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What Happens as the Numberof Arrays Increases?• Expand comparison to all 24 Arrays withsame concentration profiles fromAffymetrix HG-U95A spike-in dataset• Fit an array effect model to all 24 arrays• Look at comparisons between equalnumber of chips
Page 2 and 3:
Outline• What is Low-Level Analys
Page 4 and 5:
High-Level Analysis• Clustering/C
Page 6 and 7:
Brief TechnologyOverview• High de
Page 8 and 9:
Two Probe TypesPM: the Perfect Matc
Page 10 and 11: Focusing on a SingleGeneChip Cell L
Page 12 and 13: Hybridization to the Chip
Page 14 and 15: Chip dat file - checkered board - o
Page 16 and 17: Chip dat file - checkered board - c
Page 18 and 19: Constructing a geneexpression measu
Page 20 and 21: Background/SignalAdjustment• A me
Page 22 and 23: Normalization“Non-biological fact
Page 24 and 25: Summarization• Reduce the 11-20 p
Page 26 and 27: Affymetrix Spike-in Data• 59 chip
Page 28 and 29: Assessing Bias:Observed Expression
Page 30: Assessing Variability:M vs A plots
Page 38 and 39: Summary of Trade-offsBackgroundMeth
Page 43 and 44: Scaling is Not Sufficient
Page 45 and 46: Little effect on Spike-insMethodAll
Page 47 and 48: Comparing EstablishedExpression Mea
Page 49 and 50: General Probe LevelModelyij= f( X)+
Page 51 and 52: Fitting the PLM• Robust regressio
Page 53 and 54: Fold ChangeFC = X − XlmWhereXl=
Page 55 and 56: “Robust” t-statistict=Xlsnm2s2l
Page 57 and 58: Limma “ebayes” t-statistic• G
Page 59 and 60: Probe Level Model teststatisticstPL
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
Page 80 and 81: GeneLogic AML Spike-ins
Page 82 and 83: How About With More“Real” Data?
Page 84: What about the treatmenteffect mode
Page 87 and 88: Acknowledgements• Terry Speed (UC
Page 89 and 90: Background Signal Methods• Affyme
Page 91 and 92: Convolution Model• O = S + N- O i
Page 93 and 94: What About Non Spike-ins?• We don
Page 95 and 96: Establishing a RelationshipγBetwee
Page 97 and 98: Quantile Normalization• Normalize
Page 99 and 100: Why Quantile Normalization?• Quan
Page 101 and 102: Probe Level Models are• RMA metho
Page 103 and 104: Advantages/Disadvantages of RMA/Med
Page 105 and 106: SlopeAllMidLowHighValue0.630.7840.3
Page 107 and 108: SlopeAllMidLowHighValue0.690.820.52
Page 109 and 110: SlopeAllMidLowHighValue0.8561.0410.
Page 111 and 112:
Slope: 0.624
Page 113 and 114:
Slope: 0.683
Page 115:
Slope: 0.847
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