title: The sva package for removing batch effects and other unwanted variation in high-throughput experiments.
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.
authors.affiliation: Department of Biostatistics, JHU Bloomberg School of Public Health, Baltimore, MD, USA. jleek@jhsph.edu
authors.forename: Jeffrey T
authors.initials: JT
authors.lastname: Leek
authors.forename: W Evan
authors.initials: WE
authors.lastname: Johnson
authors.forename: Hilary S
authors.initials: HS
authors.lastname: Parker
authors.forename: Andrew E
authors.initials: AE
authors.lastname: Jaffe
authors.forename: John D
authors.initials: JD
authors.lastname: Storey
mesh_terms: D020869:Gene Expression Profiling; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D012044:Regression Analysis; D012984:Software; D001749:Urinary Bladder Neoplasms / Q000235:genetics