Probe-Level Analysis of Affymetrix GeneChip Microarray Data

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An Alternative Method forFitting a PLM• Robust regression using M-estimation• In this talk, we will use Huber’s influencefunction ψ . The software handles manymore.• Fitting algorithm is IRLS with weightsdependent on current residualsψ rr( )• Software for fitting such models is part ofaffyPLM package of Bioconductor
Variance CovarianceEstimates• Suppose model is Y = Xβ + ε• Huber (1981) gives three forms for estimatingvariance covariance matrix2κ1/( n−p) ψ ( r)⎡⎢1/n⎣∑i∑iψ ′( r)i⎤⎥⎦i22(TX X )−11/( n−p)ψ riiκ1/ n ψ ′ r∑i∑( )( )i2W−11 1/−( ) ( ) (Tn−p ∑ψr )iW X X Wκi2 1 −1We will use this formT 'W = X Ψ X
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 and 34: Sort Sort columns of of originalmat
Page 35 and 36: General Probe Level Modelyij= f( X)
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: • Advantages-Fast- Very robust•
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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