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

Nature Communications
Anna KlimovskaiaMaximilian Nickel

Abstract

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 Poincaré 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 - Poincaré maps produce state-of-the-art two-dimensional representations of cell trajectories on multiple scRNAseq datasets.

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Citations

Mar 23, 2021·IScience·Yuansheng Zhou, Tatyana O Sharpee
May 1, 2021·Biology Methods and Protocols·Hirotaka MatsumotoTsukasa Fukunaga
Jun 19, 2021·Computational and Structural Biotechnology Journal·Hitoshi IuchiMichiaki Hamada
Sep 2, 2021·Annual Review of Biomedical Data Science·Angela Oliveira PiscoAaron McGeever

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Methods Mentioned

BETA
PCA
scRNAseq

Software Mentioned

PAGA
PHATE
SIMLR
scVI
UMAP
SNE
ForceAtlas2
Monocle
PCA
SAUCIE

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