GLM common and trended dispersion EdgeR.
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Reema Singh ▴ 570
@reema-singh-4373
Last seen 10.3 years ago
Dear All, I have a query regarding using estimateGLMCommonDisp and estimateGLMTrendedDisp. It has been mentioned in the EdgeR manual[*Section 2.8.2 :- Estimating Dispersion*] "Note that we need to estimate either common dispersion or trended dispersions prior to the estimation of tagwise dispersions" and "If both exist, the default is to use the trended dispersions". Whereas in case study 4.4[*Section 4.4.4- Estimating the dispersion*] dispersion has been estimated using both common and Trended before computing the tagwise dispersion. Here my queries are:- 1) Is it fine to use both common and trended dispersion? 2) is there any specific conditions to use common or trended dispersion estimation? Kind Regards -- Reema Singh Postdoctoral Research Assistant College of Life Sciences University of Dundee, Dundee DD1 4HN, Scotland United Kingdom http://www.compbio.dundee.ac.uk/people.html [[alternative HTML version deleted]]
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Reema Singh ▴ 570
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> 1) Is it fine to use both common and trended dispersion? You can estimate both common and trended dispersions on the same DGEList object, but only one of them will be used. The other will be ignored. I believe that trended dispersion will always be preferred over common dispersion if both are available. > 2) is there any specific conditions to use common or trended dispersion > estimation? Well, broadly speaking, if you believe that there is a dependency between mean and dispersion in your data, then you need trended dispersion. If not, you would use common dispersion. A number of publications have argued that when modeling RNA-seq data with the negative binomial distribution, such a dependency exists. For example, the (I think) original DESeq paper: http://genomebiology.com/2010/11/10/R106/ So generally you would always want to use trended dispersion if you are analyzing RNA-seq. For other types of data, you would likely want to test that assumption. -Ryan
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> 1) Is it fine to use both common and trended dispersion? You can estimate both common and trended dispersions on the same DGEList object, but only one of them will be used. The other will be ignored. I believe that trended dispersion will always be preferred over common dispersion if both are available. > 2) is there any specific conditions to use common or trended dispersion > estimation? Well, broadly speaking, if you believe that there is a dependency between mean and dispersion in your data, then you need trended dispersion. If not, you would use common dispersion. A number of publications have argued that when modeling RNA-seq data with the negative binomial distribution, such a dependency exists. For example, the (I think) original DESeq paper: http://genomebiology.com/2010/11/10/R106/ So generally you would always want to use trended dispersion if you are analyzing RNA-seq. For other types of data, you would likely want to test that assumption. -Ryan
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Hello Ryan, Thanks very much for the clarification. It really helps. Regards On Thu, Jan 9, 2014 at 12:40 PM, Ryan C. Thompson <rct@thompsonclan.org>wrote: > 1) Is it fine to use both common and trended dispersion? >> > > You can estimate both common and trended dispersions on the same DGEList > object, but only one of them will be used. The other will be ignored. I > believe that trended dispersion will always be preferred over common > dispersion if both are available. > > > 2) is there any specific conditions to use common or trended dispersion >> estimation? >> > > Well, broadly speaking, if you believe that there is a dependency between > mean and dispersion in your data, then you need trended dispersion. If not, > you would use common dispersion. A number of publications have argued that > when modeling RNA-seq data with the negative binomial distribution, such a > dependency exists. For example, the (I think) original DESeq paper: > http://genomebiology.com/2010/11/10/R106/ > > So generally you would always want to use trended dispersion if you are > analyzing RNA-seq. For other types of data, you would likely want to test > that assumption. > > -Ryan > -- Reema Singh Postdoctoral Research Assistant College of Life Sciences University of Dundee, Dundee DD1 4HN, Scotland United Kingdom http://www.compbio.dundee.ac.uk/people.html [[alternative HTML version deleted]]
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