1

Intro

&

materials

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

8

10

12

14

10 12 14

Log(G)

Log

(R)

Visualization

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

10

A - a measure for hybridization :

A = mean(log2(R),log2(G))

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

0,6

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

0,6

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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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

29

pass your results

to

Dirk.Repsilber@ebc.uu.se