[BioC] Loop design - biological, technical replication and contrasts

Maciej Jończyk mjonczyk at biol.uw.edu.pl
Tue Mar 2 13:01:23 CET 2010


Hi again,

I apologise for replying to my own post, but it helps keep track if
someone will be interested.

I analysed my data with single channel analysis in limma, according to
Chapter 9. of limma usersguide.

I changed my targets file (to make it more condensed) and removed suffix
which
identified biological replication. So my targets looks like:

>nt_trg

   SlideNumber                   FileName Cy3 Cy5
1           93  c_093_DH_K_vs_DH_CHex.gpr  hk  hc
2          104  c_104_DH_CH_vs_DH_Kex.gpr  hc  hk
3          116    c_116_DHK_vs_DHCHex.gpr  hk  hc
4           16   c_016_DH_C_vs_DH_Kex.gpr  hc  hk
5           94   c_094_DH_K_vs_DL_Kex.gpr  hk  lk
6          105   c_105_DL_K_vs_DH_Kex.gpr  lk  hk
7          117     c_117_DHK_vs_DLKex.gpr  hk  lk
8          139   c_139_DL_K_vs_DH_Kex.gpr  lk  hk
9           92  c_092_DL_CH_vs_DL_Kex.gpr  lc  lk
10         106  c_106_DL_K_vs_DL_CHex.gpr  lk  lc
11         118    c_118_DLCH_vs_DLKex.gpr  lc  lk
12          23   c_023_DL_K_vs_DL_Cex.gpr  lk  lc
13          95 c_095_DL_CH_vs_DH_CHex.gpr  lc  hc
14         107 c_107_DH_CH_vs_DL_CHex.gpr  hc  lc
15         119   c_119_DLCH_vs_DHCHex.gpr  lc  hc
16         136   c_136_DH_C_vs_DL_Cex.gpr  hc  lc
17         101  c_101_DL_K_vs_DH_CHex.gpr  lk  hc
18         103  c_103_DH_CH_vs_DL_Kex.gpr  hc  lk
19         121    c_121_DLK_vs_DHCHex.gpr  lk  hc
20          15   c_015_DH_C_vs_DL_Kex.gpr  hc  lk
21         100  c_100_DH_K_vs_DL_CHex.gpr  hk  lc
22         102  c_102_DL_CH_vs_DH_Kex.gpr  lc  hk
23         120    c_120_DHK_vs_DLCHex.gpr  hk  lc
24         140   c_140_DL_C_vs_DH_Kex.gpr  lc  hk


I transform it to apropriate form:
>tgr_sc=targetsA2C(nt_trg)
>tgr_sc

     channel.col SlideNumber                   FileName Target
1.1            1          93  c_093_DH_K_vs_DH_CHex.gpr     hk
1.2            2          93  c_093_DH_K_vs_DH_CHex.gpr     hc
2.1            1         104  c_104_DH_CH_vs_DH_Kex.gpr     hc
2.2            2         104  c_104_DH_CH_vs_DH_Kex.gpr     hk
3.1            1         116    c_116_DHK_vs_DHCHex.gpr     hk
3.2            2         116    c_116_DHK_vs_DHCHex.gpr     hc
4.1            1          16   c_016_DH_C_vs_DH_Kex.gpr     hc
4.2            2          16   c_016_DH_C_vs_DH_Kex.gpr     hk
5.1            1          94   c_094_DH_K_vs_DL_Kex.gpr     hk
5.2            2          94   c_094_DH_K_vs_DL_Kex.gpr     lk
6.1            1         105   c_105_DL_K_vs_DH_Kex.gpr     lk
6.2            2         105   c_105_DL_K_vs_DH_Kex.gpr     hk
7.1            1         117     c_117_DHK_vs_DLKex.gpr     hk
7.2            2         117     c_117_DHK_vs_DLKex.gpr     lk
8.1            1         139   c_139_DL_K_vs_DH_Kex.gpr     lk
8.2            2         139   c_139_DL_K_vs_DH_Kex.gpr     hk
9.1            1          92  c_092_DL_CH_vs_DL_Kex.gpr     lc
9.2            2          92  c_092_DL_CH_vs_DL_Kex.gpr     lk
10.1           1         106  c_106_DL_K_vs_DL_CHex.gpr     lk
10.2           2         106  c_106_DL_K_vs_DL_CHex.gpr     lc
11.1           1         118    c_118_DLCH_vs_DLKex.gpr     lc
11.2           2         118    c_118_DLCH_vs_DLKex.gpr     lk
12.1           1          23   c_023_DL_K_vs_DL_Cex.gpr     lk
12.2           2          23   c_023_DL_K_vs_DL_Cex.gpr     lc
13.1           1          95 c_095_DL_CH_vs_DH_CHex.gpr     lc
13.2           2          95 c_095_DL_CH_vs_DH_CHex.gpr     hc
14.1           1         107 c_107_DH_CH_vs_DL_CHex.gpr     hc
14.2           2         107 c_107_DH_CH_vs_DL_CHex.gpr     lc
15.1           1         119   c_119_DLCH_vs_DHCHex.gpr     lc
15.2           2         119   c_119_DLCH_vs_DHCHex.gpr     hc
16.1           1         136   c_136_DH_C_vs_DL_Cex.gpr     hc
16.2           2         136   c_136_DH_C_vs_DL_Cex.gpr     lc
17.1           1         101  c_101_DL_K_vs_DH_CHex.gpr     lk
17.2           2         101  c_101_DL_K_vs_DH_CHex.gpr     hc
18.1           1         103  c_103_DH_CH_vs_DL_Kex.gpr     hc
18.2           2         103  c_103_DH_CH_vs_DL_Kex.gpr     lk
19.1           1         121    c_121_DLK_vs_DHCHex.gpr     lk
19.2           2         121    c_121_DLK_vs_DHCHex.gpr     hc
20.1           1          15   c_015_DH_C_vs_DL_Kex.gpr     hc
20.2           2          15   c_015_DH_C_vs_DL_Kex.gpr     lk
21.1           1         100  c_100_DH_K_vs_DL_CHex.gpr     hk
21.2           2         100  c_100_DH_K_vs_DL_CHex.gpr     lc
22.1           1         102  c_102_DL_CH_vs_DH_Kex.gpr     lc
22.2           2         102  c_102_DL_CH_vs_DH_Kex.gpr     hk
23.1           1         120    c_120_DHK_vs_DLCHex.gpr     hk
23.2           2         120    c_120_DHK_vs_DLCHex.gpr     lc
24.1           1         140   c_140_DL_C_vs_DH_Kex.gpr     lc
24.2           2         140   c_140_DL_C_vs_DH_Kex.gpr     hk

Next, I made design matrix

>u=unique(tgr_sc$Target)
>f=factor(tgr_sc$Target,levels=u)
>design=model.matrix(~0+f)
>colnames(design)=u
>design

   hk hc lk lc
1   1  0  0  0
2   0  1  0  0
3   0  1  0  0
4   1  0  0  0
5   1  0  0  0
6   0  1  0  0
7   0  1  0  0
8   1  0  0  0
9   1  0  0  0
10  0  0  1  0
11  0  0  1  0
12  1  0  0  0
13  1  0  0  0
14  0  0  1  0
15  0  0  1  0
16  1  0  0  0
17  0  0  0  1
18  0  0  1  0
19  0  0  1  0
20  0  0  0  1
21  0  0  0  1
22  0  0  1  0
23  0  0  1  0
24  0  0  0  1
25  0  0  0  1
26  0  1  0  0
27  0  1  0  0
28  0  0  0  1
29  0  0  0  1
30  0  1  0  0
31  0  1  0  0
32  0  0  0  1
33  0  0  1  0
34  0  1  0  0
35  0  1  0  0
36  0  0  1  0
37  0  0  1  0
38  0  1  0  0
39  0  1  0  0
40  0  0  1  0
41  1  0  0  0
42  0  0  0  1
43  0  0  0  1
44  1  0  0  0
45  1  0  0  0
46  0  0  0  1
47  0  0  0  1
48  1  0  0  0
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$f
[1] "contr.treatment"

*Is it correct form my design? I see, that it simply identifies what RNA
was hybridized on each array.

>corfit=intraspotCorrelation(nt_img_lA,design)
> corfit$consensus
[1] 0.7341876
>fit=lmscFit(nt_img_lAq,design,correlation=corfit$consensus)

I want to get contrasts "hc - hk", "lc - lk", "hc - lc", "hk - lk"
and also test effect of line and temperature. To do that I write this
command:


>contr.matrix=makeContrasts(hc-hk,lc-lk,hc-lc,hk-lk,linia=(hc+hk-lc-lk)/2,temp=(hc+lc-hk-lk)/2,inter=(hc-lc)-(hk-lk),levels=design)

* I'm not 100% sure that it's correct.

>contr.fit=contrasts.fit(fit,contr.matrix)
>contr.fit=eBayes(contr.fit)

>wynik=decideTests(contr.fit,method="global",adjust.method="BH",p.value=0.05)
>summary(wynik)
   hc - hk lc - lk hc - lc hk - lk linia  temp inter
-1    5865    5039    3014    2685  3931  7382  1113
0    30922   33433   37177   38480 35896 28364 40776
1     6594    4909    3190    2216  3554  7635  1492

From that it seem that there is a lot of differentially expressed genes.
I feel that it isn't optimal design, here technical and biological
replications
are treated in the same manner, aren't they?

I've read about "duplicateCorrelation" command, is it possible to
combine it with single channel analysis?
Or I should rewrite target file (add number of replication) and rewrite
contrasts
(e.g. hc-hk change to "((hc1+hc2+hc3+hc4)-(hk1+hk2+hk3+hk4))/4
)?

And if I want to include a dye effect, I should only add column with 1's
to my design, right?

Thank you for reading of my post.
I'd be very grateful for help. I've tried to analyse this data for a
along time
and I think limma is the best choice.

Yours sincerely,

Maciej Jończyk

Maciej Jończyk
Department of Plant Molecular Ecophysiology
Institute of Plant Experimental Biology
Faculty of Biology, University of Warsaw
02-096 Warszawa, Miecznikowa 1



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