time series data edgeR?

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I have RNA seq data from two genotypes over five time points and I have been looking for software that handles such situations. I hoped edgeR would provide me with the the tool and that the edgeR Users Guide would contain coded examples. Alas, I cannot find anything on time series there. Has anyone addressed 'my' situation? If so, I would appreciate being notified. best regards jahn -- output of sessionInfo(): NA -- Sent via the guest posting facility at bioconductor.org.

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ADD COMMENT • link updated 10.4 years ago by Ryan C. Thompson ★ 7.9k • written 10.4 years ago by Guest User ★ 13k

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Ryan C. Thompson ★ 7.9k

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Scripps Research, La Jolla, CA

Yes, edgeR can handle this case. You have a couple of options. The conceptually simplest one is to treat the timepoint as a second factor and fit a two-factor additive model (i.e. model.matrix(~genotype + timepoint) ). Another option is to use natural splines. This is an approach that I am not personally experienced with, but it has been discussed before on this list, so you should be able to find an example in the archives. Personally, I would recommend using the two-factor analysis initially, and only switching to something more complex if you decide you need it. On Mon Dec 16 08:44:50 2013, Jahn Davik [guest] wrote: > > I have RNA seq data from two genotypes over five time points and I have been looking for software that handles such situations. I hoped edgeR would provide me with the the tool and that the edgeR Users Guide would contain coded examples. Alas, I cannot find anything on time series there. Has anyone addressed 'my' situation? If so, I would appreciate being notified. > > best regards > jahn > > -- output of sessionInfo(): > > NA > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor

ADD COMMENT • link 10.4 years ago Ryan C. Thompson ★ 7.9k

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Howdy, On Mon, Dec 16, 2013 at 8:57 AM, Ryan <rct at="" thompsonclan.org=""> wrote: > Yes, edgeR can handle this case. You have a couple of options. The > conceptually simplest one is to treat the timepoint as a second factor and > fit a two-factor additive model (i.e. model.matrix(~genotype + timepoint) ). > Another option is to use natural splines. This is an approach that I am not > personally experienced with, but it has been discussed before on this list, > so you should be able to find an example in the archives. Personally, I > would recommend using the two-factor analysis initially, and only switching > to something more complex if you decide you need it. To add to Ryan's advice -- I'm pretty sure there is an example of using splines for timecourse data in the limma user's guide. Assuming so, the least effort required to get a first round analysis by following along there would be to apply limma::voom to your count data, then follow the spline method from the user's guide accordingly. HTH, -steve -- Steve Lianoglou Computational Biologist Genentech

ADD REPLY • link 10.4 years ago Steve Lianoglou ★ 13k

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Hi, I would think there is correlation in time-course data, and two- factor model may not handle it correctly as it assumes that data are independent. I tried with linear mixed model by "lme4" packages and the results seems good. Best, Chunxuan > Date: Mon, 16 Dec 2013 09:12:51 -0800 > From: lianoglou.steve@gene.com > To: rct@thompsonclan.org > CC: bioconductor@r-project.org > Subject: Re: [BioC] time series data edgeR? > > Howdy, > > On Mon, Dec 16, 2013 at 8:57 AM, Ryan <rct@thompsonclan.org> wrote: > > Yes, edgeR can handle this case. You have a couple of options. The > > conceptually simplest one is to treat the timepoint as a second factor and > > fit a two-factor additive model (i.e. model.matrix(~genotype + timepoint) ). > > Another option is to use natural splines. This is an approach that I am not > > personally experienced with, but it has been discussed before on this list, > > so you should be able to find an example in the archives. Personally, I > > would recommend using the two-factor analysis initially, and only switching > > to something more complex if you decide you need it. > > To add to Ryan's advice -- I'm pretty sure there is an example of > using splines for timecourse data in the limma user's guide. Assuming > so, the least effort required to get a first round analysis by > following along there would be to apply limma::voom to your count > data, then follow the spline method from the user's guide accordingly. > > HTH, > -steve > > -- > Steve Lianoglou > Computational Biologist > Genentech > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]]

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