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

Exon Array Design Strategy GeneChip® 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)

Probe Coverage Exon vs 3’ Array Gene Coverage RefSeq HG-U Plus Human Genome 1.0 ST

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 Informàtica Biomèdica) – Genscan (Burge, C. et al.) – GenscanSubopt (Burge, C. et al.) – Exoniphy (Siepel et al.) – RNAgene (Sean Eddy Lab) – SgpGene (Grup de Recerca en Informàtica Biomèdica) – Twinscan (Korf, I. et al.)

Probes per RefSeq Transcript >= 10 Probes % >= 20 Probes % >= 30 Probes % >= 40 Probes % >= 50 Probes % HG-U133 Plus 2.0

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

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)

Different Meta Probeset Lists Core-Constitutive

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

Correlation of Different Gene Level Estimates

Adding Low-signal Decoys Correlation with original estimates as Genscan Subopt probesets are added. (996 loci with 4-11 probesets) Regular PLIER Iterative PLIER Correlation with original estimates as mRNA probesets are added. (996 loci with 4-11 probesets)

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

Platform Concordance % Probe Set Pairs vs. Correlation Coefficient (1-way ANOVA p <= ) ~60% of matched probe sets have correlation ≥ 0.8

High Correlation: GLYAT: r= Log2(sig+16)

Moderate Correlation: TSN: r=0.6575

Poor Correlation: SREBF1: r=0.0482

Platform Gene Level Sensitivity # Exons % Significant Probesets HG-U133 Plus 2.0 (21% overall) Human Exon 1.0 ST (23% overall)

One Array, Two functions Gene Level Expression and Transcript Diversity

TPM2 Heart Muscle

Data Courtesy of Millennium

“Splicing Index” defined

Splicing Index Examples

Alternative Splicing Detection  PAttern based Correlation (PAC) – Test whether exons correlate with each other  ANOVA based (MiDAS) – Test a log-linear model  For more information see the Alternative Transcript Analysis Methods for Exon Arrays whitepaper: – aper.pdf e i,j,k = exon signal for ith probeset, k tissue, j gene g i,k = gene signal for k tissue and j gene a i,k = log coupling for exon and gene signals

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

Alternative Splicing Detection Active 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  nts/2006_exon_tiling_workshop.affx nts/2006_exon_tiling_workshop.affx

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