Intro & materials

1. lab 2 Intro & materials

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

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

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

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

6. Intro lab 2 Visualization graph - representations • Intensity(R)-Intensity(G)-Plot • Ratio-A-Plot • M-A-Plot • Mcorr-A-Plot • Tusher - Plot

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

8. Visualization

9. Intro lab 2 Visualization graph - representations • Intensity(R)-Intensity(G)-Plot • Ratio-A-Plot • M-A-Plot • Mcorr-A-Plot • Tusher - Plot

10. Visualization A - a measure for hybridization : A = mean(log2(R),log2(G))

11. Intro lab 2 Visualization Ratio-A-Plot (2) log2

12. Intro lab 2 Visualization graph - representations • Intensity(R)-Intensity(G)-Plot • Ratio-A-Plot • M*-A-Plot • Mcorr-A-Plot • Tusher - Plot

13. Intro lab 2 Normalization -- dye bias M*-A-Plot (3) M* = log2(R/G) * mean(M*) normalization for dye bias: M = M* - mean(M*)

14. Intro lab 2 Normalization -- dye bias M-A-Plot (3) M = log2(R/G)-mean(M*) mean(M) normalization for dye bias: M = M* - mean(M*)

15. Intro lab 2 Visualization M-A-Plot

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

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

18. Intro lab 2 differential expression 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)

19. Intro lab 2 differential expression graph - representations • Intensity(R)-Intensity(G)-Plot • Ratio-A-Plot • M-A-Plot • Mcorr-A-Plot • Tusher - Plot

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

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

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

23. lab 2 preparations • Create a working directory on your local PC(e.g. C:\temp\MA_LAB) • copy the directory H:\temp\MA_lab__copy_thisto 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)!

24. lab 2 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!

25. lab 2 lab2 - Step 2 (5)Ratio-A-Plot • In column J calculate: A=mean(log2(G),log2(R)) =MEDEL(LOG(H2;2);LOG(I2;2))in column K calculate:Ratio = R/G = I2/H2and apply these calculations for all genes. • Insert a diagram for the Ratio - A - Plot • rescale the axis: xmin=10, ymin=0.5, ymax=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!

26. lab 2 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*=log2(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

27. lab 2 lab2 - Step 4 (5)Mcorr - 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 = -0.0445x2 + 1.1116x - 6,9037 (x~A in this case!) • in column Q calculate Mcorr = M - y(A) • Insert a new diagram for the Mcorr-A-Plot • Find the 4 most upregulated and the 5 most down-regulated genes (gene_IDs)(use the Mcorr - A - Plot to guess the suitable cutt-off values (theta1,2) and then use OM(ELLER((Mcorr>theta1);(Mcorr<theta2));gene_ID;0) note the gene_IDs)

28. lab 2 lab2 - Step 5 (5)Tusher - Plot • in columns R, S and T calculate M1, M2, M3 from the three repeated intensity measurements • in column U calculate the standard error of M1, M2, M3(STDAV(R2:T2)) • in column V calculate the S statistics:S = Mcorr / (a+StdErr(Mcorr); using the 0.9-percentile of all standard errors as a = 0,442. • insert the Tusher-Plot as a new diagram(x: StdErr(Mcorr), 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 Mcorr-A-Plot)!!

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