Such effects can lead to inaccurate conclusions when their causes are correlated with one or more outcomes of interest in an experiment.
They are common in many types of high-throughput sequencing experiments, including those using microarrays, mass spectrometers,[1] and single-cell RNA-sequencing data.
[2] They are most commonly discussed in the context of genomics and high-throughput sequencing research, but they exist in other fields of science as well.
Focusing on microarray experiments, they propose a new definition based on several previous ones: "[T]he batch effect represents the systematic technical differences when samples are processed and measured in different batches and which are unrelated to any biological variation recorded during the MAGE [microarray gene expression] experiment.
They have historically mostly focused on genomics experiments, and have only recently begun to expand into other scientific fields such as proteomics.