Question 4 of 6 Intermediate Concept
What is the difference between normalized counts and log-fold change estimates?
Short answer
Normalized counts are abundance measurements adjusted for library-scale factors. Log2 fold change estimates compare expected expression between conditions within a statistical model. Normalized counts help describe expression, while log-fold changes estimate the condition contrast and its uncertainty.
Why
For gene X, normalized counts of 100 and 200 in representative control and treated samples suggest greater expression in treatment. A model estimating twice the expression produces log2 fold change near +1. But a model also accounts for biological variation and any design covariates, so its estimated contrast need not equal the ratio of two displayed counts.
Normalization adjusts differences such as library size. Effect estimation asks how treatment relates to the mean count after accounting for dispersion and specified predictors. When genes have few counts or high variance, fold changes can be unstable.
Moderated or shrunken estimates may improve ranking or visualization under their assumptions. Never treat a high normalized count as evidence of differential expression by itself. Explain that uncertainty, multiple testing and independent replicates determine confidence in the contrast, while TPM or other relative abundance summaries serve different purposes.
Common mistakes
- Interpreting a twofold difference between two normalized counts as a validated significant treatment effect
The interviewer might ask next
Why might a gene with high apparent fold change have low statistical confidence?