Glossary
Batch effect
In a large study, the batch a sample ran in can explain more variance than the biology. Recognizing and limiting batch effects is part of designing a study that can actually be analyzed.
Definition
A batch effect is systematic, nonbiological variation associated with how and when samples were prepared and measured. In LC/MS it appears as shifts in retention, response, and detected features grouped by plate, day, operator, or column. Batch effects are a problem because they can dominate the leading components of an unsupervised analysis, so patterns that look biological may reflect the processing schedule instead. They are limited by randomization and quality control during acquisition and by correct feature matching and normalization during analysis, but they cannot be fully removed after the fact if design was poor.
Worked example
In practice
If cases were run in one week and controls in the next, a difference between groups may reflect the two weeks of instrument state rather than disease, which is why randomization across batches matters.
What matters
Where this makes a difference
Design first
Randomization and quality control injections limit how far batch effects reach. No correction fully substitutes for design, because some batch differences are confounded with the contrast of interest.
Matching before correction
Intensity based batch correction assumes features are already matched across batches. If matching is imperfect, correction spreads the error, so feature level alignment should come first.
How to spot it
Color an unsupervised plot by batch. If samples cluster by run order rather than biology, batch effects are dominating and need to be addressed before interpretation.
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