1. Evaluation of Affymetrix array normalization procedures based on spiked cRNAs Andrew Hill Expression Profiling Informatics Genetics Institute/Wyeth-Ayerst Research

2. October 11, 20012 Outline The GI/Harvard C. elegans array dataset as a normalization testbed Some general challenges of array data reduction GeneChip Scaled Average Difference (ADs) –the constant mean assumption A purely spike-based normalization strategy (Frequency) A hybrid normalization (Scaled Frequency) Conclusions

3. October 11, 20013 GI/Harvard C. elegans dataset This data set used to evaluate several normalization procedures Experiments: –8 developmental stages of the worm C. elegans were profiled, ranging from egg to adult worm –n=2-4 replicate hybridizations for most array designs at most stages –52 total arrays Arrays: –Three custom worm GeneChip designs (A, B, and C) –Each array monitors between 5700-6700 ORFs, in aggregate ~98% of the worm genome –Chip A: ORFs with cDNA/EST matches in AceDB –Chips B/C: other ORFs –Several worm ORFs tiled on all 3 arrays for across-array-design comparisons Science 290 809-812; Genome Biology (in the press)

4. October 11, 20014 Some challenges of Affymetrix GeneChip data reduction Array data from Affymetrix GeneChip sofware (pre-MAS 5.0): –negative low intensity signals –lack of across-design normalization standard –limited QC information Spike-based normalization methods can help to address each of these challenges Normalization: array scaling of average difference data from multiple arrays/designs to minimize technical noise among arrays Current “standard” normalization procedure is a global scaling procedure: the GeneChip scaled average difference (ADs)

5. October 11, 20015 GeneChip Scaled Average Difference (ADs) The trimmed (2%) mean intensity of all probesets on all arrays is scaled to a constant target level. Works well in many cases (e.g. replicates) Some obvious situations where the “constant mean assumption” may not be well supported.

6. October 11, 20016 Constant mean assumption: problematic cases Chips monitoring a “small” fraction of transcriptome Non-random gene selection on arrays (e.g. C. elegans A vs. B/C) Large biological variation in expression

7. October 11, 20017 A cRNA spike-based normalization procedure (Frequency) Add 11 biotin-labeled cRNA spikes to each hybridization cocktail Construct a calibration curve Use the Absent/Present calls for the spikes to estimate array sensitivity Dampen AD signals below the sensitivity level to eliminate negative AD values.

8. October 11, 20018 Eleven spiked cRNAs

9. October 11, 20019 Figure 2 Response to spikes over 2.5 log range Fit response with S-plus GLM, gamma error model, zero intercept. Power law fit AD=kF n yields n=0.93 cRNA mass, scanner PMT gain are important determinants of response

10. October 11, 200110 Chip sensitivity calculation Consider A/P calls as binary response against log(known frequency) Compute sensitivity as 70% likelihood level by either interpolation or logistic regression “Dampen” computed frequencies below sensitivity: F < 0: F’ = avg(0,S) 0<F<S: F’=avg(F,S)

11. October 11, 200111 How well does it work?

12. October 11, 200112 Reproducibility of F metric (A array)

13. October 11, 200113 Example of spike-skewed hybridization (36 hr sample) cRNA spikes are well normalized at the expense of worm genes Suggests inconsistency between ratio of spikes to worm cRNA across samples: spike skew

14. October 11, 200114 Sources of spike skew Actual concentration of spikes may not be nominal due to variation in cRNA “purity” Causes: liquid handling of small microlitre volumes, side reactions in cDNA/IVT process produce UV- absorbing, non-hybridizable contaminants Result: random per-hybe noise term introduced into normalized frequencies

15. October 11, 200115 An alternative hybrid normalization: Scaled frequency (Fs) Need to reduce or eliminate spike skew as a source of experimental variation in normalized frequencies Average the globally scaled spike response over a complete set of arrays

16. October 11, 200116 Scaled frequency description Define a set of arrays Compute ADs for all arrays Pool spike responses and fit single model to pooled response Calibrate all arrays with single calibration factor Compute array sensitivity and dampen frequencies as in the frequency approach.

17. October 11, 200117 A pooled, scaled spike response Fit response with S-plus GLM, gamma error model, zero intercept.

18. October 11, 200118 Reproducibility of Fs metric (A array)

19. October 11, 200119 Scaled frequency: cross design reproducibility (A,B,C arrays) Three messages tiled on all array designs and called Present on all 0h arrays

20. October 11, 200120 Conclusions Array response to spiked cRNAs can be close to linear over 2.5 logs of concentration. A chip sensitivity metric can be computed from Absolute Decisions associated with spikes; a very useful QC metric. Normalization based only on spikes performs inconsistently in some cases due to ill-quantitation of cRNAs, but can still be valuable when constant-mean assumption is violated. Better cRNA quantitation and process control will help. A hybrid approach based on global scaling and spikes performs the same as global AD scaling for single designs, and also allows cross-design comparisons

21. October 11, 200121 Acknowledgements Donna Slonim Maryann Whitley Yizheng Li Bill Mounts Scott Jelinsky Gene Brown Harvard University: Craig Hunter Ryan Baugh

22. October 11, 200122 Extra slides follow ( not part of presentation)

23. October 11, 200123 Simulations (description) Simulations were performed Governing equation:

24. October 11, 200124 Figure 4 CV characteristics of simulated data

25. October 11, 200125 Simulations: spike skew degrades reproducibility of frequency (A array)

26. October 11, 200126 Figure 7 Simulations: spike skew degrades accuracy of frequency