Gene Level Expression Profiling Using Affymetrix Exon Arrays

1. Gene Level Expression Profiling Using Affymetrix Exon Arrays Alan Williams, Ph.D.Director Chip DesignAffymetrix, Inc.

2. Exon Array Design StrategyGeneChip� Human Exon 1.0 ST All content is projected onto the genome Content has hard edges and soft edges: Hard edges partition regions into multiple probe selection regions Soft edges infer a probe selection region, but can be extended into a larger region by other content Hard Edges Internal splice site boundaries PolyA sites CDS Start and Stop Positions Soft Edges Transcript start and stop positions (except when there is evidence of a PolyA site) Internal splice site boundaries for aligned cDNAs when there are unaligned cDNA bases All splice site boundaries from syntenic cDNA content Introducing some new concepts: Probe Selection Region (PSR) Exon cluster Transcript cluster (gene locus)

3. Probe Coverage Exon vs 3� Array Gene Coverage

4. Content Sources GeneChip� Human Exon 1.0 ST Core Gene Annotations RefSeq alignments GenBank annotated full length alignments Extended Gene Annotations cDNA alignments Ensembl annotations (Hubbard, T. et al.) Mapped syntenic mRNA from rat and mouse microRNA annotations MitoMAP annotations Vegagene (The HAVANA group, Hillier et al., Heilig et al.) VegaPseudogene (The HAVANA group, Hillier et al., Heilig et al.) Full Gene Annotations Geneid (Grup de Recerca en Inform�tica Biom�dica) Genscan (Burge, C. et al.) GenscanSubopt (Burge, C. et al.) Exoniphy (Siepel et al.) RNAgene (Sean Eddy Lab) SgpGene (Grup de Recerca en Inform�tica Biom�dica) Twinscan (Korf, I. et al.)

5. Probes per RefSeq Transcript

6. Gene Level Summaries With exon arrays we can combine exon-level probesets to obtain better gene-level estimates. More probes for greater sensitivity Gene level signal estimates based on expression throughout the locus rather than a single point Simplified bioinformatics More flexibility in restructuring probe groupings based on expert knowledge There is a variety of well established tools (including R/BioConductor) and methods for secondary analysis of gene level array data Challenge Non-constitutive exons Discovery/Speculative content

7. Gene Level Analysis on Exon Arrays Sketch Normalization (Quantile-like) PM-GCBG IterPLIER using Extended Meta Probeset File groupings Users may want to do post summarization operations: Normalization Log transform Variance stabilization by adding positive bias (ie PLIER+16)

8. Different Meta Probeset Lists

9. IterPLIER Start by generating PLIER signal estimate using all the probes Pick 22 probes which are best correlated to the PLIER signal Run PLIER on just the 22 probes Pick 11 probes which are best correlated to the PLIER signal Generate a final PLIER estimate with the 11 probes Corollary: If the meta probeset has 11 or fewer probes, then only 1 run of PLIER is performed and the result is equal to a regular PLIER result If the meta probeset has more than 11 but 22 or fewer probes, then PLIER is run twice: once on the full set of probes and once on the best 11

10. Correlation of Different Gene Level Estimates

11. Adding Low-signal Decoys OWNER: Chuck 4-11 probesets are the 25th and 75th percentile of all the 1674 loci with at least 3 constitutive probe sets. Experimental design: Use cDNA to identify constitutive probesets included in all transcripts and at least 10 ESTs (or 5mRNAs) at that probeset. Generate gene-level estimates from constitutive probesets and use them as gold standard. Add low-signal decoy sets (Genscan Suboptimals) and observe effect on correlation with original estimates. Add high-signal decoy sets (mRNA based) and observe effect of correlation with original estimates. OWNER: Chuck 4-11 probesets are the 25th and 75th percentile of all the 1674 loci with at least 3 constitutive probe sets. Experimental design: Use cDNA to identify constitutive probesets included in all transcripts and at least 10 ESTs (or 5mRNAs) at that probeset. Generate gene-level estimates from constitutive probesets and use them as gold standard. Add low-signal decoy sets (Genscan Suboptimals) and observe effect on correlation with original estimates. Add high-signal decoy sets (mRNA based) and observe effect of correlation with original estimates.

12. Gene Level Performance HuEx 1.0 ST vs HG-U133 Plus 2.0

13. Platform Concordance% Probe Set Pairs vs. Correlation Coefficient (1-way ANOVA p <= 10-8)

14. High Correlation: GLYAT: r=0.9902

15. Moderate Correlation: TSN: r=0.6575

16. Poor Correlation: SREBF1: r=0.0482

17. Platform Gene Level Sensitivity

18. One Array, Two functions Gene Level Expression and Transcript Diversity

19. TPM2

22. �Splicing Index� defined

23. Splicing Index Examples

24. Alternative Splicing Detection PAttern basedCorrelation (PAC) Test whether exonscorrelate with eachother ANOVA based(MiDAS) Test a log-linearmodel For more information see the Alternative Transcript Analysis Methods for Exon Arrays whitepaper: http://www.affymetrix.com/support/technical/whitepapers/exon_alt_transcript_analysis_whitepaper.pdf OWNER: Earl Colon Cancer: Median normalization over entire data set Tissue Data Set: Quantile w/in rep, Median over set Method Assessment Manufacture �unspliced� gene set Choose 5,800 well sequenced genes Bioinformatic pruning any alternative exons Remaining exons form a �gene� Simulate splice data Move first exon of each gene to a different gene Calculate ROC curves of �true positive� on simulated splice set versus �false positive� on unspliced gene set OWNER: Earl Colon Cancer: Median normalization over entire data set Tissue Data Set: Quantile w/in rep, Median over set Method Assessment Manufacture �unspliced� gene set Choose 5,800 well sequenced genes Bioinformatic pruning any alternative exons Remaining exons form a �gene� Simulate splice data Move first exon of each gene to a different gene Calculate ROC curves of �true positive� on simulated splice set versus �false positive� on unspliced gene set

25. ROC Curves PAC method not suitable for a two group data set No filter on input data Synthetic Data Tissues � mix exons across genes Cancer � mix in low expression exons OWNER: EarlOWNER: Earl

26. Alternative Splicing DetectionActive Area of Research Exon Array Workshop 45 attendees 11 presentations New alternative splicing algorithms New confidence in using Exon Arrays for Gene-Level expression profiling New directions for filtering data for more robust results http://www.affymetrix.com/corporate/events/2006_exon_tiling_workshop.affx We have just reviewed our efforts on the commercial vendor front, there is a considerable amount of research that has been on-going in the research community, that is extremely active in designing new algorithms for microarray data analysis in the past. In order to proactively collaborate with the research community, also in response to the request of early customers, who are interested in hearing directly from each other of their actual experiences, we sponsored the first of Exon Array Data Analysis Workshop. This is the format that we intend to continue to support and would appreciate feedback from you as a user of the usefulness of such events and what you need from Affymetrix to help you get your research started.We have just reviewed our efforts on the commercial vendor front, there is a considerable amount of research that has been on-going in the research community, that is extremely active in designing new algorithms for microarray data analysis in the past. In order to proactively collaborate with the research community, also in response to the request of early customers, who are interested in hearing directly from each other of their actual experiences, we sponsored the first of Exon Array Data Analysis Workshop. This is the format that we intend to continue to support and would appreciate feedback from you as a user of the usefulness of such events and what you need from Affymetrix to help you get your research started.

27. Resources Human, Mouse, & Rat array content and annotation information Array Support Page on Affymetrix.com Various Analysis Whitepapers Array Support Page on Affymetrix.com Sample Data Sets Sample Data section under Support Colon cancer data set with 10 paired samples Tissue data set 11 tissues in triplicate 4 different mixture levels for 3 tissues Includes HG-U133 Plus 2.0 and Human Exon 1.0 ST Analysis Software Affymetrix Power Tools (APT) ExACT