[BioC] Paired t-test query

[James MacDonald]

Hi Priyanka,

Since you put the two time points on the same array, you have explicitly set up the pairing already. You don't need to separate the data into a dual color analysis. Just read in normally, normalize within arrays (I don't know about between arrays - IIRC, Gordon doesn't seem to recommend that step for two-color arrays), and then make the comparison. You don't even have to create a design matrix - limma will automatically supply you with the correct design matrix for your experiment.

As for paired t-test vs time course, it depends on what your hypothesis is.

Best,

Jim



James W. MacDonald, M.S.
Biostatistician
Douglas Lab
5912 Buhl
1241 E. Catherine St.
Ann Arbor MI 48109-5618
734-615-7826
>>> "Kachroo, Priyanka"  01/06/11 12:35 PM >>>
Hi All,

I would like to ask the forum if a paired t-test is better suited for my experimental design or a time course analysis. I have three time points T0, T6 and T12 for a group of animals. 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 used dual color arrays and hence separated the channels before fitting the model.

For example when i try to do a paired t-test between T0 and T6 i get 50 warning messages.
 I have 5 different animals (biological replicates) that were given no treatment at T0 and a treatment at T6. I tried to run the code as given in limma manual for paired t-test. Since i have two color array i separated the channels first. However i do not understand where the code is wrong. Therefor can someone please explain if the code is right for performing a paired t-test and whether performing a t-test at all is a good idea or not for my design. 
>
> 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
>> RG$genes<-readGAL()
>> RG <- backgroundCorrect(RG, method="normexp", offset=50)
> Green channel
> Corrected array 1
> Corrected array 2
> Corrected array 3
> Corrected array 4
> Corrected array 5
> Red channel
> Corrected array 1
> Corrected array 2
> Corrected array 3
> Corrected array 4
> Corrected array 5
>> MA.p <- normalizeWithinArrays(RG)
>> MA.Aq<-normalizeBetweenArrays(MA.p,method="Aquantile")
>> targets2<-targetsA2C(targets)
>> targets2
>    channel.col SlideNumber     FileName Identity Pairing Target
> 1.1           1    14117071 14117071.gpr       61       1     T6
> 1.2           2    14117071 14117071.gpr       61       1     T0
> 2.1           1    14117070 14117070.gpr      123       2     T6
> 2.2           2    14117070 14117070.gpr      123       2     T0
> 3.1           1    14116987 14116987.gpr      308       3     T6
> 3.2           2    14116987 14116987.gpr      308       3     T0
> 4.1           1    14117067 14117067.gpr      315       4     T0
> 4.2           2    14117067 14117067.gpr      315       4     T6
> 5.1           1    14117099 14117099.gpr      319       5     T0
> 5.2           2    14117099 14117099.gpr      319       5     T6
>> u<-unique(targets2$Target)
>> Pairing<-factor(targets2$Pairing)
>> Exposure<-factor(targets2$Target,levels=c("T0","T6"))
>> design<-model.matrix(~Pairing+Exposure)
>> corfit<-intraspotCorrelation(MA.Aq,design)
> There were 50 or more warnings (use warnings() to see the first 50)
>

Priyanka Kachroo
Graduate Assistant Research
Texas A&M University

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