq datasets, such as cell type annotations, errors made in previous annotation and clustering attempts, subtypes or substates of cells and clusters, identification of rare or diseased cell types, and more.
