1. A Distribution-Free Summarization Method for Affymetrix GeneChip Arrays
Zhongxue Chen, Monnie McGee, Qingzhong Liu and Richard Scheuermann
Dallas Area Bioinformatics Workshop
August 29, 2006

2. A new summarization method
Distribution Free Weighted (DFW) Summarization
Use information on variability of probe intensities to summarize Affymetrix data
Translate variability into weights which allow downweighting of poorly performing probes
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3. Need for Summarization
Result of unique Affymetrix array structure
Summarization is necessary to obtain one number for each gene
All probes interrogating each gene must be summarized into one expression value
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4. Structure of Affymetrix Arrays
Probe = sequence of 25 bases
Probe pair = perfect match (PM) probe and its corresponding mismatch (MM)
Probe set = 11 to 20 probe pairs interrogating one gene or EST
Chips contain 6K to 54K probe sets
Image courtesy of Affymetrix
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5. PM and MM
PM = 25 base probe perfectly complementary to a specific region of a gene
MM = 25 base probe agreeing with PM apart from middle base
Middle base is a transition to Watson-Crick complement (AT, G C)
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6. DFW
Transform probe-level intensities to log2 scale for all arrays in experiment
Stabilizes the variance (larger intensity  increased variability
Arrange arrays in N by R matrix
N = total number of PM probes
R = total number of arrays for entire experiment
For each probe set, calculate a weight for each PM probe using Tukey biweight function
Multiply weights by each probe intensity and summarize
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7. Calculating Weights Calculate range of log intensities for each PM
Find median of each range (M)
Calculate distance of range to M for each PM (call this distance x)
Weighting function:
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8. Probe Weights Weight for probe i is given by
J = number of probes in the probe set
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9. More Calculations Weighted Range (WR)
Range of weighted intensities
Weighted Standard Deviation (WSD)
Transformed Intensity Values (TIV)
Standardizes measures between DEGs and non-DEGs
m and n should be positive integers
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10. Example array-1, 2, 3, 4, 5, 6 range x w(x) wi SD wi(SD)PM PM PM PM
M = 3.85 max(x) = 1.65
Weighted Intensities:
Transformed Intensities (TI):
Weighted Range (WR): = 3.53
Weighted SD (WSD): 1.75
Expression values (m=3, n=1):
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11. Why Weight? Some PMs may have poor behavior
Give small or 0 weight to “poor” PM
Use information across arrays
Assess quality of PM based on overall behavior
SD of range provides information for detecting differentially expressed genes
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12. Probe Performance
Poorly
performing
probes
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13. Comparison Data Sets Affymetrix Latin Square Spike-In Experiments
Two experiments: on HGU-95Av2 platform and HGU-133A platform
HGU-95 experiments has 14 transcripts spiked-in at concentrations from 0 to 1024 pM (59 arrays)
HGU-133 experiment has 42 transcripts spiked-in in triplicate at concentrations from 0 to 512 (42 arrays)
McGee and Chen (2006) report 22 more spike-ins
“GoldenSpike” Experiment (Choe et al., 2005)
Six arrays (3 experiment, 3 control) on DrosGenome1 Chip
1309 transcripts recognizing known fold differences (from 1.2 to 4)
2551 recognizing transcripts included at the same concentration
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14. Comparison Methods ROC curves, AUC values and CPU time Competitors:
Robust Multichip Average (RMA)
Bolstad, 2004; Irizarry et al., 2003
Gene Chip RMA (GCRMA)
Wu et al., 2004
MAS 5.0, PLIER
Affymetrix 2001, 2004
Model-Based Expression Index (MBEI)
Li & Wong, 2001a,b
Factor Analysis for Robust Array Summarization (FARMS)
Hochreiter et al., 2006
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15. HGU-95 dataset :
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16. HGU-133 dataset (64 spike-ins)
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17. “Preferred” Method
Choe et al. tested dozens of combinations of background correction, normalization, and summarization methods
Preferred = the “best performing” method (according to DEGs obtained by CyberT - Baldi & Long, 2001)
MAS 5.0 background correction  Quantile normalization  median polish summarization  second expression level normalization using LOESS procedure
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18. GoldenSpike Data (FC = 1.2)
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19. Overall Area Under the Curve
HGU-95a
HGU-133a
Choeb
DFW
1.00
0.85
FARMS
0.91
0.95
0.83
GCRMA
0.69
0.57
0.88
RMA
0.60
0.63
0.77
RMA-noBG
0.65
0.82
MAS 5
0.05
0.06
0.39
MBEI
0.26
0.40
0.76
PLIER
0.03
0.20
0.50
a From Affycomp II competition: 16 spike-ins for HGU95, 42 spike-ins for HGU133, bAll spike-ins
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20. Computation Speed (in seconds)
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21. Computational Speed (in seconds)
HGU-95
HGU-133
Choe
DFW
112
150 68
FARMS
132
198
280
GCRMA
214
210 78
RMA
342
388
RMA-noBG
299
353
147
MAS 5
953
1064
130
MBEI
869
833
269
PLIER
321
239 17
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22. Further Comparisons Affycomp II Competition SMU Technical Report
Cope, et al., 2004; Irizarry et al., 2006
For Hgu95 spikein data, uses 16 spike-ins
For Hgu133 spikein data, uses 42 spike-ins
SMU Technical Report
Monnie McGee’s website
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23. References DAB Workshop 2006
Affymetrix, Inc.. (2002) Statistical algorithms description document.
Affymetrix, Inc. (2005) Technical note: guide to probe logarithmic intensity error (PLIER) estimation.
Baldi,P. and Long, A.D. (2001) A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17,
Bolstad, BM. (2004) Low Level Analysis of High-density oligonucleotide array data: Background, normalization and summarization [dissertation]. Department of Statistics, University of California at Berkeley.
Choe, S.E. et al. (2005) Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control datasets. Genome Biol., 6, R16.1-R16.6.
Cope, L.M. et.al. (2004) A benchmark for Affymetrix GeneChip expression measures. Bioinformatics, 20,
Hochreiter, S. et al. (2006) A new summarization method for Affymetrix probe level data. Bioinformatics, 22,
Irizarry, R.A. et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4,
Irizarry, R.A. et al. (2006) Comparison of Affymetrix GeneChip expression measures. Bioinformatics, 22,
Li, C. and Wong, H.W. (2001a) Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Nat. Acad. Sci., 98,
Li, C and Wong, H.W. (2001b) Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol., 2, research
McGee, M. and Chen, Z. (2006) New spiked-in probe sets for the Affymetrix HG-U133A Latin Square experiment. COBRA Preprint Series, Article 5
Wu, Z. et.al. (2004) A model-based background adjustment for oligonucleotide expression arrays. J. Am. Stat. Assoc., 99,
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