- Page 1 and 2: Probe-Level Analysis ofAffymetrix G
- Page 3: The Human Genome• The cell is the
- Page 8 and 9: Exons and introns• Genes comprise
- Page 10 and 11: Functional genomics• The various
- Page 12 and 13: RNA• A ribonucleic acid or RNA mo
- Page 14 and 15: Brief TechnologyOverview• High de
- Page 16 and 17: Two Probe TypesReference SequenceTA
- Page 18 and 19: Sample Preparation
- Page 20 and 21: The Chip is Scanned
- Page 22 and 23: Chip cel file - checkered boardCour
- Page 24 and 25: High-Level Analysis• Clustering/C
- Page 26 and 27: Background/SignalAdjustment• A me
- Page 28 and 29: ECorrection is given by( )SO= o = a
- Page 30 and 31: Non-Biological Variability5 scanner
- Page 32 and 33: Quantile Normalization• Normalize
- Page 34 and 35: It Reduces VariabilityExpression Va
- Page 36 and 37: Parallel Behavior SuggestsMulti-chi
- Page 38 and 39: Also Want Robustness
- Page 40 and 41: The Three Steps of RMA1. Convolutio
- Page 42 and 43: One DrawbackRMA MAS 5.0Some fixes f
- Page 44 and 45: Median Polish Algorithmy11 L y1J0M
- Page 46 and 47: An Alternative Method forFitting a
- Page 48 and 49: We Will Focus on theSummarization P
- Page 50 and 51: How Do We Know WhichGenes are Diffe
- Page 52 and 53: Testing for DifferentialExpression
- Page 54 and 55:
Simple t-statistict=Xlsnm2s2l ml−
- Page 56 and 57:
Simple Moderated t-statistict=snl2s
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Probe Level Model teststatisticsΣ
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A First Comparison• 8 chips from
- Page 70 and 71:
A Larger Comparison• Look at the
- Page 72:
More Spike-in Datasets• Two GeneL
- Page 76 and 77:
Affymetrix Spike-ins
- Page 78 and 79:
Middle Non-Differential
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GeneLogic AML Spike-ins
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How About With More“Real” Data?
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Moderation for the PLMtest statisti
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Pseudo-chip imagesWeightsResidualsP
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NUSE PlotsNormalizedUnscaledStandar
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Discordant Probes
- Page 92 and 93:
Morals for Today’s Talk• The
- Page 94 and 95:
Acknowledgements• Terry Speed (UC
- Page 96 and 97:
Focusing on a SingleGeneChip Cell L
- Page 98 and 99:
Chip dat file - checkered board - c
- Page 100 and 101:
Constructing a geneexpression measu
- Page 102 and 103:
Background Methods• Affymetrix- L
- Page 104 and 105:
Original RMA Background• Convolut
- Page 106 and 107:
A Standard Curve AdjustmentBased on
- Page 108 and 109:
γRelates to Concentration
- Page 110 and 111:
The Two Curves Yield anAdjustment C
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Why Quantile Normalization?• Quan
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Basic RMA modelLetthenyij= log N B2
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Probe Level Models are• RMA metho
- Page 118 and 119:
Assessing Bias: ObservedExpression
- Page 120:
Assessing Variability:M vs A plots
- Page 128 and 129:
Summary of Trade-offsBackgroundMeth
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Scaling is Not Sufficient
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Little effect on Spike-insMethod Al
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Comparing EstablishedExpression Mea
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Slope ValueAll 0.63Mid 0.784Low 0.3
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Slope ValueAll 0.69Mid 0.82Low 0.52
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Slope ValueAll 0.856Mid 1.041Low 0.
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Slope: 0.624
- Page 147 and 148:
Slope: 0.683
- Page 149 and 150:
Slope: 0.847
- Page 152:
We Will Focus on TwoParticular PLM