Distinguishing cell phenotype using cell epigenotype.
| Autor: | Wytock TP; Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA., Motter AE; Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA.; Northwestern Institute on Complex Systems, Evanston, IL 60208, USA.; Chicago Region Physical Sciences-Oncology Center, Northwestern University, Evanston, IL 60208, USA. |
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| Jazyk: | angličtina |
| Zdroj: | Science advances [Sci Adv] 2020 Mar 18; Vol. 6 (12), pp. eaax7798. Date of Electronic Publication: 2020 Mar 18 (Print Publication: 2020). |
| DOI: | 10.1126/sciadv.aax7798 |
| Abstrakt: | The relationship between microscopic observations and macroscopic behavior is a fundamental open question in biophysical systems. Here, we develop a unified approach that-in contrast with existing methods-predicts cell type from macromolecular data even when accounting for the scale of human tissue diversity and limitations in the available data. We achieve these benefits by applying a k -nearest-neighbors algorithm after projecting our data onto the eigenvectors of the correlation matrix inferred from many observations of gene expression or chromatin conformation. Our approach identifies variations in epigenotype that affect cell type, thereby supporting the cell-type attractor hypothesis and representing the first step toward model-independent control strategies in biological systems. (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).) |
| Databáze: | MEDLINE |
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