Version Compatibility

Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, limma 3.58+, pandas 2.2+, numpy 1.26+, scanpy 1.10+, scran 1.30+, scater 1.30+, EDASeq 2.36+, cqn 1.48+

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.

Expression Matrix Normalization

"Normalize my counts for X" -> Pick a method that matches the downstream task (raw counts for DE; TMM/RLE-scaled CPM for cross-sample comparison; VST/rlog for visualization/ML; scran for single-cell) and the data structure (bulk vs single-cell, zero-heavy or not, length-biased or not).

The Single Most Important Modern Insight -- TMM/RLE assume most genes are NOT DE; that assumption is wrong in MYC, apoptosis, viral host shutoff, and prokaryotic stress

TMM (Robinson & Oshlack 2010 Genome Biol 11:R25) and RLE/median-of-ratios (Anders & Huber 2010 Genome Biol 11:R106) both rely on the assumption that the MAJORITY of genes are unchanged between samples. The scaling factor is computed from a trimmed reference. When that assumption holds (most bulk RNA-seq), both methods are excellent.