Thesis/graphics/PAM

(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