Dear Gordon,
Thank you so much for your reply and help. I greatly appreciate it.
My data is exactly count data, which are integers. I got the same
results after I updated R/edgeR and rerun the code.
I have some questions related to the dispersion estimation using edgeR
package. Briefly introduction of my multi-factor project (please refer
to my previous post for more details), I have three factors: L (16
levels), S (2 levels) and R(3 levels), so here I totally have 16 x 2 x
3 = 96 different conditions.
1. Due to some reasons, one of the condition has only one replicate,
all the other conditions have at least 5 replicates. In this
situation, how does edgeR estimate the common dispersion and tagwise
dispersion?
2. I searched quite a lot about the examples using edgeR to estimate
the dispersion (including those examples shown in edgeR user guide), I
found that the common dispersion was not greater than 1 in most of
cases, however, I got 3.999943 for the common dispersion and 0.0624991
for all of the tagwise dispersion. When the tagwise dispersions
approach to same value, shouldn't they be close to the common
dispersion?
3. Use current version of edgeR, I tried different values for prior.df
(including the default) in the input of estimateTagwiseDisp function.
However, I always got the same results for tagwise dispersion
estimates. Are there any other input parameters in estimateTagwiseDisp
function that will affect the estimate results? And users can input
the values for these parameter according to their own projects?
Please feel free to correct me if I make some mistakes here. I will
also greatly appreciate it if you can provide any other suggestions.
Best regards,
Yanzhu
###########################################################
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 at="" bioconductor.org="">
> To: bioconductor at r-project.org, mlinyzh at 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:Se
x: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.
-- output of sessionInfo():
sessionInfo()
--
Sent via the guest posting facility at bioconductor.org.
