The sva package for removing batch effects and other unwanted variation in high-throughput experiments
The sva package for removing batch effects and other unwanted variation in high-throughput experiments
复制标题
DOI：
10.1093/bioinformatics/bts034
复制
发表时间：
2012-03-15
期刊：
影响因子：
5.8
通讯作者：
Storey, John D.
中科院分区：
文献类型：
--
作者：
Leek, Jeffrey T.;Johnson, W. Evan;Storey, John D.
异质性和潜在变量现在被广泛认为是高通量实验中偏差和变异性的主要来源。基因组实验中最著名的潜在变异来源是批效应——当样本在不同的日子、不同的小组或由不同的人处理时。然而，还有大量其他变量可能对高通量测量产生重大影响。在这里，我们描述了用于识别、估计和去除高通量实验中不需要的变异源的sva包。sva包支持使用sva函数进行代理变量估计，使用ComBat函数直接调整已知批处理效果，以及使用fsva函数调整预测问题中的批处理和潜在变量。
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.