Probe-Level Analysis of Affymetrix GeneChip Microarray Data

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It Reduces VariabilityExpression ValuesFold changeAlso no serious bias effects. For more see Bolstad et al (2003)
General Probe Level Modelyij= f( X)+εij• Where f(X) is function of factor (and possiblycovariate) variables (our interest will be inlinear functions)• y ij is a pre-processed probe intensity(usually log scale)• Assume that E⎡ ⎤ = 0Var⎣ε ij⎦⎡⎣ε⎤ij ⎦ = σ2
Page 1 and 2: Probe-Level Analysis ofAffymetrix G
Page 3 and 4: The Human Genome• The cell is the
Page 6: DNA• A deoxyribonucleic acid or D
Page 9 and 10: Differential expression• Each cel
Page 11 and 12: Central dogma• The expression of
Page 13 and 14: Idea: measure the amount of mRNA to
Page 15 and 16: Probes and Probesets
Page 17 and 18: Constructing the Chip
Page 19 and 20: Hybridization to the Chip
Page 21 and 22: Chip dat file - checkered board - c
Page 23 and 24: Levels of Analysis• Probe level-
Page 25 and 26: Probe-Level Analysis• What is Pro
Page 27 and 28: RMA Background Approach• Convolut
Page 29 and 30: Normalization“Non-biological fact
Page 31 and 32: Non-linear normalizationneededUnnor
Page 33: Sort Sort columns of of originalmat
Page 37 and 38: Probe Pattern SuggestsIncluding Pro
Page 39 and 40: Summarization• Problem: Calculati
Page 41 and 42: RMA mostly does well inpracticeDete
Page 43 and 44: The RMA modely = m+ α + β + εij
Page 45 and 46: • Advantages-Fast- Very robust•
Page 47 and 48: Variance CovarianceEstimates• Sup
Page 49 and 50: Detecting DifferentialExpression•
Page 51 and 52: Affymetrix Spike-in Data• 59 chip
Page 53 and 54: Fold ChangeFC = X − XlmWhereXl=
Page 55 and 56: “Robust” t-statistict=X% − X%
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: Mixture Data ResultsMethod 3 vs 3 4
Page 85 and 86:
Quality Assessment using PLM• PLM
Page 87 and 88:
An Image Gallery“Crop Circles”
Page 89 and 90:
RLE PlotsRelativeLogExpression
Page 91 and 92:
Discordant Arrays
Page 93 and 94:
Ongoing Work in this Area• Techno
Page 95 and 96:
Additional Slides
Page 97 and 98:
Chip dat file - checkered board - o
Page 99 and 100:
From Chip To Data
Page 101 and 102:
Computing ExpressionMeasures:A Thre
Page 103 and 104:
Background Signal Methods• Affyme
Page 105 and 106:
Convolution Model• O = S + N- O i
Page 107 and 108:
What About Non Spike-ins?• We don
Page 109 and 110:
Establishing a RelationshipγBetwee
Page 111 and 112:
Normalization Methods• Methods al
Page 113 and 114:
RMA Model• To each probeset (k),
Page 115 and 116:
Advantages/Disadvantages of RMA/Med
Page 117 and 118:
Comparing the backgroundmethods•
Page 119 and 120:
Assessing Bias: ObservedFold-change
Page 127 and 128:
Detecting DifferentialExpression: R
Page 129:
Comparing theNormalization Methods
Page 134 and 135:
Variability of Non-DifferentialGene
Page 136 and 137:
ROC Curves
Page 138 and 139:
Slope ValueAll 0.493Mid 0.665Low 0.
Page 140 and 141:
Slope ValueAll 0.589Mid 0.751Low 0.
Page 142 and 143:
Slope ValueAll 0.695Mid 0.82Low 0.5
Page 144 and 145:
Slope: 0.484
Page 146 and 147:
Slope: 0.583
Page 148 and 149:
Slope: 0.692
Page 150:
What About the TreatmentEffect Mode
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Probe-Level Analysis of Affymetrix GeneChip Microarray Data

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