A maximum-likelihood approach for building cell-type trees by lifting |
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Authors: | Nair Nishanth Ulhas Hunter Laura Shao Mingfu Grnarova Paulina Lin Yu Bucher Philipp E Moret Bernard M |
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Institution: | 1.School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, Lausanne, CH-1015, Switzerland ;2.Computer Science Department, Stanford University, Stanford, USA ;3.Department of Computer Science and Engineering, University of California, San Diego, San Diego, USA ;4.School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland ;5.Swiss Institute of Bioinformatics, Lausanne, Switzerland ; |
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Abstract: | Background In cell differentiation, a less specialized cell differentiates into a more specialized one, even though all cells in one organism have (almost) the same genome. Epigenetic factors such as histone modifications are known to play a significant role in cell differentiation. We previously introduce cell-type trees to represent the differentiation of cells into more specialized types, a representation that partakes of both ontogeny and phylogeny. ResultsWe propose a maximum-likelihood (ML) approach to build cell-type trees and show that this ML approach outperforms our earlier distance-based and parsimony-based approaches. We then study the reconstruction of ancestral cell types; since both ancestral and derived cell types can coexist in adult organisms, we propose a lifting algorithm to infer internal nodes. We present results on our lifting algorithm obtained both through simulations and on real datasets. ConclusionsWe show that our ML-based approach outperforms previously proposed techniques such as distance-based and parsimony-based methods. We show our lifting-based approach works well on both simulated and real data. |
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