Open AccessVisualizing 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 , computer science , tree (set theory) , tree structure , inference , visualization , data mining , artificial intelligence , dimensionality reduction , data structure , pattern recognition (psychology) , machine learning , mathematics , artificial neural network , mathematical analysis , programming language
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.