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.

Trib1 regulates T cell differentiation during chronic infection by restraining the effector program

“…We used TooManyCells, a scRNA-seq hierarchal spectral clustering and visualization tool ( Schwartz et al, 2020 ), to stratify transcriptionally distinct subpopulations of control and Trib1 cKO cells. We observed a distinct cluster (“Cluster 1,” Fig.…”

While We Wait for a Vaccine Against SARS-CoV-2, Why Not Think About Available Drugs?

“…Such developments make it possible to undertake transcriptome-wide analysis of differential gene expression and, differential splicing of mRNAs, and establish the tissue distribution of molecular constituents in individual cells (spatial transcriptomics, "spacialomics") with unprecedented discriminative power that surpasses conventional antibody-based immunocytochemistry (for a review see (Stark et al 2019)). Another promising technique of recent implementation is "TooManyCells", a suite of graph-based algorithms that can be applied to partition scRNA-seq data to resolve and visualize clusters of cells and establish their relationships in an unbiased manner (Schwartz et al 2020). With this technique only relatively rare sets of cells, representing only a mere 0.5% of the population, could be resolved.…”

Identifying persistent structures in multiscale ‘omics data

“…We compared HiDeF to TooManyCells 16 and Conos 17 as baseline methods. The former is a divisive method which iteratively applies bipartite spectral clustering to the cell population until the modularity of the partition is below a threshold; the latter uses the Walktrap algorithm to agglomeratively construct the cell-type hierarchy 46 .…”

“…The former is a divisive method which iteratively applies bipartite spectral clustering to the cell population until the modularity of the partition is below a threshold; the latter uses the Walktrap algorithm to agglomeratively construct the cell-type hierarchy 46 . TooManyCells (version 0.2.2.0) was run with the parameter " min-modularity " set to 0.025 as recommended in the original paper 16 , with other settings set to default. This process generated dendrograms (binary trees) with 463 communities.…”

“…We used groups of cells sharing the same annotations to define a panel of 81 reference cell types and measured the degree to which each reference cell type could be recapitulated by a HiDeF community of cells ( Methods ). We compared these results to TooManyCells 16 and Conos 17 , two recently developed methods that generate nested communities of single cells in divisive and agglomerative manners, respectively ( Methods ). Reference cell types tended to better match communities generated by HiDeF than those of other approaches, with 65% (54/81) having a highly overlapping community (Jaccard index > 0.5) in the HiDeF hierarchy ( Fig.…”