1. 1 Intro & materials

2. 2 Overview Monday –MA experimental basic –MA data analysis –Introduction to lab 1 –lab 1 Tuesday –Introduction to lab 2 –lab 2 Bio-Informatic motivation

3. 3 Intro lab 2 Biological question Differentially expressed genes Classification etc. Testing Biological verification and interpretation Microarray experiment Description Experimental design Image analysis Normalization Clustering Discrimination lab 2

4. 4 Normalization to correct for systematic (non-random) effects (”bias”) issues: –dye bias –hybridization-dye interaction –positional bias –spotting tip bias –between-array-bias Intro lab 2

5. 5 graph - representations 1.Intensity(R)-Intensity(G)-Plot 2.Ratio-A-Plot 3.M-A-Plot 4.M corr -A-Plot 5.Tusher - Plot to decide if a normalization is necessary ! Intro lab 2

6. 6 graph - representations 1.Intensity(R)-Intensity(G)-Plot 2.Ratio-A-Plot 3.M-A-Plot 4.M corr -A-Plot 5.Tusher - Plot Intro lab 2 Visualization

7. 7 Intensity(R)-Intensity(G)-Plot (1) Intro lab 2

8. 8 Visualization

9. 9 graph - representations 1.Intensity(R)-Intensity(G)-Plot 2.Ratio-A-Plot 3.M-A-Plot 4.M corr -A-Plot 5.Tusher - Plot Intro lab 2 Visualization

10. 10 A - a measure for hybridization : A = mean(log 2 (R),log 2 (G)) Visualization

11. 11 Ratio-A-Plot (2) Intro lab 2 Visualization log 2

12. 12 graph - representations 1.Intensity(R)-Intensity(G)-Plot 2.Ratio-A-Plot 3.M*-A-Plot 4.M corr -A-Plot 5.Tusher - Plot Intro lab 2 Visualization

13. 13 M* = log 2 (R/G) mean(M*) M*-A-Plot (3) normalization for dye bias: M = M* - mean(M*) Normalization -- dye bias Intro lab 2 *

14. 14 M-A-Plot (3) Intro lab 2 mean(M) normalization for dye bias: M = M* - mean(M*) M = log 2 (R/G)-mean(M*) Normalization -- dye bias

15. 15 M-A-Plot Intro lab 2 Visualization

16. 16 Normalization -- hybridization-bias M-A-Plot Intro lab 2

17. 17 Intro lab 2 M-A-Plot Normalization -- hybridization-bias

18. 18 Differential Expression 1 here: finding the differentially expressed genes Reporting the 4 most upregulated, and the 5 most down-regulated genes (by choosing suitable cut-offs) Intro lab 2 differential expression

19. 19 graph - representations 1.Intensity(R)-Intensity(G)-Plot 2.Ratio-A-Plot 3.M-A-Plot 4.M corr -A-Plot 5.Tusher - Plot Intro lab 2 differential expression

20. 20 Weighting the data with the standard-error (according to Tusher et al, 2001 (PNAS)) M M/(a+s), s : Standard-Error, a : const. differential Expression 2 concept behind the Tusher - plot :

21. 21 differential Expression 2 Tusher - Plot S = M corr / (a+StdErr(M corr )), a=0.442 Intro lab 2

22. 22 Overview Monday –MA experimental basic –MA data analysis –Introduction to lab 1 –lab 1 Tuesday –Introduction to lab 2 –lab 2 preparations Steps 1 - 5 Bio-Informatic motivation

23. 23 preparations Create a working directory on your local PC (e.g. C:\temp\MA_LAB) copy the directory H:\temp\MA_lab__copy_this to the working directory on your PC Open ma_raw_data_lab2.xls with Excel We want you to perform the dye-bias and the hybridisation-bias normalizations using the five different plots mentioned before (sheet 5), and to find the 4 most upregulated, and the 5 most downregulated genes (the next sheets give a detailed guide)! lab 2

24. 24 lab2 - Step 1 (5) Intensity(R)-Intensity(G)-Plot Calculate the mean of the three measurements in Ch1(green) and Ch2(red) for all genes (column H: mean(green)=G, column I: mean(red)=R) Mark both all values for G and R and insert a diagram (as separate sheet) for the Intensity(R)-Intensity(G)-Plot Change the axis' max values so that they are both 40000 Draw a red line as y=x (from (0,0) to (40000,40000)). Observe that in this diagram almost every gene looks as if upregulated ! This is the dye bias! lab 2

25. 25 lab2 - Step 2 (5) Ratio-A-Plot In column J calculate: A=mean(log 2 (G),log 2 (R)) =MEDEL(LOG(H2;2);LOG(I2;2)) in column K calculate: Ratio = R/G = I2/H2 and apply these calculations for all genes. Insert a diagram for the Ratio - A - Plot rescale the axis: x min =10, y min =0.5, y max =2 (0.5=0,5 in Excel!) Do you see a maximum curve as tendency in all data (having a maximum round about A=12.5)? This is the hybridization bias! lab 2

26. 26 lab2 - Step 3 (5) M-A-Plot Copy the values (and only the values, not the formulae) for A (column J) to column L In column M calculate M*=log 2 (R/G) Insert the M*-A-Plot as a new diagram set xmin=10 Calculate mean(M*) in the cell below all data in column M Dye-bias normalization: calculate M=M*-mean(M*) in column N Insert the M-A-Plot as a new diagram lab 2

27. 27 lab2 - Step 4 (5) M corr - A - Plot Insert a quadratic trendline in the M-A-Plot (Typ: Polynom, Ordning 2; Alternativ: Visa ekvation i diagrammet), note the quadratic function (it should look similar to this one:) y = -0.0445x 2 + 1.1116x - 6,9037 (x~A in this case!) in column Q calculate M corr = M - y(A) Insert a new diagram for the M corr -A-Plot Find the 4 most upregulated and the 5 most down-regulated genes (gene_IDs) (use the M corr - A - Plot to guess the suitable cutt-off values (theta1,2) and then use OM(ELLER((M corr >theta1);(M corr <theta2));gene_ID;0) note the gene_IDs) lab 2

28. 28 lab2 - Step 5 (5) Tusher - Plot in columns R, S and T calculate M 1, M 2, M 3 from the three repeated intensity measurements in column U calculate the standard error of M 1, M 2, M 3 (STDAV(R2:T2)) in column V calculate the S statistics: S = M corr / (a+StdErr(M corr ); using the 0.9-percentile of all standard errors as a = 0,442. insert the Tusher-Plot as a new diagram (x: StdErr(M corr ), y: S) Use the plot to guess reasonable cut-off values (theta1,2) for both down- and upregulated genes Find the corresponding gene_IDs for the 4 most upregulated and the 5 most down-regulated genes (use e.g. =OM(ELLER((V2>theta1);(V2<theta2)); gene_ID;0) as column W). Compare with those from Step 4 (extreme genes in the M corr -A-Plot)!! lab 2

29. 29 pass your results to Dirk.Repsilber@ebc.uu.se