Hi Gordon,

What exactly does edgeR now do with non-integer counts? Does it just round
them to the nearest integer and proceed as normal, or does it somehow use
them as is?

-Ryan
On Apr 24, 2014 7:40 PM, "Gordon K Smyth" <smyth@wehi.edu.au> wrote:

> Dear Yanzhu,
>
> My guess is that some of your "count data" are not integers.  For example,
> are they perhaps expected counts from RSEM?  In the edgeR version that you
> are using, the GLM dispersion estimation functions do not work correctly
> for non-integer data.  (They weren't intended to.)
>
> Please update your copyies of R and edgeR to the latest versions.
> Bioconductor 2.14 was released a couple of weeks ago.  All edgeR functions
> now permit non-integer "counts".
>
> Also check that your data are counts and not RPKM or similar.  The counts
> should sum to the total sequence depth for each sample.
>
> Best wishes
> Gordon
>
>  Date: Wed, 23 Apr 2014 07:58:30 -0700 (PDT)
>> From: "Yanzhu [guest]" <guest@bioconductor.org>
>> To: bioconductor@r-project.org, mlinyzh@gmail.com
>> Subject: [BioC] EdgeR: dispersion estimation
>>
>>
>> Dear community,
>>
>> I use edgeR to do the data analysis of my RNA-seq project (as mentioned
>> in my previous posts about multi-factor analysis of RNA-Seq project), I
>> meet an issue with dispersion estimation:
>> I first used estimateGLMCommonDisp and then used estimateGLMTagwiseDisp
>> to estimate the dispersion, however, I got 3.999943 for y$common.dispersion
>> and 0.0624991 for all of the y$tagwise.dispersion (all of the
>> y$tagwise.dispersion are the same). isn't it that all of the tagwise
>> dispersion should NOT be the same?
>>
>> The fellowing is the code I used:
>> ##Read in count data
>> T<-data.frame(HTSeqRE)
>>
>> ##Factors:
>> Design<-data.frame(HTSeqCondRE[,2:4])
>> Rep<-as.factor(Design$Rep)
>> Line<-as.factor(Design$Line)
>> Sex<-as.factor(Design$Sex)
>> design<-model.matrix(~Line+Rep+Sex+Line:Rep+Line:Sex+Rep:
>> Sex+Line:Sex:Rep)
>>
>> group<-paste(Design$Line,Design$Sex,Design$Rep,sep=".")
>> y<-DGEList(counts=T,group=group)
>>
>>
>> y<-calcNormFactors(y,method="TMM")
>>
>> y<-estimateGLMCommonDisp(y,design)
>> y<-estimateGLMTagwiseDisp(y,design)
>>
>> y$common.dispersion
>> [1] 3.999943
>>
>> y$tagwise.dispersion
>> [1] 0.0624991 0.0624991 0.0624991 0.0624991 0.0624991
>> 13474 more elements ...
>>
>>
>> Yanzhu
>>
>> -- output of sessionInfo():
>>
>>  sessionInfo()
>>>
>> R version 3.0.1 (2013-05-16)
>> Platform: x86_64-w64-mingw32/x64 (64-bit)
>>
>> locale:
>> [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
>> States.1252    LC_MONETARY=English_United States.1252 LC_NUMERIC=C
>> [5] LC_TIME=English_United States.1252
>>
>> attached base packages:
>> [1] parallel  stats     graphics  grDevices utils     datasets  methods
>> base
>>
>> other attached packages:
>> [1] DESeq_1.12.1       lattice_0.20-27    locfit_1.5-9.1
>> Biobase_2.20.1     BiocGenerics_0.6.0 edgeR_3.2.4        limma_3.16.8
>>
>> loaded via a namespace (and not attached):
>> [1] annotate_1.38.0      AnnotationDbi_1.22.6 DBI_0.2-7
>>  genefilter_1.42.0    geneplotter_1.38.0   grid_3.0.1
>> IRanges_1.18.4
>> [8] RColorBrewer_1.0-5   RSQLite_0.11.4       splines_3.0.1
>>  stats4_3.0.1         survival_2.37-4      tools_3.0.1          XML_3.98-1.1
>> [15] xtable_1.7-3
>>
>> --
>> Sent via the guest posting facility at bioconductor.org.
>>
>
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