TooManyCells identifies and visualizes relationships of single-cell clades.

Abstract

Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering “resolution” hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a novel visualization model built on a concept intentionally orthogonal to dimensionality reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering wholly different from prevalent single-resolution clustering methods. Together, TooManyCells enables multi-resolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug resistance acquisition in leukemic T cells.