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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
Intro lab 2Biological question
Differentially expressed genesClassification etc.
Testing
Biological verification and interpretation
Microarray experiment
Description
Experimental design
Image analysis
Normalization
Clustering Discrimination
lab 2
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
graph - representations
1. Intensity(R)-Intensity(G)-Plot2. Ratio-A-Plot3. M-A-Plot
4. Mcorr-A-Plot5. Tusher - Plot
to decide if a normalization is necessary !
Intro lab 2
6
graph - representations
1. Intensity(R)-Intensity(G)-Plot2. Ratio-A-Plot3. M-A-Plot
4. Mcorr-A-Plot5. Tusher - Plot
Intro lab 2 Visualization
7
Visualization
0
10000
20000
30000
40000
0 10000 20000 30000 40000
Mean(Ch1)
Mea
n(C
h2)
Intensity(R)-Intensity(G)-Plot (1)
Intro lab 2
9
graph - representations
1. Intensity(R)-Intensity(G)-Plot2. Ratio-A-Plot3. M-A-Plot
4. Mcorr-A-Plot5. Tusher - Plot
Intro lab 2 Visualization
11
0,75
1
1,25
1,5
1,75
2
10 11 12 13 14 15 16
A
R/G
Ratio-A-Plot (2)
Intro lab 2 Visualization
log 2
12
graph - representations
1. Intensity(R)-Intensity(G)-Plot2. Ratio-A-Plot3. M*-A-Plot
4. Mcorr-A-Plot5. Tusher - Plot
Intro lab 2 Visualization
13
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
10 11 12 13 14 15 16
A
M
M* = log2(R/G)
mean(M*)
M*-A-Plot (3)
normalization for dye bias: M = M* - mean(M*)
Normalization -- dye biasIntro lab 2
*
14
M-A-Plot (3)
Intro lab 2
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
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0,8
1
10 11 12 13 14 15 16
A
M
mean(M)
normalization for dye bias: M = M* - mean(M*)
M = log2(R/G)-mean(M*)
Normalization -- dye bias
15
M-A-Plot
Intro lab 2
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
10 11 12 13 14 15 16
A
M
Visualization
16
Normalization -- hybridization-bias
M-A-Plot
Intro lab 2
y = -0,452x2 + 1,289x - 7,134-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
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1
10 11 12 13 14 15 16
A
M
17
Intro lab 2
M-A-Plot
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
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0,8
1
10 11 12 13 14 15 16
A
Mco
rr
Normalization -- hybridization-bias
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
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
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1
10 11 12 13 14 15 16
A
Mco
rr
differential expression
19
graph - representations
1. Intensity(R)-Intensity(G)-Plot2. Ratio-A-Plot3. M-A-Plot
4. Mcorr-A-Plot5. Tusher - Plot
Intro lab 2 differential expression
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
differential Expression 2
-1,2
-0,6
0
0,6
1,2
0 0,2 0,4 0,6 0,8 1 1,2 1,4
StdErr(Mcorr)
S
Tusher - Plot
S = Mcorr / (a+StdErr(Mcorr)), a=0.442
Intro lab 2
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
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)!
lab 2
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
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!
lab 2
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*=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
lab 2
27
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)
lab 2
28
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)!!
lab 2