1. Automated Chip QC Michael Elashoff

2. Chip QC Transition from mostly manual/visual chip QC to mostly automated chip QC Database of passing and failing chips to serve as the training set (5K passing, 2K failing)

3. Chip QC: Defect Classes In order of occurrence: –Dimness –High Background –Unevenness –Spots –Haze Band –Scratches –Brightness –Crop Circle –Cracked –Snow –Grid Misalignment Training set of 7K chips (Human, Rat, Mouse)

4. Dimness/Brightness Passing Chips Bright/Dim Chips A chip Low Scan

5. Dimness/Brightness Passing Chips Bright/Dim Chips A chip Low Scan

6. Dimness/Brightness Each chip type has a different typical brightness range Typical brightness range depends on scanner setting –tuned-up versus tuned-down –scanners must be calibrated to achieve consistency

7. Spots, Scratches, etc.

9. Implementation of Li-Wong With training set of 5K passing chips, apply Li-Wong algorithm For each probe set, algorithm yields: –“outlier” status for each probe-pair –probe weights for non-outlier probe-pairs

10. Implementation of Li-Wong For QC, new chips are screened individually For each probe set: –Ignore “model outlier” probes –Using training ‘s, compute –Compute residuals for each probe pair –Flag residuals that are large

11. Implementation of Li-Wong Compare distributions of outlier count for passing and failing chips in training set Determine upper bound of acceptable outlier count:

12. Grid Alignment

15. Limitations of Li-Wong Must estimate 1.8 million probe weights for human/rat chip sets Works poorly for rare genes Probe weights may vary –Tissue Type –RNA Processing –Chip Lot –Training Set

16. Haze Band

18. Crop Circles

20. Using Spike-Ins Spike-in R 2 must be >96.5%

21. QC Metrics Mean of Non-control Oligo Intensity Mean OligoB2 Intensity Spike-in R 2 Li-Wong Outlier Count Several measures of LiWong Outlier “clustering” Vertical profiles Horizontal profiles Thresholds differ for each chip type

22. QC Metrics

23. QC Metrics: Performance Two week validation run False Negative Rate = 0.4% These will not be manually QC’d anymore False Positive Rate = 46.8% These are still manually QC’d

24. Conclusions Automated QC has: –reduced the number of chips in visual QC –made the process more objective Automated QC has not: –eliminated the need for visual QC –incorporated the impact on real world data quality/analysis

25. Thanks Peter Lauren Chris Alvares John Klein Michelle Nation Jeff Wiser