Automated Chip QC

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Automated Chip QC

• Michael Elashoff

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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)

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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)

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

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Spots, Scratches, etc.
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Spots, Scratches, etc.
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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

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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

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Implementation of Li-Wong

• Compare distributions of outlier count for
  passing and failing chips in training set
• Determine upper bound of acceptable outlier
  count

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Grid Alignment
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Grid Alignment
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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

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Haze Band
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Haze Band
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Crop Circles
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Crop Circles
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Using Spike-Ins
Spike-in R2 must be gt96.5
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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

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QC Metrics
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QC Metrics Performance
Two week validation run
False Negative Rate 0.4 These will not be
manually QCd anymore
False Positive Rate 46.8 These are still
manually QCd
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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

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Thanks

• Peter Lauren
• Chris Alvares
• John Klein
• Michelle Nation
• Jeff Wiser