1. First analysis steps o quality control and optimization o calibration and error modeling o data transformations Wolfgang Huber Dep. of Molecular Genome Analysis (A. Poustka) DKFZ Heidelberg

2. Acknowledgements Anja von Heydebreck Günther Sawitzki Holger Sültmann, Andreas Buness, Markus Ruschhaupt, Klaus Steiner, Jörg Schneider, Katharina Finis, Stephanie Süß, Anke Schroth, Friederike Wilmer, Judith Boer, Martin Vingron, Annemarie Poustka Sandrine Dudoit, Robert Gentleman, Rafael Irizarry and Yee Hwa Yang: Bioconductor short course, summer 2002 and many others

3. 4 x 4 or 8x4 sectors 17...38 rows and columns per sector ca. 4600…46000 probes/array sector: corresponds to one print-tip a microarray slide Slide: 25x75 mm Spot-to-spot: ca. 150-350  m

4. Terminology sample: RNA (cDNA) hybridized to the array, aka target, mobile substrate. probe: DNA spotted on the array, aka spot, immobile substrate. sector: rectangular matrix of spots printed using the same print-tip (or pin), aka print-tip-group plate: set of 384 (768) spots printed with DNA from the same microtitre plate of clones slide, array channel: data from one color (Cy3 = cyanine 3 = green, Cy5 = cyanine 5 = red). batch: collection of microarrays with the same probe layout.

5. Raw data scanner signal resolution: 5 or 10  m spatial, 16 bit (65536) dynamical per channel ca. 30-50 pixels per probe (60  m spot size) 40 MB per array Image Analysis spot intensities 2 numbers per probe (~100-300 kB) … auxiliaries: background, area, std dev, …

6. 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 foreground intensities; background intensities; quality measures. R and G for each spot on the array.

7. Segmentation adaptive segmentation seeded region growing fixed circle segmentation Spots may vary in size and shape.

8. spot intensity data two-color spotted arrays Probes (genes) n one-color arrays (Affymetrix, nylon) conditions (samples)

9. Which genes are differentially transcribed? same-sametumor-normal log-ratio

10.  ratios and fold changes Fold changes are useful to describe continuous changes in expression 1000 1500 3000 x3 x1.5 ABC 0 200 3000 ? ? ABC But what if the gene is “off” (below detection limit) in one condition?

11.  ratios and fold changes Many interesting genes will be off in some of the conditions of interest 1.If you want expression measure (“net normalized spot intensity”) to be an unbiased estimator of abundance  many values  0  need something more than (log)ratio 2. If you let expression measure be biased  can keep ratios.  how do you chose the bias?

12. Raw data are not mRNA concentrations o tissue contamination o clone identification and mapping o image segmentation o RNA degradation o PCR yield, contamination o signal quantification o amplification efficiency o spotting efficiency o ‘background’ correction o reverse transcription efficiency o DNA-support binding o hybridization efficiency and specificity o other array manufacturing- related issues The problem is less that these steps are ‘not perfect’; it is that they may vary from gene to gene, array to array, experiment to experiment.

13. Sources of variation amount of RNA in the biopsy efficiencies of -RNA extraction -reverse transcription -labeling -photodetection PCR yield DNA quality spotting efficiency, spot size cross-/unspecific hybridization stray signal CalibrationError model Systematic o similar effect on many measurements o corrections can be estimated from data Stochastic o too random to be ex- plicitely accounted for o “noise”

14. a i per-sample offset  ik ~ N(0, b i 2 s 1 2 ) “additive noise” b i per-sample normalization factor b k sequence-wise probe efficiency  ik ~ N(0,s 2 2 ) “multiplicative noise”  modeling ansatz measured intensity = offset + gain  true abundance

15.  The two-component model raw scalelog scale “additive” noise “multiplicative” noise B. Durbin, D. Rocke, JCB 2001

16. Calibration ("normalization") Correct for systematic variations. To do: fit appropriate "correction parameters" a i, b i (and possibly more…) and apply to the data. "Heteroskedasticity" (unequal variances)  weighted regression or variance stabilizing transformation Outliers:  use a robust method

17. the variance-mean dependence data (cDNA slide): relation between mean u=E(Y ik ) and variance v=Var(Y ik ):

18. variance stabilization X u a family of random variables with EX u =u, VarX u =v(u). Define  var f(X u )  independent of u derivation: linear approximation

19.  variance stabilization f(x) x

20. variance stabilizing transformations 1.) constant variance 2.) const. coeff. of variation 4.) microarray 3.) offset

21. the arsinh transformation - - - log u ——— arsinh((u+u o )/c)

22. parameter estimation o maximum likelihood estimator: straightforward – but sensitive to deviations from normality o model holds for genes that are unchanged; differentially transcribed genes act as outliers. o robust variant of ML estimator, à la Least Trimmed Sum of Squares regression. o works as long as <50% of genes are differentially transcribed

23. Least trimmed sum of squares regression minimize - least sum of squares - least trimmed sum of squares

24. evaluation: effects of different data transformations difference red-green rank(average)

25. Coefficient of variation cDNA slide: H. Sueltmann

26. evaluation: a benchmark for Affymetrix genechip expression measures o Data: Spike-in series: from Affymetrix 59 x HGU95A, 16 genes, 14 concentrations, complex background Dilution series: from GeneLogic 60 x HGU95Av2, liver & CNS cRNA in different proportions and amounts  o Benchmark: 15 quality measures regarding -reproducibility -sensitivity -specificity Put together by Rafael Irizarry (Johns Hopkins) http://affycomp.biostat.jhsph.edu

27.  ROC curves

28.  affycomp results (28 Sep 2003) good bad

29. Summary log-ratio 'glog' (generalized log-ratio) - interpretation as "fold change" + interpretation even in cases where genes are off in some conditions + visualization + can use standard statistical methods (hypothesis testing, ANOVA, clustering, classification…) without the worries about low-level variability that are often warranted on the log-scale

30.  Availability o implementation in R o open source package vsn on www.bioconductor.org o Bioconductor is an international collaboration on open source software for bioinformatics and statistical omics

31. Quality control: diagnostic plots and artifacts

32. Scatterplot, colored by PCR-plate Two RZPD Unigene II filters (cDNA nylon membranes) PCR plates

34. PCR plates: boxplots

35. array batches

36. print-tip effects q (log-ratio) F(q)

37. spotting pin quality decline after delivery of 3x10 5 spots after delivery of 5x10 5 spots H. Sueltmann DKFZ/MGA

38. spatial effects R RbR-Rb color scale by rank spotted cDNA arrays, Stanford-type another array: print-tip color scale ~ log(G) color scale ~ rank(G)

39. Batches: array to array differences d ij = mad k (h ik -h jk ) arrays i=1…63; roughly sorted by time

40. Density representation of the scatterplot (76,000 clones, RZPD Unigene-II filters)

41. Oligonucleotide chips

42. Affymetrix files Main software from Affymetrix: MAS - MicroArray Suite. DAT file: Image file, ~10^7 pixels, ~50 MB. CEL file: probe intensities, ~400000 numbers CDF file: Chip Description File. Describes which probes go in which probe sets (genes, gene fragments, ESTs).

43. Image analysis DAT image files  CEL files Each probe cell: 10x10 pixels. Gridding: estimate location of probe cell centers. Signal: –Remove outer 36 pixels  8x8 pixels. –The probe cell signal, PM or MM, is the 75 th percentile of the 8x8 pixel values. Background: Average of the lowest 2% probe cells is taken as the background value and subtracted. Compute also quality values.

44. Data and notation PM ijg, MM ijg = Intensities for perfect match and mismatch probe j for gene g in chip i i = 1,…, none to hundreds of chips j = 1,…, J usually 11 or 16 probe pairs g = 1,…, G 6…30,000 probe sets. Tasks: calibrate (normalize) the measurements from different chips (samples) summarize for each probe set the probe level data, i.e., 16 PM and MM pairs, into a single expression measure. compare between chips (samples) for detecting differential expression.

45. expression measures: MAS 4.0 Affymetrix GeneChip MAS 4.0 software uses AvDiff, a trimmed mean: o sort d j = PM j -MM j o exclude highest and lowest value o J := those pairs within 3 standard deviations of the average

46. Expression measures MAS 5.0 Instead of MM, use "repaired" version CT CT=MM if MM<PM = PM / "typical log-ratio"if MM>=PM "Signal" = Tukey.Biweight (log(PM-CT)) (…  median) Tukey Biweight: B(x) = (1 – (x/c)^2)^2 if |x|<c, 0 otherwise

47. Expression measures: Li & Wong dChip fits a model for each gene where –  i : expression index for gene i –  j : probe sensitivity Maximum likelihood estimate of MBEI is used as expression measure of the gene in chip i. Need at least 10 or 20 chips. Current version works with PMs only.

48. Affymetrix: I PM = I MM + I specific ? log(PM/MM) 0 From: R. Irizarry et al., Biostatistics 2002

49.  Chemistry i wiwi position- and sequence-specific effects w i (s): Naef et al., Phys Rev E 68 (2003)

50. Expression measures RMA: Irizarry et al. (2002) o Estimate one global background value b=mode(MM). No probe-specific background! o Assume: PM = s true + b Estimate s  0 from PM and b as a conditional expectation E[s true |PM, b]. o Use log 2 (s). o Nonparametric nonlinear calibration ('quantile normalization') across a set of chips.

51. AvDiff-like with A a set of “suitable” pairs. Li-Wong-like: additive model Estimate RMA = a i for chip i using robust method median polish (successively remove row and column medians, accumulate terms, until convergence). Works with d>=2 Robust expression measures RMA: Irizarry et al. (2002)

52. Software for pre-processing of Affymetrix data Bioconductor R package affy. Background estimation. Probe-level normalization. Expression measures Two main functions: ReadAffy, expresso. Can use vsn as a normalization method for expresso.

53. References Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. YH Yang, S Dudoit, P Luu, DM Lin, V Peng, J Ngai and TP Speed. Nucl. Acids Res. 30(4):e15, 2002. Variance Stabilization Applied to Microarray Data Calibration and to the Quantification of Differential Expression. W.Huber, A.v.Heydebreck, H.Sültmann, A.Poustka, M.Vingron. Bioinformatics, Vol.18, Supplement 1, S96-S104, 2002. A Variance-Stabilizing Transformation for Gene Expression Microarray Data. : Durbin BP, Hardin JS, Hawkins DM, Rocke DM. Bioinformatics, Vol.18, Suppl. 1, S105-110. Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2002). Accepted for publication in Biostatistics. http://biosun01.biostat.jhsph.edu/~ririzarr/papers/index.html A more complete list of references is in: Elementary analysis of microarray gene expression data. W. Huber, A. von Heydebreck, M. Vingron, manuscript. http://www.dkfz-heidelberg.de/abt0840/whuber/