Hyperbolic geometry of gene expression

Summary: Patterns of gene expressions play a key role in determining cell state. Although correlations in gene expressions have been well documented, most of the current methods treat them as independent variables. One way to take into account gene correlations is to find a low-dimensional curved ge...

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Main Authors: Yuansheng Zhou, Tatyana O. Sharpee
Format: Article
Language:English
Published: Elsevier 2021-03-01
Series:iScience
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2589004221001930
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author Yuansheng Zhou
Tatyana O. Sharpee
author_facet Yuansheng Zhou
Tatyana O. Sharpee
author_sort Yuansheng Zhou
collection DOAJ
description Summary: Patterns of gene expressions play a key role in determining cell state. Although correlations in gene expressions have been well documented, most of the current methods treat them as independent variables. One way to take into account gene correlations is to find a low-dimensional curved geometry that describes variation in the data. Here we develop such a method and find that gene expression across multiple cell types exhibits a low-dimensional hyperbolic structure. When more genes are taken into account, hyperbolic effects become stronger but representation remains low dimensional. The size of the hyperbolic map, which indicates the hierarchical depth of the data, was the largest for human cells, the smallest for mouse embryonic cells, and intermediate in differentiated cells from different mouse organs. We also describe how hyperbolic metric can be incorporated into the t-SNE method to improve visualizations compared with leading methods.
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spelling doaj.art-0be56d57ad284b489787fd8f9e19ffe62022-12-21T23:41:35ZengElsevieriScience2589-00422021-03-01243102225Hyperbolic geometry of gene expressionYuansheng Zhou0Tatyana O. Sharpee1Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USAComputational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Department of Physics, University of California San Diego, La Jolla, CA 92093, USA; Corresponding authorSummary: Patterns of gene expressions play a key role in determining cell state. Although correlations in gene expressions have been well documented, most of the current methods treat them as independent variables. One way to take into account gene correlations is to find a low-dimensional curved geometry that describes variation in the data. Here we develop such a method and find that gene expression across multiple cell types exhibits a low-dimensional hyperbolic structure. When more genes are taken into account, hyperbolic effects become stronger but representation remains low dimensional. The size of the hyperbolic map, which indicates the hierarchical depth of the data, was the largest for human cells, the smallest for mouse embryonic cells, and intermediate in differentiated cells from different mouse organs. We also describe how hyperbolic metric can be incorporated into the t-SNE method to improve visualizations compared with leading methods.http://www.sciencedirect.com/science/article/pii/S2589004221001930GenesCell BiologyComplex Systems
spellingShingle Yuansheng Zhou
Tatyana O. Sharpee
Hyperbolic geometry of gene expression
iScience
Genes
Cell Biology
Complex Systems
title Hyperbolic geometry of gene expression
title_full Hyperbolic geometry of gene expression
title_fullStr Hyperbolic geometry of gene expression
title_full_unstemmed Hyperbolic geometry of gene expression
title_short Hyperbolic geometry of gene expression
title_sort hyperbolic geometry of gene expression
topic Genes
Cell Biology
Complex Systems
url http://www.sciencedirect.com/science/article/pii/S2589004221001930
work_keys_str_mv AT yuanshengzhou hyperbolicgeometryofgeneexpression
AT tatyanaosharpee hyperbolicgeometryofgeneexpression