Normalisation and Analysis of the Affymetrix Data

1. Normalisation and Analysis of the Affymetrix Data David J Craigon

2. What I am not going to talk about • General microarray topics • Biology

3. The introduction

4. Affymetrix workflow Biological sample of some sort Extract mRNA Amplify Label and Fragment Analyse down to one number per gene Find features in scan Scan chip Hybridise to a chip

5. What do we want to find out? • We want to find out how much mRNA of each type was in the original sample

6. Biological sample of some sort Extract mRNA Amplify Label and Fragment Each of these steps need to be proportional Analyse down to one number per gene Find features in scan Scan chip Hybridise to a chip

7. Biological sample of some sort Extract mRNA Amplify Label and Fragment This talk is about this bit Analyse down to one number per gene Find features in scan Scan chip Hybridise to a chip

8. Affymetrix Chips • On an Affymetrix chip each oligo takes up a “square” • The RNA extracted from the plant is first amplified. Then is labelled. This allows the scanner to see it. • The RNA is then hybridised to the array. Matching RNA for that square sticks to the square, and can be seen by the scanner. • By observing the intensity of a square, the amount of RNA bound to that oligo can be calculated

9. Design of the oligos 5’ 3’ • Series of oligos designed for one gene • Each oligo comes in two versions…

10. Match and mismatch • The exact match is a section of the mRNA sequence you wish to probe for • The mismatch is identical except for one base difference from it’s exact match counterpart, and is used to calculate a background. • There are typically 11 “probe pairs” scattered around the chip- called a probe set. • By combining the expression values for a probe set, a value for the expression of mRNA can be found.

11. EXP, DAT, CEL, CHP files • EXP file- experiment file • DAT file- the picture- like a TIFF. • CEL file- a unnormalised number for each probe. • CHP file- one number for each probeset

12. What do you think of it so far? So far… • What we want to find out is the amount of each mRNA in the starting sample. • The mRNA hybridises to a series of probes. • We can get a number for each probe from the CEL file.

13. The rest of this talk We are going to go through four distinct ways of determining “Signal” values from CEL file data • MAS 4 • MAS 5 • MBEI (dChip) • RMA

14. Mismatch probes in detail

15. All about mismatch probes ATGCTGTACAATCGCTTGATACTGG Mismatch probe: ATGCTGTACAATAGCTTGATACTGG Perfect match probe: ATGCTGTACAATAGCTTGATACTGG Target sequence:

16. Why do we have mismatch probes? • Mismatch probes (MM) are trying to detect background. • The mismatch probes are supposed to detect things that are close but not an exact match. • It is assumed that these things also bind to the perfect match (PM), erroneously.

17. Yes folks, it’s Expression Method No 1! • The original method that was used by MAS 4

18. MAS 4 Algorithm For a probe set: • A is the set of probes you haven’t thrown away due to being outliers • j=0 to the number of probesets • In English, the formula is very simple- throw away the outliers, then simply average the differences between PM and MM of the probes you’ve got left.

19. Problems with the MAS4 algorithm • Better fit with log(PM) preferred

20. Expression Method No 2! • MAS 5 method. • Still used by GCOS- the current Affymetrix supplied method.

21. Normalisation Procedure • Before any work is done with the “CEL” data, the CEL file is normalised. • Corrects for intra-chip differences

22. Normalisation Procedure • Divides the chip into K zones (by default, 16 zones) • Select the lowest 2% of probes (of any description) • Assume these are “switched off”

23. Normalisation Procedure • Calculate Mean, SD of these “switched off” probes for each section. • Used as background. • Each point’s local background weighted difference between each zone • Subtract background from each probe.

24. MAS 5 Algorithm For a probe set: • Tukey’s Biweight is an average that minimises the effect of outliers. • IM is the “ideal mismatch”. This is the same as the MM intensity, except in the case where the MM is greater than the PM, in which case a new MM values is calculated based on other probes nearby

26. MAS4 to MAS5 comparison

27. Signal Normalisation • To try to eliminate chip-to-chip variability. • Sort the signal values and remove the top and bottom 2% • Calculate a scaling factor to adjust this middle 96%’s mean to 100 (configurable, and variable) • Multiply all signal values by the scaling factor • Affymetrix state that scaling factors should be similar for arrays to be comparable

28. Expression Method No 3! • The MBEI method of Li and Wong. • Found in dChip, so often known as the dChip method.

29. Observation

30. Observation • The probes are vastly variable in effectiveness • Li and Wong point out that the difference between probes is much greater than the difference between arrays! • They contend that any proper model should take this into account.

31. MBEI model

32. MBEI model Baseline response due to noise Rate of increase of MM probe as signal increases (really? See later) Error term Rate of increase of PM probe as signal increases (separate for each probe) Expression value (the thing we are interested in)

33. Model is fitted over all chips • Processes an entire experiment at once • Model is fitted using residual sum of squares • In their paper on the subject they talk a lot about how you can use this model to detect outliers, scratches on the array, etc. I’m not going to talk about that.

34. RMA paper observations

35. A spiked in experiment from the RMA paper • It would be useful if we had an experiment where we “knew the answer” • Run a series of experiments with a fixed background, but spike in some artificial RNA for a series of probes, at different concentrations.

36. Mismatch probes • Mismatch probes are supposed to calculate what similar things hybridise to probes, to detect background for PM probes. • The background should be at a relatively low level most of the time…

37. Yikes! • Actually MM>PM between 33% and 40% of the time!

38. Mismatch probes • Mismatch probes are supposed to calculate what similar things hybridise to probes, to detect background for PM probes. • The amount of this stuff shouldn’t depend on how much “interesting” RNA there is about…

39. Man the lifeboats!

40. Some observations from the RMA paper … perfect match probes appear to be additive (in the log scale)

41. The amount of signal does affect mismatch probes. • Clearly some of the useful mRNA is hybidising to the MM probes. • This kind of shock has led to some people abandoning the use of MM probes altogether!

42. What’s going on?

43. Perfect match probes … in RMA, in the log scale, they assume that probe effects are effectively additive

44. How RMA (roughly) works

45. RMA process • Normalise array • Fit model

46. Normalisation procedure involves adusting distributions

47. RMA process • Normalise array • Fit model

48. Fit model Correct background using estimate from all mismatch probes for each array. Fit model: Additive probe affinitive effect for this probe over all slides Background corrected PM value Log scale expression value

49. In summary then… • There are various ways you can get from a CEL file to expression estimates. • These models are derived by considering the behaviour of PM and MM probes • Both dChip and RMA show better results than the standard Affy algorithm • MM probes in particular behave contrary to how you would expect.

50. Enough theory- how do you actually do these things? • The MAS5 algorithm can be performed using (erm) MAS5! • dChip is a piece of software that will be making an appearance later this afternoon, and can do the MBEI algorithm • The RMA authors have a piece of software called RMAExpress, which does RMA for Windows. • All of these algorithms can be done using the Bioconductor package in R.