Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder | Zendy

Quentin Garrido | Zendy; Sebastian Damrich | Zendy; Alexander Jäger | Zendy; Dario Cerletti | Zendy; Manfred Claassen | Zendy; Laurent Najman | Zendy; Fred A. Hamprecht | Zendy

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Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder

Author(s) -

Quentin Garrido,

Sebastian Damrich,

Alexander Jäger,

Dario Cerletti,

Manfred Claassen,

Laurent Najman,

Fred A. Hamprecht

Publication year - 2022

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac249

Subject(s) - autoencoder , tree (set theory) , computer science , tree structure , inference , visualization , dimensionality reduction , artificial intelligence , data mining , representation (politics) , data structure , pattern recognition (psychology) , machine learning , mathematics , artificial neural network , mathematical analysis , programming language , politics , political science , law

Single-cell RNA sequencing (scRNA-seq) allows studying the development of cells in unprecedented detail. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data are expected to be approximately tree-shaped in gene expression space. Inference and representation of this tree structure in two dimensions is highly desirable for biological interpretation and exploratory analysis.

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