CycleMix: Gaussian Mixture Modeling of the Cell Cycle

The cell cycle is a crucial component of many biological processes, including cancer, tissue repair, and inflammation. However, due to the heterogeneity of this cycle it has been difficult to assess the extent of proliferation in clinical tissues. Single-cell RNAseq (scRNAseq) and spatial transcriptomics enables high resolution measurement of gene expression enabling the classification of individual cells into their cycling state. The most widely used method for cell-cycle assignment is Seurat, however their approach is limited to classifying cells into only 3 states: G1, S, G2M and frequently incorrectly labels fully mature non-cycling cells (e.g. neurons) as S/G2M. Here we propose CycleMix, an alternative cell-cycle assignment algorithm that can flexibly assign cells into any number of states provided sufficient marker genes as well as being capable of identifying when cells are not cycling.