Effect of batch correction method
There were clear differences in all dimensions between batch correction methods. For all effects (infant sex, BMI, intervention and fake groups), supervised ComBat processing produced a larger number of DMPs compared to either unsupervised ComBat processing or adjustment for batch in the analysis model. The difference between unsupervised ComBat and batch-adjustment was less consistent for infant sex effects, but for BMI, intervention and fake group effects, there were no DMPs in unsupervised ComBat models, whereas there were a few for batch-adjusted models.Rankings of top probes by The distribution of Of particular note is the combination of SQN normalisation and either adjustment for batch in the model, or use of supervised ComBat. These combinations produced an extremely large number of significant DMPs for fake group effects; this was more extreme in the case of supervised ComBat (producing over 6,000 DMPs) than when adjusting for batch (somewhat over 2,000 DMPs). Additional adjustment for cell type proportion ameliorated this effect, as discussed below, but in the case of supervised ComBat data, did not eliminate spurious findings. This suggests that batch adjustment may be particularly ill-advised in the context of SQN normalisation; since SQN involves between-array as well as within-array normalisation, additional adjustment for batch may be over-correcting.