Version Compatibility

Reference examples tested with: DESeq2 1.42+, apeglm 1.28+, ashr 2.2+, IHW 1.34+, tximport 1.30+, edgeR 4.0+ (for cross-comparison), PyDESeq2 0.5+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters
• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

DESeq2 Basics

"Find differentially expressed genes between conditions" -> Fit a negative-binomial GLM per gene with shared dispersion shrinkage, test the coefficient of interest (Wald) or the joint effect of a factor (LRT), and report a shrunken effect-size estimate for ranking.

The Single Most Important Modern Insight -- Shrunken LFC and the Wald p-value come from different models

lfcShrink() returns LFCs from a Bayesian posterior with apeglm/ashr/normal priors, BUT the p-value column it carries forward is still the unshrunken Wald p-value from results(). This is a deliberate design choice (Zhu, Ibrahim, Love 2019 Bioinformatics 35:2084) -- the shrunken estimate is for ranking and visualization; the p-value is for inference. Reporting "shrunken LFC = 0.4, padj = 1e-8" mixes two models, which is fine because both are correct for their stated purpose. What is NOT fine: using the shrunken LFC in a downstream filter and then claiming FDR control on that filter (it has none). For threshold-based FDR claims, use lfcThreshold= or TREAT (glmTreat in edgeR).