Your browser doesn't support javascript.
loading
Applications of weighted association networks applied to compositional data in biology.
Espinoza, Josh L; Shah, Naisha; Singh, Suren; Nelson, Karen E; Dupont, Chris L.
Afiliação
  • Espinoza JL; J. Craig Venter Institute, La Jolla, USA.
  • Shah N; Applied Sciences, Durban University of Technology, Durban, South Africa.
  • Singh S; J. Craig Venter Institute, La Jolla, USA.
  • Nelson KE; Applied Sciences, Durban University of Technology, Durban, South Africa.
  • Dupont CL; J. Craig Venter Institute, La Jolla, USA.
Environ Microbiol ; 22(8): 3020-3038, 2020 08.
Article em En | MEDLINE | ID: mdl-32436334
ABSTRACT
Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biologia / Pesquisa Biomédica / Análise de Dados Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biologia / Pesquisa Biomédica / Análise de Dados Idioma: En Ano de publicação: 2020 Tipo de documento: Article