A maximum-likelihood approach for building cell-type trees by lifting.
BMC Genomics
; 17 Suppl 1: 14, 2016 Jan 11.
Article
em En
| MEDLINE
| ID: mdl-26819094
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Epigenômica
Limite:
Humans
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article