Hi
I’m seeing RNA-seq batch effects even when everything is “kept constant.” Any real-world fixes that actually worked for you in the lab?
Regards
Barry
RNA-seq batch effects are common even when protocols are “kept constant” because hidden variables (RNA integrity nuances, operator habits, reagent lots, timing, thermal exposure) inevitably creep in. The most effective fix is deliberate randomization and interleaving: mix all conditions within every extraction day, library prep batch, plate, and sequencing lane so batch effects are orthogonal to biology.
Then you can treat RNA extraction as a major batch source and record it explicitly. ERCC spike-ins help diagnose where batch effects arise but don’t fix them.
For analysis, model batch directly (e.g., ComBat/ComBat-Seq when batch ≠ condition) rather than blindly correcting counts; overcorrection can erase real biology. RIN alone is insufficient—gene body coverage often reveals degradation-driven batch effects. Reagent lot changes matter and should be tracked. Priorities: randomize aggressively, include batch in DE models, and QC RNA degradation early.
Hopefully this will resolve your issues
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