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

Abstract : Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution. Given that many cellular differentiation pro- cesses are hierarchical, their scRNA-seq data is 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. Our two contributions are an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data, and a visualization method respecting the tree-structure. We extract the tree structure by means of a density based minimum spanning tree on a vector quantization of the data and show that it captures biological information well. We then introduce DTAE, a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space. We com- pare to other dimension reduction methods and demonstrate the success of our method experimentally. Our implementation relying on PyTorch and Higra is available at
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Contributor : Laurent Najman <>
Submitted on : Wednesday, February 10, 2021 - 12:55:56 PM
Last modification on : Tuesday, February 16, 2021 - 8:02:08 AM


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  • HAL Id : hal-03136103, version 1
  • ARXIV : 2102.05892



Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, et al.. Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. 2021. ⟨hal-03136103⟩



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