[BioC] two color time course analysis

[Kachroo, Priyanka]

Hi All,

I would like to ask the forum the best statistical analysis approach for my experimental design in which i have three time points T0, T6 and T12 for a treatment group. I need to evaluate the DE genes between T6&T0 and also T12&T0. Since the same set of animals were involved at all three time points, will a paired t-test for T6-T0 and T12-T0 be a better strategy or a time course analysis. 

I have dual color arrays hybridized in the following format. I tried to do a time series analysis by first separating the channels and then setting the contrasts as depicted in limma manual for single color arrays (section 8.8 in limma manual). However i get following error: "Error in intraspotCorrelation(MA.Aq, design) : The number of rows of the design matrix should match the number of channel intensities, i.e., twice the number of arrays".

Target file:
SlideNumber	FileName	Cy3	Cy5	Identity
14117071	14117071.gpr	T6	T0	61
14117070	14117070.gpr	T6	T0	123
14116987	14116987.gpr	T6	T0	308
14117067	14117067.gpr	T0	T6	315
14117099	14117099.gpr	T0	T6 	319
14116988	14116988.gpr 	T0	T12	61
14116990	14116990.gpr	T0	T12	123
14116964	14116964.gpr	T0	T12	308
14116989	14116989.gpr	T12	T0	315
14116948	14116948.gpr	T12	T0	319

Here is code used so far:
> targets<-readTargets("targets.txt")
> RG<-read.maimages(targets,source="genepix",columns=list(R="F635 Median",G="F532 Median",Rb="B635",Gb="B532"))
Read 14117071.gpr 
Read 14117070.gpr 
Read 14116987.gpr 
Read 14117067.gpr 
Read 14117099.gpr 
Read 14116988.gpr  
Read 14116990.gpr 
Read 14116964.gpr 
Read 14116989.gpr 
Read 14116948.gpr 
> RG$genes<-readGAL()
> spottypes<-readSpotTypes("Spottypes.txt")
> RG <- backgroundCorrect(RG, method="normexp", offset=50)
Green channel
Corrected array 1 
Corrected array 2 
Corrected array 3 
Corrected array 4 
Corrected array 5 
Corrected array 6 
Corrected array 7 
Corrected array 8 
Corrected array 9 
Corrected array 10 
Red channel
Corrected array 1 
Corrected array 2 
Corrected array 3 
Corrected array 4 
Corrected array 5 
Corrected array 6 
Corrected array 7 
Corrected array 8 
Corrected array 9 
Corrected array 10 
> MA.p <- normalizeWithinArrays(RG)
> MA.Aq<-normalizeBetweenArrays(MA.p,method="Aquantile")
> targets2<-targetsA2C(targets)
> targets2
     channel.col SlideNumber      FileName Identity Target
1.1            1    14117071  14117071.gpr       61     T6
1.2            2    14117071  14117071.gpr       61     T0
2.1            1    14117070  14117070.gpr      123     T6
2.2            2    14117070  14117070.gpr      123     T0
3.1            1    14116987  14116987.gpr      308     T6
3.2            2    14116987  14116987.gpr      308     T0
4.1            1    14117067  14117067.gpr      315     T0
4.2            2    14117067  14117067.gpr      315     T6
5.1            1    14117099  14117099.gpr      319     T0
5.2            2    14117099  14117099.gpr      319    T6 
6.1            1    14116988 14116988.gpr        61     T0
6.2            2    14116988 14116988.gpr        61    T12
7.1            1    14116990  14116990.gpr      123     T0
7.2            2    14116990  14116990.gpr      123    T12
8.1            1    14116964  14116964.gpr      308     T0
8.2            2    14116964  14116964.gpr      308    T12
9.1            1    14116989  14116989.gpr      315    T12
9.2            2    14116989  14116989.gpr      315     T0
10.1           1    14116948  14116948.gpr      319    T12
10.2           2    14116948  14116948.gpr      319     T0
> lev<-c("T0","T6","T12")
> u<-unique(targets2$Target)
> f<-factor(targets2$Target,levels=lev)
> design<-model.matrix(~0+f)
> colnames(design)<-lev
> corfit<-intraspotCorrelation(MA.Aq,design)
Error in intraspotCorrelation(MA.Aq, design) : 
  The number of rows of the design matrix should match the number of channel intensities, i.e., twice the number of arrays
> 

Can someone please help me with this error and how to obtain differentially expressed genes for contrasts "T6-T0" and "T12-T0".


Regards

Priyanka Kachroo