[BioC] two color time course analysis

[Gordon K Smyth]

Dear Priyanka,

Thanks for the complete code and output.  I can now see the problem.  In 
your targets file, in the row for 14117099.gpr, there is a trailing space 
after "T6".  In other words, "T6 " has been entered instead of "T6".

This would normally become evident when typing

  f <- factor(targets2$Target)

because f would show up with four levels instead of three.  However, you 
supplied levels for f explicitly, so the abnormal entry was removed, 
giving you one too few lines in your design matrix.

Best wishes
Gordon


On Tue, 4 Jan 2011, Gordon K Smyth wrote:

> Dear Priyanka,
>
> On the quick read through, I don't see any problems with your code.  It 
> should work perfectly as far as I can see.  Can you please give us the output 
> of
>
>  dim(MA.Aq)
>
> and
>
>  dim(design)
>
> at the time of the error.
>
> Best wishes
> Gordon
>
>> Date: Sun, 2 Jan 2011 22:00:06 -0600 (CST)
>> From: "Kachroo, Priyanka" <priya_coll at neo.tamu.edu>
>> To: bioconductor at r-project.org
>> Subject: [BioC] two color time course analysis
>> Message-ID: <200725487.382131294027206350.JavaMail.root at neo-mail-3>
>> Content-Type: text/plain; charset=utf-8
>> 
>> 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
>

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