(This was written back in 2013, and I can't necessarily vouch for any of the claims within it.)

Questions

Overarching question: Can we accurately distinguish AR from TX?
Can we work well in "clinical" mode, i.e. classifying single samples?
- How to normalize new sample with training set?
- How to avoid recalculating classifier for each sample?
Can we perform well on an external validation set (GEO data)?
- Are the same genes predictive in both datasets?
- Can a classifier trained on our data perform well on GEO data?

Experiments

pam-analysis.R
- How important is it to normalize to the training set? (RMA separate vs together)
- Conclusion: must normalize together. Separate introduced bias toward one class or the other.
- Question: how to do it with a single sample?
pam-analysis-norm.R
- Can single-channel normalization improve classification results? Yes.
- Try PAM with RMA and two single-channel normalizations
- fRMA improves cross-dataset accuracy from 65% to 71%.
limma-analysis-norm.R
- What is the source of the variation