The sva package for removing batch effects and other unwanted variation in high-throughput experiments.
Bioinformatics
; 28(6): 882-3, 2012 Mar 15.
Article
em En
| MEDLINE
| ID: mdl-22257669
ABSTRACT
Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects-when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Estados Unidos