title: A maximum-likelihood approach for building cell-type trees by lifting.
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.
  
  
  RESULTS
  We 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.
  
  
  CONCLUSIONS
  We 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.
authors.affiliation: 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. nishanth.u.nair@gmail.com.
authors.forename: Nishanth Ulhas
authors.initials: NU
authors.lastname: Nair
authors.affiliation: Computer Science Department, Stanford University, Stanford, USA. hunterlauram@gmail.com.
authors.forename: Laura
authors.initials: L
authors.lastname: Hunter
authors.affiliation: 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. shaomingfu@gmail.com.
authors.forename: Mingfu
authors.initials: M
authors.lastname: Shao
authors.affiliation: 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. pgrnarova@gmail.com.
authors.forename: Paulina
authors.initials: P
authors.lastname: Grnarova
authors.affiliation: Department of Computer Science and Engineering, University of California, San Diego, San Diego, USA. biolinyu@gmail.com.
authors.forename: Yu
authors.initials: Y
authors.lastname: Lin
authors.affiliation: School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. philipp.bucher@epfl.ch.|Swiss Institute of Bioinformatics, Lausanne, Switzerland. philipp.bucher@epfl.ch.
authors.forename: Philipp
authors.initials: P
authors.lastname: Bucher
authors.affiliation: 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. bernard.moret@epfl.ch.|Swiss Institute of Bioinformatics, Lausanne, Switzerland. bernard.moret@epfl.ch.
authors.forename: Bernard M
authors.initials: BM
authors.lastname: E Moret
mesh_terms: D000465:Algorithms; D002460:Cell Line; D047369:Chromatin Immunoprecipitation; D057890:Epigenomics; D006657:Histones / Q000145:classification / Q000378:metabolism; D006801:Humans; D016013:Likelihood Functions; D010802:Phylogeny
chemical_list: D006657:Histones
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