1. Biostatistics Grand Rounds March 13, 2001 Discussion of Richard Simon’s talk: Using DNA Microarrays to Improve Cancer Diagnostic Classification

2. Outline How is classification different? Is classification and/or clustering always appropriate? What’s the contribution of Statistics? Are current analyses tools always appropriate?

3. Classification http://linus.nci.nih.gov/~brb/TechReport.htm “NN classifiers and DLDA had the lowest error rates, whereas FLDA had the highest” Dudoit, Fridlyand, and Speed (2001) JASA 97 77-87

5. Clustering/Classification always appropriate? Common problem: Find differentially expressed genes in two populations A good design is important.

6. Dye effect?

7. Contribution of Statistics “Not as simple as looking for red and green spots” How big is big? Critical exploration of Data Garbage in, garbage out

8. Saturation problem Probes reaching maximum in experiment 1 ScannerChipPM MMValue 2NV21a 354 25 46140 2NV21b 564 57 46144 2V21a 1004 83 46141 2V21b 665 51 46139 1NV100a 1917 328 46154 1NV100b 1265 168 46160 1V100a 3399 1085 46155 1V100b 2267 446 46149

9. Scanner Effect

10. Remove Scanner Effect

11. Top 2.5%of ratios red, bottom 2.5% of ratios green The red-green ratios can be spatially biased

12. Another example

13. Looking for large FC