Version Compatibility

Reference examples tested with: sva 3.50+ (includes ComBat + ComBat_seq), DESeq2 1.42+, edgeR 4.0+, limma 3.58+, RUVSeq 1.36+, ggplot2 3.5+, harmony 1.2+ (single-cell context only)

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

• R: packageVersion('<pkg>') then ?function_name to verify parameters

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

Batch Effect Correction

"Remove the batch effect before DE" -> Almost always WRONG. Include batch as a covariate in the design formula (~ batch + condition) so DESeq2/edgeR/limma model it without subtracting. Subtraction is for visualization only.

The Single Most Important Modern Insight -- The Nygaard 2016 cardinal sin

Nygaard, Rødland, Hovig 2016 Biostatistics 17(1):29-39, "Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses." Translation: never run ComBat (or ComBat-seq, or removeBatchEffect, or SVA-subtract-then-test) and then run DE on the corrected matrix.

Mechanism: batch-correction methods fit a model y_ij = alpha + X_ij beta + gamma_i + delta_i epsilon_ij and subtract the batch terms. The downstream DE test then computes p-values as if those degrees of freedom had never been spent. Residual df is lower than what DESeq2/edgeR/limma assume. Type-I error inflates -- the gene list looks more significant than it should.