Probe-Level Analysis of Affymetrix GeneChip Microarray Data

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Outline for Today's Talk• A brief introduction to the AffymetrixGeneChip Technology• Pre-processing– Background Correction– Normalization• Probe Level Modeling– Models– Applications
The Human Genome• The cell is the fundamental working unit of everyliving organism.• Humans: trillions of cells (metazoa);other organisms like yeast: one cell (protozoa).• Cells are of many different types (e.g. blood, skin,nerve cells), but all can be traced back to a singlecell, the fertilized egg.
Page 1: Probe-Level Analysis ofAffymetrix G
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 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%
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Limma “ebayes” t-statistic• G
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Probe Level Model teststatisticstPL
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What Happens as the Numberof Arrays
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ResultsMethod Individual Models Sin
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What is Going On Here?• Examine r
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Low Non-Differential
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High Non-Differential
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GeneLogic Tonsil dataset
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Mixture Data ResultsMethod 3 vs 3 4
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Quality Assessment using PLM• PLM
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An Image Gallery“Crop Circles”
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RLE PlotsRelativeLogExpression
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Discordant Arrays
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Ongoing Work in this Area• Techno
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Additional Slides
Page 97 and 98:
Chip dat file - checkered board - o
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From Chip To Data
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Computing ExpressionMeasures:A Thre
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Background Signal Methods• Affyme
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Convolution Model• O = S + N- O i
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What About Non Spike-ins?• We don
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Establishing a RelationshipγBetwee
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Normalization Methods• Methods al
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RMA Model• To each probeset (k),
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Advantages/Disadvantages of RMA/Med
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Comparing the backgroundmethods•
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Assessing Bias: ObservedFold-change
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Detecting DifferentialExpression: R
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Comparing theNormalization Methods
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Variability of Non-DifferentialGene
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ROC Curves
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Slope ValueAll 0.493Mid 0.665Low 0.
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Slope ValueAll 0.589Mid 0.751Low 0.
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Slope ValueAll 0.695Mid 0.82Low 0.5
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Slope: 0.484
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Slope: 0.583
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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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