1. Pre-processing in DNA microarray experiments Sandrine Dudoit PH 296, Section 33 13/09/2001

2. Joint work with Yee Hwa (Jean) Yang Terry Speed. R packages –Spot, for image analysis; –SMA, “Statistics for Microarray Analysis”, for normalization and much more. http://www.stat.berkeley.edu/~sandrine

3. Biological question Differentially expressed genes Sample class prediction etc. Testing Biological verification and interpretation Microarray experiment Estimation Experimental design Image analysis Normalization Clustering Discrimination R, G 16-bit TIFF files (Rfg, Rbg), (Gfg, Gbg)

4. Image analysis

5. The raw data from a microarray experiment consist of pairs of image files, 16-bit TIFFs, one for each of the dyes. Image analysis is required to extract measures of the red and green fluorescence intensities for each spot on the array.

7. Steps in image analysis 1. Addressing. Estimate location of spot centers. 2. Segmentation. Classify pixels as foreground (signal) or background. 3. Information extraction. For each spot on the array and each dye signal intensities; background intensities; quality measures.

8. Spot Software package. Spot, built on the freely available software package R. Batch automatic addressing. Segmentation. Seeded region growing (Adams & Bischof 1994): adaptive segmentation method, no restriction on the size or shape of the spots. Information extraction –Foreground. Mean of pixel intensities within a spot. –Background. Morphological opening: non-linear filter which generates an image of the estimated background intensity for the entire slide. Spot quality measures.

9. Addressing 4 by 4 grids Automatic addressing within the same batch of images. Other problems: -- Mis-registration -- Rotation -- Skew in the array Estimate translation of grids. Estimate row and column positions. Foreground and background grids.

10. Segmentation Adaptive segmentation, SRGFixed circle segmentation Spots usually vary in size and shape.

11. Segmentation — Small spot — Not circular Results from SRG

12. Seeded region growing Adaptive segmentation method. Requires the input of seeds, either individual pixels or groups of pixels, which control the formation of the regions into which the image will be segmented. Here, based on fitted foreground and background grids from the addressing step. The decision to add a pixel to a region is based on the absolute gray-level difference of that pixel’s intensity and the average of the pixel values in the neighboring region. Done on combined red and green images.

13. Information extraction Spot foreground intensities Rfg Gfg –mean or median of foreground pixel intensities. Spot background intensities Rbg Gbg –local; –morphological opening; –constant (global); –none. Quality information –spot quality; –slide quality. Signal Background

14. Local background ---- GenePix ---- QuantArray ---- ScanAnalyze

15. Morphological opening Image is probed with a structuring element, here, a square with side length about twice the spot to spot distance. Morphological opening: erosion followed by dilation. Erosion (Dilation): the eroded (dilated) value at a pixel x is the minimum (maximum) value of the image in the window defined by the structuring element when its origin is at x. Done separately for the red and green images. Produces image of estimated background for the entire slide.

16. Background matters Morphological openingLocal background

17. Quality measures Spot quality –Brightness: foreground/background ratio; –Uniformity: variation in pixel intensities and ratios of intensities; –Morphology: area, perimeter, circularity. Slide quality –Percentage of spots with no signal; –Range of intensities; –Distribution of spot signal area, etc. How to use quality measures in subsequent analyses?

18. Normalization

19. Identify and remove systematic sources of variation in the measured fluorescence intensities, other than differential expression, for example –different labeling efficiencies of the dyes; –different amounts of Cy3- and Cy5- labeled mRNA; –different scanning parameters; –sector/print-tip, spatial, or plate effects, etc. Necessary for within and between slides comparisons of expression levels.

20. Normalization The need for normalization can be seen most clearly in self-self hybridizations where the same mRNA sample is labeled with the Cy3 and Cy5 dyes. The imbalance in the red and green intensities is usually not constant across the spots within and between arrays, and can vary according to overall spot intensity, location, plate origin, etc. These factors should be considered in the normalization.

21. Single-slide data display Usually: R vs. G log 2 R vs. log 2 G. Preferred M = log 2 R - log 2 G vs. A = (log 2 R + log 2 G)/2. An MA-plot amounts to a 45 o counterclockwise rotation of a log 2 R vs. log 2 G plot followed by scaling.

22. Self-self hybridization log 2 R vs. log 2 GM vs. A

23. Normalization Within-slides –Location normalization. –Scale normalization. –Which genes to use? Paired-slides (dye-swap) –Self-normalization. Between-slides.

24. Apo AI experiment Goal. Identify genes with altered expression in the livers of apo AI knock-out mice compared to inbred C57Bl/6 control mice. 8 treatment (trt) mice and 8 control (ctl) mice. 16 hybridizations: target mRNA from each of the 16 mice is labeled with Cy5, pooled mRNA from control mice is labeled with Cy3. Probes: ~6,000 cDNAs, including 200 related to lipid metabolism.

25. Target mRNA samples: R = apo A1 KO mouse liver G = pooled control C57Bl/6 mouse liver

26. MA plot M = log 2 R - log 2 G, A = (log 2 R + log 2 G)/2 log 2 R vs. log 2 G M vs. A

27. Location normalization log 2 R/G log 2 R/G –l(intensity, location, plate, …) Global normalization. Normalization function l is constant across the spots and equal to the mean or median of the log-ratios M. Adaptive normalization. Normalization function l depends on a number of predictor variables, such as spot intensity, location, plate origin. The normalization function can be obtained by robust locally weighted regression of the log-ratios M on the predictor variables.

28. Location normalization Variable selection. Many different smoothers are available. E.g. lowess and loess smoothers. For local regression, choices must be made regarding: the weight function, the parametric family that is fitted locally, the bandwidth, and the fitting criterion. As with any other normalization approach, these choices represent a modeling of the data.

29. Location normalization Intensity dependent normalization. Regression of M on A. Intensity and print-tip dependent normalization. Same as above, for each print-tip separately. Spatial normalization. Regression of M on 2D-coordinates. Other variables: time of printing, plate, etc.

30. Global median normalization First panel: density estimates for log 2 G and log 2 R. Second panel: MA plot after median normalization.

31. Intensity dependent normalization

32. Intensity + print-tip dependent normalization Before

33. Intensity + print-tip dependent normalization After

34. Effect of location normalization Before normalization After intensity + print-tip normalization

35. Scale normalization The log-ratios M from different print-tips or plates may exhibit different spreads and some scale adjustment may be necessary. log 2 R/G (log 2 R/G –l)/s Can use a robust estimate of scale like the median absolute deviation (MAD) MAD = median | M – median(M) |.

36. Effect of location + scale normalization

37. Comparing different normalization methods

38. Which genes to use? All genes on the array. Constantly expressed genes (housekeeping). Controls –Spiked controls (e.g. plant genes); –Genomic DNA titration series. Rank invariant set.

39. Follow-up experiment 50 distinct clones with largest absolute t-statistics from apo AI experiment + 72 other clones. Spot each clone 8 times. Two hybridizations with dye-swap: Slide 1: trt  red, ctl  green. Slide 2: trt  green, ctl  red.

40. Follow-up experiment

41. Self-normalization Slide 1, M = log 2 (R/G) - l Slide 2, M’ = log 2 (R’/G’) - l’ Combine by subtracting the normalized log-ratios: [ (log 2 (R/G) - l) - (log 2 (R’/G’) - l’) ] / 2  [ log 2 (R/G) + log 2 (G’/R’) ] / 2  [ log 2 (RG’/GR’) ] / 2 provided l = l’. Assumption: the normalization functions are the same for the two slides.

42. Checking the assumption MA plot for slides 1 and 2

43. Result of self-normalization (M - M’)/2 vs. (A + A’)/2

44. Summary Case 1. Only a few genes are expected to change. Within-slides –Location: intensity + print-tip dependent lowess normalization. –Scale: for each print-tip-group, scale by MAD. Between-slides –An extension of within-slide scale normalization. Case 2. Many genes expected to change. –Paired-slides: Self-normalization. –Use of controls or known information.

45. cDNA gene expression data Genes mRNA samples Gene expression level of gene i in mRNA sample j = log 2 ( Red intensity / Green intensity) sample1sample2sample3sample4sample5 … 1 0.46 0.30 0.80 1.51 0.90... 2-0.10 0.49 0.24 0.06 0.46... 3 0.15 0.74 0.04 0.10 0.20... 4-0.45-1.03-0.79-0.56-0.32... 5-0.06 1.06 1.35 1.09-1.09... Data on p genes for n samples

46. Reading for next Monday Robust local regression W. S. Cleveland. (1979). Robust localy weighted regression and smoothing scatterplots. Journal of the American Statistical Association. 74: 829-836. Experimental design M. K. Kerr & G. A. Churchill. (2001). Experimental Design for Gene Expression Microarrays. Biostatistics. 2: 183-201.