From a Gene List to Biology: Enrichment Tests and Their Backgrounds
Enrichment analysis asks whether a gene list is unusually full of some annotated category, and the answer depends entirely on the background set chosen. This stop runs the test properly and names the biases that make it lie.
What a learner can do afterwards
- Run and interpret an over-representation test against a stated, defensible background set
- Explain how gene length and expression level bias enrichment results
- Compare an over-representation test with a rank-based method on the same data
1 · Read
A differential expression experiment ends with a list of hundreds of changed genes. You cannot interpret them one by one, so you group genes by shared function and ask about pathways instead. The over-representation test asks whether a pathway contributes more changed genes than random drawing would predict.
The calculation is only as honest as the background set you choose. The background must hold exactly the genes that stood a chance of selection. Any gene never measured, or filtered out upstream, must stay out, or the result is invalid.
Assays do not sample genes evenly. Long transcripts shed more fragments, and highly expressed genes are easier to call changed. A test that ignores this rediscovers assay bias and labels it biology. You correct it by modelling each gene's prior chance of selection from its length or expression level.
Check every hit three ways before you believe it. Use a defensible background, correct for length and expression effects, and rerun with a rank-based method that walks the full ordered list. A term that survives all three steps deserves follow-up.
State the background, correct the bias, confirm with a second method, then trust the term.
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Where it sits
8 questions wait behind this lesson, each with its answer explained. Every answer feeds the sky: stars light as they are learned, and dim when it is time to come back.