Loop design - biological, technical replication and contrasts
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boczniak767 ▴ 720
@maciej-jonczyk-3945
Last seen 4 weeks ago
Poland
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.valu e=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
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avehna ▴ 240
@avehna-3930
Last seen 9.6 years ago
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.va lue=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 > > > > ___________________________________ > NOCC, http://nocc.sourceforge.net > > > > > -- > This email was Anti Virus checked by Astaro Security Gateway. > http://www.astaro.com > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]
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I also think about lower p-value cutoff (0,01). Below I pasted information about my experimental design (it is from my previus message). I have 2x2 factorial experiment conducted in a loop-design (two-colour data). There are two maize lines (dh7, dl3) and two temperature treatments (cold=c and control=k). Following hybridizations were conducted : dh_c vs dh_k; dh_k vs dl_k; dl_k vs dl_c; dl_c vs dh_c (forming a square on a diagram) and two (diagonal on a diagram), namely: dl_k vs dh_c and dh_k vs dl_c. I have four biological replications of this design, including dye-swap (i.e. two hyb. cy3-cy5 and two cy5-cy3). In each biological replication I have also three technical replications of each RNA source (i.e. in dh_c vs dh_k; dl_c vs dh_c and dl_k vs dh_c, sample dh_c is from the same RNA pool). I don't know how analyse it in limma. I want to get following contrasts: dh_c vs dh_k; dh_k vs dl_k; dl_k vs dl_c; dl_c vs dh_c and include effect of temperature, maize line and interaction. Avehna <avhena at="" gmail.com=""> nadawca : > > 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,Avhena2010/3/2 Maciej Jo?czyk <mjonczyk at="" 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.valu e=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 > > Jo?czyk Maciej Jo?czyk Department of Plant Molecular > > Ecophysiology Institute of Plant Experimental Biology Faculty of > > Biology, University of Warsaw 02-096 Warszawa, Miecznikowa 1 > > ___________________________________ NOCC, > > http://nocc.sourceforge.net -- This email was Anti Virus checked > > by Astaro Security Gateway. http://www.astaro.com > > _______________________________________________ Bioconductor mailing > > list Bioconductor at stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor Search the > > archives: > > http://news.gmane.org/gmane.science.biology.informatics.conductor Maciej Jo?czyk Department of Plant Molecular Ecophysiology Institute of Plant Experimental Biology Faculty of Biology, University of Warsaw 02-096 Warszawa, Miecznikowa 1 ___________________________________ NOCC, http://nocc.sourceforge.net
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