- 是什么 ？
Batch effects are sub-groups of measurements that have qualitatively different behaviour across conditions and are unrelated to the biological or scientific variables in a study. For example, batch effects may occur if a subset of experiments was run on Monday and another set on Tuesday, if two technicians were responsible for different subsets of the experiments or if two different lots of reagents, chips or instruments were used.
- 为啥产生?
Many technologies used in biology including high-throughput ones such as microarrays, bead chips, mass spectrometers and second generation sequencing depend on a complicated set of reagents and hardware, along with highly trained personnel, to produce accurate measurements.
When these conditions vary during the course of an experiment, many of the quantities being measured will be simultaneously affected by both biological and non-biological factors.
Batch effects occur because measurements are affected by laboratory conditions, reagent lots and personnel differences.
- 为什么要去除？
Normalization is a data analysis technique that adjusts global properties of measurements for individual samples so that they can be more appropriately compared. Including a normalization step is now standard in data analysis of gene expression experiments
But normalization does not remove batch effects, which affect specific subsets of genes and may affect different genes in different ways. In some cases, these normalization procedures may even exacerbate technical artefacts in high-throughput measurements, as batch and other technical effects violate the assumptions of normalization methods.
- 方法有哪些？
Empirical Bayes method (ComBat)
singular-value decomposition (SVD)
distance weighted discrimination (DWD)
Kolmogorov-Smirnov test (BEclear)
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