Evaluation of Affymetrix Array Normalization Strategies Using Spiked cRNAs in C. elegans

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

2. 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. 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. 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. 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. 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. 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. Eleven spiked cRNAs

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

10. 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. How well does it work?

12. Reproducibility of F metric (A array)

13. 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. 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. 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. 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. A pooled, scaled spike response • Fit response with S-plus GLM, gamma error model, zero intercept.

18. Reproducibility of Fs metric (A array)

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

20. 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. Acknowledgements • Donna Slonim • Maryann Whitley • Yizheng Li • Bill Mounts • Scott Jelinsky • Gene Brown • Harvard University: • Craig Hunter • Ryan Baugh

22. Extra slides follow ( not part of presentation)

23. Simulations (description) • Simulations were performed • Governing equation:

24. Figure 4 CV characteristics of simulated data

25. Simulations: spike skew degrades reproducibility of frequency (A array)

26. Figure 7 Simulations: spike skew degrades accuracy of frequency