Hi Maciej:

I have the same problem, I did the same procedure but still I'm getting
large numbers for differentially expressed genes. I could reduce this number
by defining p.value = 0.01 in decideTests. But I'm not completely sure
whether changing the "method" for decideTests and/or pvalue should give
better results.

I'm looking forward to someone else answer.

Best Regards,

Avhena

2010/3/2 Maciej Jończyk <mjonczyk@biol.uw.edu.pl>

> 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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