Empirical evaluation of data normalization methods for molecular classification

BACKGROUND: Data artifacts due to variations in experimental handling are ubiquitous in microarray studies, and they can lead to biased and irreproducible findings. A popular approach to correct for such artifacts is through post hoc data adjustment such as data normalization. Statistical methods fo...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Huei-Chung, Qin, Li-Xuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2018
Materias:
Bioinformatics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5899419/
https://www.ncbi.nlm.nih.gov/pubmed/29666754
http://dx.doi.org/10.7717/peerj.4584

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5899419/
https://www.ncbi.nlm.nih.gov/pubmed/29666754
http://dx.doi.org/10.7717/peerj.4584

Ejemplares similares