Maqola

The <tt>sva</tt> package for removing batch effects and other unwanted variation in high-throughput experiments

Jeffrey T. Leek1 Department of Biostatistics, JHU Bloomberg School of Public Health, Baltimore, MD, 2Division of Computational Biomedicine, Boston University, Boston, MA, 3Department of Epidemiology, JHU Bloomberg School of Public Health, Baltimore, MD and 4Lewis-Sigler Institute, Department of Molecular Biology, Princeton University, Princeton, NJ, USAW. Evan Johnson1 Department of Biostatistics, JHU Bloomberg School of Public Health, Baltimore, MD, 2Division of Computational Biomedicine, Boston University, Boston, MA, 3Department of Epidemiology, JHU Bloomberg School of Public Health, Baltimore, MD and 4Lewis-Sigler Institute, Department of Molecular Biology, Princeton University, Princeton, NJ, USAHilary S. Parker1 Department of Biostatistics, JHU Bloomberg School of Public Health, Baltimore, MD, 2Division of Computational Biomedicine, Boston University, Boston, MA, 3Department of Epidemiology, JHU Bloomberg School of Public Health, Baltimore, MD and 4Lewis-Sigler Institute, Department of Molecular Biology, Princeton University, Princeton, NJ, USAAndrew E. Jaffe1 Department of Biostatistics, JHU Bloomberg School of Public Health, Baltimore, MD, 2Division of Computational Biomedicine, Boston University, Boston, MA, 3Department of Epidemiology, JHU Bloomberg School of Public Health, Baltimore, MD and 4Lewis-Sigler Institute, Department of Molecular Biology, Princeton University, Princeton, NJ, USAJohn D. Storey1 Department of Biostatistics, JHU Bloomberg School of Public Health, Baltimore, MD, 2Division of Computational Biomedicine, Boston University, Boston, MA, 3Department of Epidemiology, JHU Bloomberg School of Public Health, Baltimore, MD and 4Lewis-Sigler Institute, Department of Molecular Biology, Princeton University, Princeton, NJ, USA

2012en

ABI

Annotatsiya

Abstract Summary: 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. Availability: The R package sva is freely available from http://www.bioconductor.org. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Hali tarjima qilinmagan

Identifikatorlar

DOI: 10.1093/bioinformatics/bts034

Iqtiboslar va manbalar

3 ta iqtibos0 ta foydalanilgan manba

Koʻrsatkichlar — AkademScholar