Fig. 1

Schematics of the MC2 algorithm. The input is a large UMI matrix, and the output is a partition of cell into metacells and final outlier cells. MC2 is deriving a solution using recursive two-phase process. It first divides the data into random piles and generates low-quality metacells and outliers from them. It then (recursively) groups low-quality metacells into coherent piles and repartitions these piles to generate high-quality metacells. The algorithm is ensuring high sensitivity of rare behavior detection by identifying rare metacell through a pre-process, as well as through regrouping of outlier cells that are being pooled from all piles at both phases of the algorithm