1 / 29

Intro & materials

lab 2. Intro & materials. Monday MA experimental basic MA data analysis Introduction to lab 1 lab 1. Tuesday Introduction to lab 2 lab 2. Overview. Bio-Informatic motivation. Intro lab 2. Biological question Differentially expressed genes Classification etc.

trevet
Download Presentation

Intro & materials

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related