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Automated Chip QC

Automated Chip QC. Michael Elashoff. 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). Chip QC: Defect Classes. In order of occurrence: Dimness High Background

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Automated Chip QC

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  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 A chip Low Scan Passing Chips Bright/Dim Chips

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

  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.

  8. 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

  13. Grid Alignment

  14. 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

  15. Haze Band

  16. Haze Band

  17. Crop Circles

  18. Crop Circles

  19. Using Spike-Ins Spike-in R2 must be >96.5%

  20. QC Metrics • Mean of Non-control Oligo Intensity • Mean OligoB2 Intensity • Spike-in R2 • Li-Wong Outlier Count • Several measures of LiWong Outlier “clustering” • Vertical profiles • Horizontal profiles • Thresholds differ for each chip type

  21. QC Metrics

  22. 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

  23. 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

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

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