Estimating batch effects with SVA

Batch effects occur in scientific experiments when there are systematic differences in the measurements that are made between different groups of samples, even though the samples themselves are biologically the same. These differences can be caused by various factors, such as differences in the lab conditions, the equipment used, or the time of the experiment. In RNA-Seq experiments, batch effects can occur when samples are run on different sequencing platforms or at different times, leading to differences in the read counts between samples. This can affect the statistical power of the experiment, as well as introduce bias into the analysis.

One common approach to address batch effects in RNA-Seq experiments is to use the surrogate variable analysis (SVA) Bioconductor package. The SVA package uses a statistical method to identify and correct the batch effects by identifying sources of variation in the data that are likely to be caused by technical...