1. Affymetrix GeneChipsandAnalysis Methods Neil Lawrence

2. Schedule and some of this

3. Photolithography • Photolithography (Affymetrix) • Based on the same technique used to make the microprocessors. • Oligonucleotides are generated in situ on a silicon surface. • Oligonucleotides up to 30bp in length. • Array density of 106 probes per cm-2.

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5. Affymetrix • Only one biological sample per chip. • Oligonucleotides represent a portion of a gene’s sequence. • Twenty sub-sequences present for each gene.

6. Perfect vs Mismatch • For each oligonucleotide there is • A perfect match • A mismatch • The perfect match is a sub-sequence of the true sequence. • The mismatch is a sub-sequence with a ‘central’ base-pair replaced.

7. Affymetrix Analysis • Mismatch is designed to measure ‘background’. • Signal from each sub-sequence is IPerfect match – IMismatch • Twenty of these sub-sequences are present. • Average of all these signals is taken.

8. Problems • Sometimes Imismatch > Iperfect match • Solution: set it to 20??!!! • Other issues • Present/Absent call • Based on the number of Signals > 0. • Proprietary Technology • You don’t know what the subsequences are. • Apparently this is changing!

9. Scaling Factors – Maximum likelihood estimation • The data produced is still affected by undesirable variations that we need to remove. • We can assume that the variations are primarily multiplicative: (No intensity dependent or print-tip effect) Obs.-exp.Level = true-exp.Level * error *random-noise (chip variations) (biological noise)

10. Model Assumption • Organise the twelve values from three exogenous control species in a matrix: X=[NControls * NChips] • Error model: Here mi is associated with each control and rj is associated with each chip or experiment. Taking logs we have:

11. Scaling Factors • Calculating scaling factors using maximum likelihood estimation of the model parameters Likelihood: • Estimates are calculated solving Scaling factors are thus :

12. You Should Know • The Central Dogma (Gene Expression). • cDNA chip overview. • Noise in cDNA chips. • Affymetrix GeneChip overview.

13. Analysis of Microarray Data • Vanilla-flavour analysis: • Obtain temporal profiles (e.g. from last week’s mouse experiment). • ‘Cluster’ profiles • Assume genes in the same cluster are functionally related.

14. Temporal Profiles • Lack of statistical independence. • Take temporal differences to recover. • Justified by assuming and underlying Markov process.

15. Analysis of Microarray Data Original Temporal Profile 120 80 Gene expression level 40 0 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Take Temporal Differences 80 40 Change in exp. level 0 2-1 3-2 6-5 4-3 5-4 -40 -80

16. Consider Clustering via MSE These two similar profiles won’t cluster 120 80 Gene expression level 40 0 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 140 100 Gene expression level 60 20 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6

17. The Temporal Differences Will 80 40 Change in exp. level 0 2-1 3-2 6-5 4-3 5-4 -40 -80 80 40 Change in exp. level 0 2-1 3-2 6-5 4-3 5-4 -40 -80

18. Many Other Different Techniques • Hierachical Clustering • Self-Organising Maps • ML-Group • Generative Topographic Mappings (GTM)

19. GTM • Data lies in high dimensional space (>2). • Model it with a lower embedded dimensionality (2). • MATLAB Demo of embedded dimensions.

20. GTM on Gene Data • MATLAB Demo.

21. Conclusions • Take Temporal differences of Profiles. • Attempt to Cluster. • Test Hypothesis that clustered Genes are functionally related. • Good luck in the Exam!