Hi Jim,

Thanks for the reply. I've tried that -  with a slighlty modified code. I
am sorry. But, I'm getting error again.

Here:

pair=factor(rep(c(1:45), each=2)).Treat=factor( rep(c("Before",
"After"),45), levels=c("Before", "After"))
Phenotype=factor(rep(c(1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1,  2, 2, 2, 2,
2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1,  2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2,
1, 2, 2), each=2),levels=c("1","2"))
design=model.matrix(~Phenotype+Phenotype:pair+Phenotype:Treat)
colnames(design)
counts.DGEList<-DGEList(counts, group=Treat)
y<-calcNormFactors(counts.DGEList)
y<-estimateCommonDisp(y, design)
y<-estimateGLMTrendedDisp(y, design)


The error message here:

> y<-estimateGLMTrendedDisp(y, design)
Error in return(NA, ntags) : multi-argument returns are not permitted
In addition: Warning message:
In estimateGLMTrendedDisp.default(y = y$counts, design = design,  :
  No residual df: cannot estimate dispersion


One reason I can see is that the number of columns in my design matrix  is
"92"  as they are more replicates/patients than in the 3.5 example. And
there are only "90" columns in my "count" matrix.

But, do you have any idea how can I solve this ?

Thanks,
Preethy





On Fri, Apr 25, 2014 at 6:45 PM, James W. MacDonald <jmacdon@uw.edu> wrote:

> Hi Preethy,
>
> This experiment is very similar to the example in part 3.5 of the edgeR
> User's guide, starting on page 31.
>
> Best,
>
> Jim
>
>
>
> On 4/25/2014 11:29 AM, Preethy Venkat Ram wrote:
>
>> Hi Devon,
>>
>> Thanks for the replies both on biostars and here.  Sorry for
>> crossposting.  Both
>> mailing lists and discussion have been very helpful to me.
>>
>> But, I rarely see replies from BioC package maintainers at biostars.
>>
>> All the samples are paired and I have them all correct - I mean none of
>> them are empty
>> I tried different designs. But ending up with the same error message.
>> What I want to do: Getting DE genes between
>> (Phenotype1.Before-Phenotype1.After) & (Phenotype2.Before-Phenotype2.
>> After)
>>
>> pdata here:
>>
>> pair=rep(c(1:45), each=2)
>> Treat=rep(c("Before", "After"),45)
>> Phenotype=rep(c(1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1,  2, 2, 2, 2, 2, 2,
>> 2, 1, 1, 1, 1, 1, 1, 2, 2, 1,  2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1,
>> 2,
>> 2), each=2)
>> pdata<-data.frame(pair,Treat,Phenotype)
>>
>>
>> Preethy
>>
>>
>>
>> On Fri, Apr 25, 2014 at 4:53 PM, Devon Ryan <dpryan@dpryan.com> wrote:
>>
>>  Hi Preethy,
>>>
>>> You likely want:
>>>
>>> design=model.matrix(~pair+Treat:Phenotype, data=pdata)
>>>
>>> If that still yields the error, then you'll need to share "pdata" or
>>> "design". Also, please don't crosspost on both this list and biostars (
>>> https://www.biostars.org/p/98907/), it duplicates the community effort.
>>>
>>> Devon
>>>
>>>
>>> --
>>> Devon Ryan, Ph.D.
>>> Email: dpryan@dpryan.com
>>> Laboratory for Molecular and Cellular Cognition
>>> German Centre for Neurodegenerative Diseases (DZNE)
>>> Ludwig-Erhard-Allee 2
>>> 53175 Bonn
>>> Germany
>>> <devon.ryan@dzne.de>
>>>
>>>
>>>
>>> On Fri, Apr 25, 2014 at 11:15 AM, Preethy [guest] <
>>> guest@bioconductor.org>wrote:
>>>
>>>  Hi All,
>>>>
>>>> I had been trying to do DE analysis of my RNAseq experiment using edgeR
>>>> and am having some isssues. The details of the Experiment and the R
>>>> code I
>>>> tried below:
>>>>
>>>> (a) Paired experimental design with 45 pairs
>>>> (b) Treatment: "Before" and "After"
>>>> (c) Phenotype: 1 & 2
>>>> Aim: Look for DE genes between Phenotype 1 and 2 upon treatment taking
>>>> into account the paired design
>>>>
>>>> The R code tried:
>>>>
>>>> library(edgeR)
>>>> counts<-read.delim(file="counts.dat",header=T)
>>>> pair=factor(pdata$pair)
>>>> Treat=factor( pdata$treat)
>>>> Phenotype=factor(pdata$pheno)
>>>> group<-paste(Treat,Phenotype,sep=".")
>>>> design=model.matrix(~pair+Treat:Phenotype, data=counts)
>>>> counts.DGEList<-DGEList(counts, group=group)
>>>> y<-calcNormFactors(counts.DGEList)
>>>> y<-estimateCommonDisp(y, design)
>>>> y<-estimateGLMTrendedDisp(y, design)
>>>>
>>>>
>>>> Error message I get:
>>>>
>>>> Error in glmFit.default(y, design = design, dispersion = dispersion,
>>>> offset = offset,  :
>>>>    Design matrix not of full rank.  The following coefficients not
>>>> estimable:
>>>>   TreatBefore:Phenotype1 TreatBefore:Phenotype2
>>>>
>>>>
>>>> Any idea to solve this out?
>>>>
>>>> Thanks,
>>>> Preethy
>>>>
>>>>   -- output of sessionInfo():
>>>>
>>>> R version 3.1.0 (2014-04-10)
>>>> Platform: x86_64-pc-linux-gnu (64-bit)
>>>>
>>>> locale:
>>>>   [1] LC_CTYPE=fi_FI.UTF-8       LC_NUMERIC=C
>>>>   [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8
>>>>   [5] LC_MONETARY=fi_FI.UTF-8    LC_MESSAGES=en_GB.UTF-8
>>>>   [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C
>>>>   [9] LC_ADDRESS=C               LC_TELEPHONE=C
>>>> [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
>>>>
>>>> attached base packages:
>>>> [1] stats     graphics  grDevices utils     datasets  methods   base
>>>>
>>>> other attached packages:
>>>> [1] edgeR_3.4.2   limma_3.18.13
>>>>
>>>>
>>>> --
>>>> Sent via the guest posting facility at bioconductor.org.
>>>>
>>>> _______________________________________________
>>>> Bioconductor mailing list
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>>>> Search the archives:
>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>>>
>>>>
>>>          [[alternative HTML version deleted]]
>>
>>
>> _______________________________________________
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>>
>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>
>

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