1. Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data Rafael A. Irizarry Department of Biostatistics, JHU (joint work with Leslie Cope, Ben Bolstad, Francois Collin, Bridget Hobbs, and Terry Speed) http://biosun01.biostat.jhsph.edu/~ririzarr

2. Summary Review of technology Probe level summaries Normalization Assess technology and expression measures Conclusion/future work

3. Probe Arrays 24µm Millions of copies of a specific oligonucleotide probe Image of Hybridized Probe Array Image of Hybridized Probe Array >200,000 different complementary probes Single stranded, labeled RNA target Oligonucleotide probe * * * * *1.28cm GeneChip Probe Array Hybridized Probe Cell Compliments of D. Gerhold

4. PM MM

5. Data and Notation PM ijn, MM ijn = Intensity for perfect/mis-match probe cell j, in chip i, in gene n i = 1,…, I (ranging from 1 to hundreds) j=1,…, J (usually 16 or 20) n = 1,…, N (between 8,000 and 12,000)

6. The Big Picture Summarize 20 PM,MM pairs (probe level data) into one number for each gene We call this number an expression measure Affymetrix GeneChip’s Software has defaults. Does it work? Can it be improved?

7. What is the evidence? Lockhart et. al. Nature Biotechnology 14 (1996)

8. Competing Measures of Expression GeneChip ® software uses Avg.diff with A a set of “suitable” pairs chosen by software. Log ratio version is also used. For differential expression Avg.diffs are compared between chips.

9. Competing Measures of Expression GeneChip ® new version uses something else with MM* a version of MM that is never bigger than PM.

10. Competing Measures of Expression Li and Wong fit a model Consider expression in chip i Efron et. al. consider log PM – 0.5 log MM Another is second largest PM

11. Competing Measures of Expression Why not stick to what has worked for cDNA? with A a set of “suitable” pairs.

12. Features of Probe Level Data

13. SD vs. Avg

14. ANOVA: Strong probe effect 5 times bigger than gene effect

15. Normalization at Probe Level

16. Spike-In Experiments Set A: 11 control cRNAs were spiked in, all at the same concentration, which varied across chips. Set B: 11 control cRNAs were spiked in, all at different concentrations, which varied across chips. The concentrations were arranged in 12x12 cyclic Latin square (with 3 replicates)

17. Set A: Probe Level Data

18. What Did We Learn? Don’t subtract or divide by MM Probe effect is additive on log scale Take logs

19. Why Remove Background?

20. Background Distribution

21. RMA “Background correct” PM Normalize (quantile normalization) Assume additive model: Estimate a i using robust method

22. Spike-In B Probe SetConc 1Conc 2Rank BioB-51000.51 BioB-30.525.02 BioC-52.075.04 BioB-M1.037.54 BioDn-31.550.05 DapX-335.73.06 CreX-350.05.07 CreX-512.52.08 BioC-325.01009 DapX-55.01.510 DapX-M3.01.011 Later we consider 23 different combinations of concentrations

23. Differential Expression

27. Observed Ranks GeneAvDiffMAS 5.0Li&WongAvLog(PM-BG) BioB-56211 BioB-316132 BioC-574625 BioB-M30373 BioDn-344564 DapX-323924 7 CreX-333373369 CreX-532763331288 BioC-3270985726816431 DapX-527091021220310 DapX-M16519136 Top 1515610

28. Observed vs True Ratio

29. Dilution Experiment cRNA hybridized to human chip (HGU95) in range of proportions and dilutions Dilution series begins at 1.25  g cRNA per GeneChip array, and rises through 2.5, 5.0, 7.5, 10.0, to 20.0  g per array. 5 replicate chips were used at each dilution Normalize just within each set of 5 replicates For each probe set compute expression, average and SD over replicates

30. Dilution Experiment Data

31. Expression

32. SD

33. Log Scale SD

34. Model check Compute observed SD of 5 replicate expression estimates Compute RMS of 5 nominal SDs Compare by taking the log ratio Closeness of observed and nominal SD taken as a measure of goodness of fit of the model

35. Observed vs. Model SE

36. Conclusion Take logs PMs need to be normalized Using global background improves on use of probe-specific MM Gene Logic spike-in and dilution study show technology works well RMA is arguably the best summary in terms of bias, variance and model fit Future: What stastistic should we use to rank?

37. Acknowledgements Gene Brown’s group at Wyeth/Genetics Institute, and Uwe Scherf’s Genomics Research & Development Group at Gene Logic, for generating the spike-in and dilution data Gene Logic for permission to use these data Magnus Åstrand (Astra Zeneca Mölndal) Skip Garcia, Tom Cappola, and Joshua Hare (JHU)