Poincaré maps for analyzing complex hierarchies in single-cell data.

The need to understand cell developmental processes spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry, a suboptimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincare maps, a method that harness the power of hyperbolic geometry into the realm of single-cell data analysis. Often understood as a continuous extension of trees, hyperbolic geometry enables the embedding of complex hierarchical data in only two dimensions while preserving the pairwise distances between points in the hierarchy. This enables the use of our embeddings in a wide variety of downstream data analysis tasks, such as visualization, clustering, lineage detection and pseudotime inference. When compared to existing methods — unable to address all these important tasks using a single embedding — Poincare maps produce state-of-the-art two-dimensional representations of cell trajectories on multiple scRNAseq datasets. The discovery of hierarchies in biological processes is central to developmental biology. Here the authors propose Poincare maps, a method based on hyperbolic geometry to discover continuous hierarchies from pairwise similarities.