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Affymetrix Spike-in Data• 59 chips. All but 1 of the rows are done as triplicates37777 684 1597 38734 39058 36311 36889 1024 36202 36085 40322 407 1091 1708A 0 0.25 0.5 1 2 4 8 16 32 64 128 0 512 1024B 0.25 0.5 1 2 4 8 16 32 64 128 256 0.25 1024 0C 0.5 1 2 4 8 16 32 64 128 256 512 0.5 0 0.25D 1 2 4 8 16 32 64 128 256 512 1024 1 0.25 0.5E 2 4 8 16 32 64 128 256 512 1024 0 2 0.5 1F 4 8 16 32 64 128 256 512 1024 0 0.25 4 1 2G 8 16 32 64 128 256 512 1024 0 0.25 0.5 8 2 4H 16 32 64 128 256 512 1024 0 0.25 0.5 1 16 4 8I 32 64 128 256 512 1024 0 0.25 0.5 1 2 32 8 16J 64 128 256 512 1024 0 0.25 0.5 1 2 4 64 16 32K 128 256 512 1024 0 0.25 0.5 1 2 4 8 128 32 64L 256 512 1024 0 0.25 0.5 1 2 4 8 16 256 64 128M 512 1024 0 0.25 0.5 1 2 4 8 16 32 512 128 256N 512 1024 0 0.25 0.5 1 2 4 8 16 32 512 128 256O 512 1024 0 0.25 0.5 1 2 4 8 16 32 512 128 256P 512 1024 0 0.25 0.5 1 2 4 8 16 32 512 128 256Q 1024 0 0.25 0.5 1 2 4 8 16 32 64 1024 256 512R 1024 0 0.25 0.5 1 2 4 8 16 32 64 1024 256 512S 1024 0 0.25 0.5 1 2 4 8 16 32 64 1024 256 512T 1024 0 0.25 0.5 1 2 4 8 16 32 64 1024 256 512
Comparing the backgroundmethods• Using an Affymetrix spike-in experiment weshall examine– Observed vs spike-in concentration– Observed vs expected fold change– Composite M vs A plots– ROC curves• In each case we will compute expressionvalues use standard RMA methodology. (iequantile normalization, median polishsummarization)
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 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 65: What Happens as the Numberof Arrays
Page 71 and 72: ResultsMethod Individual Models Sin
Page 75 and 76: What is going on here?• Examine r
Page 77 and 78:
Low Non-Differential
Page 79 and 80:
High Non-Differential
Page 81 and 82:
GeneLogic Tonsil dataset
Page 83 and 84:
ResultsMethod 3 vs 3 4 vs 4 5 vs 50
Page 86 and 87:
Ongoing work in this area• Techno
Page 88 and 89:
Additional Slides
Page 90 and 91:
Original RMA Background• Convolut
Page 92 and 93:
A Standard Curve AdjustmentBased on
Page 94 and 95:
γRelates to Concentration
Page 96 and 97:
The Two Curves Yield anAdjustment C
Page 98 and 99:
Sort columns of of originalmatrixTa
Page 100 and 101:
RMA Model• To each probeset (k),
Page 102 and 103:
Basic RMA modelLetthenyij= log N B2
Page 104 and 105:
SlopeAllMidLowHighValue0.4930.6650.
Page 106 and 107:
SlopeAllMidLowHighValue0.5890.7510.
Page 108 and 109:
SlopeAllMidLowHighValue0.6950.820.5
Page 110 and 111:
Slope: 0.484
Page 112 and 113:
Slope: 0.583
Page 114 and 115:
Slope: 0.692
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