Dear Sir or Madam,

I am new on this mailing list so I apologise if this subject has already
been discussed.

I am using Limma 2.4.7 on R 2.2.1 to analyse two-color arrays.
My experiment is as follow:
Slide 1 = stimulated_cells_6h vs Ref
Slide 2 = Ref vs stimulated_cells_6h
Slide 3 = stimulated_cells_24h vs Ref
Slide 4 = Ref vs stimulated_cells_24h

I want to know which genes are modulated at 6h, which are modulated at
24h and those modulated between 6h and 24h.
After normalisation of the data I use the following script to create a
matrix and a contrast matrix:

> design <- cbind("6h"=c(1,-1,0,0),"24h"=c(0,0,1,-1))
> targets <- readTargets() # ‘Targets.txt’ contains the names of the
slides and their description Cy3/Cy5 in the good order
> rownames(design) <- removeExt(targets$FileName)
> design
                                  6h 24h
62088_Th2_6hCy5vsCtrlCy3_100_80   1   0
62090_CtrlCy5vsTh2_6hCy3_100_80  -1   0
62091_Th2_24hCy5vsCtrlCy3_100_80  0   1
62096_CtrlCy5vsTh2_24hCy3_100_80  0  -1
> fit <- lmFit(MA, design)
> cont.matrix <- cbind("24h-6h"=c(-1, 1), "6h"=c(1,0), "24h"=c(0,1))
> rownames(cont.matrix) <- cbind("6h","24h")
> cont.matrix
    24h-6h 6h 24h
6h      -1  1   0
24h      1  0   1
> fitcont <- contrasts.fit(fit, cont.matrix)
> fitcontbayes<-eBayes(fitcont)

> topTable(fitcontbayes, coef=1, n=50, adjust="fdr")
      Block Column Row     ID      Name Status         M
A         t    P.Value        B
21897    39      9   1  46861      PRAC   Gene  2.876222 8.631290
8.433385 0.03855747 3.506137
20473    36      1  14  28828     PAPD1   Gene -2.383738 6.927029
-7.473090 0.03855747 2.783536
19040    34      8   2  78497    CLDN23   Gene -3.139618 5.859508
-8.299092 0.03855747 2.386795
6221     11      5  20  84193 LOC115749   Gene  3.010418 5.792223
7.957571 0.03855747 2.193344
19497    34      9  21 125848    CRISP3   Gene -2.376080 6.925017
-6.742931 0.07172051 2.143568
21099    37      3  16 147050    HCG2P7   Gene -2.724596 6.706554
-7.202047 0.07172051 1.713956
6196     11      4  19  64892    ETR101   Gene  1.954074 8.169356
6.048570 0.13538457 1.453035
6199     11      7  19 149357       #NA   Gene  1.898843 8.060294
5.954056 0.14110973 1.352464
21132    37     12  17 155265 LOC440590   Gene -2.527218 6.638719
-6.680307 0.10272218 1.336678
20556    36     12  17 160410       #NA   Gene  2.082918 7.656239
5.881841 0.14362662 1.274528

> topTable(fitcontbayes, coef=2, n=50, adjust="fdr")
      Block Column Row     ID         Name Status         M
A         t      P.Value        B
2603      5     11  13 128320          CA2   Gene  2.312298  8.323775
12.690864 0.0004900596 7.446470
23250    41     18   9  83486       CYP1B1   Gene  1.365898  9.351358
7.529896 0.0168728265 4.118037
14667    26      3  12  89981          TPO   Gene -1.370724  9.825983
-7.441705 0.0168728265 4.018976
17543    31     23  11  95124          TNC   Gene  1.362982 11.478777
7.450669 0.0176675806 3.633118
27313    48      1  11  59209         CTSC   Gene  1.337229 10.259389
6.727203 0.0276792498 3.169911
2542      5     22  10  91105        FABP5   Gene  1.180159 10.683417
6.708199 0.0276792498 3.146168
19497    34      9  21 125848       CRISP3   Gene  2.254023  6.925017
7.834144 0.0168728265 3.042473
4862      9     14  11 158129       CAPN14   Gene  2.252979  5.170231
8.422209 0.0168728265 3.002382
18922    33     10  21  49700 DKFZp547F072   Gene  2.241397  6.368605
8.378910 0.0168728265 2.975528
20210    36      2   3 129217     MGC10854   Gene  2.155348  6.019866
8.057238 0.0176289711 2.768940

> topTable(fitcontbayes, coef=3, n=50, adjust="fdr")
      Block Column Row     ID        Name Status         M
A          t      P.Value        B
23257    41      1  10 119072       CDH26   Gene  2.045561  8.618405
10.977306 0.0007520237 8.012605
2550      5      6  11 112799    SERPINB3   Gene  1.905378  9.951108
10.146438 0.0007520237 7.297240
21897    39      9   1  46861        PRAC   Gene  3.036560  8.631290
10.904529 0.0007520237 6.084763
6221     11      5  20  84193   LOC115749   Gene  3.157852  5.792223
11.804853 0.0007520237 5.857994
20473    36      1  14  28828       PAPD1   Gene -2.693871  6.927029
-10.343418 0.0009737256 5.746556
27313    48      1  11  59209        CTSC   Gene  1.677554 10.259389
8.439275 0.0036026318 5.575635
17543    31     23  11  95124         TNC   Gene  2.432964 11.478777
9.404284 0.0022965417 5.110162
18097    32      1  11 104580        CTSC   Gene  1.414608  9.663292
8.014200 0.0056176380 5.087315
22151    39     23  11 104788      COL6A3   Gene  1.507607  9.470752
7.775592 0.0069709347 4.801786
8275     15     19   9  53347 TXN delta 3   Gene  1.340699 11.341639
7.522172 0.0090096504 4.489250

The obtained M values are OK and the A value for coef=1 is OK but A
value of coef=2 (6h) and coef=3  (24h) are false. The result is the mean
of the 4 slides where one expected a mean between only 2 slides (slides
1 and 2 for 6h and slides 3and 4 for 24h). Have a look for example at
gene 'CRISP3', it has the same A value for coef = 1 and coef = 2. Same
for gene 'TNC' between coef = 2 and coef = 3.
I first though it was just a problem in the report of A value in
topTable but it seems that the P.value and B value are also considerably
modify.
Is that a mistake in 'lmFit' or 'contrasts.fit' functions or am I doing
something wrong in the design of my matrix and contrast matrix?

Many thanks for any help or comments.

David


######################################
David HOT, PhD.
Laboratoire d'Etudes Transcriptomiques et Génomiques Appliquées -
Plate-forme Biopuces
UMR8161 - IFR142

Institut Pasteur de Lille
1, rue du professeur Calmette
59 019 LILLE, Cedex.
FRANCE
Tél : +33 (0)3 20 87 72 09
Fax : +33 (0)3 20 87 73 11
E-mail : david.hot@pasteur-lille.fr
http://www.pasteur-lille.fr/fr/recherche/biopuces/index.html
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