On Sat, Dec 15, 2012 at 4:14 PM, capricy gao <capricyg@yahoo.com> wrote:

> I really wish that the answers are only related to my questions in this
> maillist. I know I am learning. If you feel the questions appear too
> stupid, you can simply choose to ignore. Unnecessary suggestions are not
> helpful at all.
>
>
Hi, Capricy.

For the record, you are asking *volunteers* on a public email list to
explain something to you.  No one is trying to be patronizing and I hope
you are not taking answers offered as anything other than an honest attempt
to answer your question.

Now, back to your question.  Both limma and edgeR fall into a category of
models called "Generalized Linear Models".  So, if you really want to get
up-to-speed on what the differences are, you can start there.  Believe it
or not, youtube has several lectures on glm, but google and wikipedia may
be helpful here, also, in addition to the material that Wolfgang suggested.


That said, basically, limma and edgeR differ in the components of the
generalized linear model that are used.  These differences are, as Steve
pointed out, dictated by the characteristics of the data being analyzed.
 In microarray data, the data are generally approximately normally
distributed.  With sequencing data, the data are close to a poisson model,
but with a little extra variance than expected for a poisson; this extra
variance leads to the use of the negative binomial distribution.  In
summary, a generalized linear model can have different components "plugged
in" and that the choice of these components is dictated by the data under
analysis.

Hope that helps.

Sean



>
>
>
> ________________________________
>  From: Steve Lianoglou <mailinglist.honeypot@gmail.com>
>
> Cc: Wolfgang Huber <whuber@embl.de>; "bioconductor@r-project.org" <
> bioconductor@r-project.org>
> Sent: Saturday, December 15, 2012 2:25 PM
> Subject: Re: [BioC] linear models for microarray and RNA-seq
>
>
> Hi,
>
> On Saturday, December 15, 2012, capricy gao  wrote:
>
> I hate to say this. However, to be honest, I don't think those papers are
> very helpful for biologists.
>
> I also hate to say this, but if the tutorials are written at a level that
> you can't make much sense of, then perhaps you should be collaborating with
> someone who can?
>
> I don't mean that as an insult -- I also had a hard time digesting all of
> this stuff at first, but there's a non-trivial amount of expertise
> (stats) you need to get comfortable with before you can hope to *really*
> make sense of this stuff.
>
> Bench protocols aren't written to be understood by statisticians, you know?
>
>
>
>
>
>
>
>
>  Not that there is any harm in someone trying to learn.
>
> In short, limma has been used to analyze microarray data, and there are
> assumptions about the characteristics of that data that do not apply to
> sequencing data. edgeR was built to handle these differences for sequencing
> data
>
> There is also "voom" in limma which apparently can handle sequencing data
>
> You are approaching the reason why each of them isn't suited for the other
> with your reference to normal vs. negative binomial distributions. There is
> a subtlety in what you said, though ... in layman terms, it's not that they
> "require" normal (or neg B) data, but rather that they use these
> distributions as assumptions for aspects of the data in order to
> correctly  model it ... or so. Using one in place of the other can (in some
> sense) be done, which is my point re: your use of "require", but it would
> be wrong.
>
> Anyway. There is lots of literature (and tutorials (even on the
> bioconductor website from precious workshop)) on this -- ie. the use of NB
> or poisson distros for sequencing data, and a google search with those
> keywords would likely be very fruitful if you are genuinely interested into
> digging into this further for self study.
>
> If you just want to analyze data and get results, then just take it all as
> gospel and use the suggested tools for the data you have at hand ;-)
>
> HTH,
> -steve
>
>
>
>
>
> >
> >
> >
> >________________________________
> > From: Wolfgang Huber <whuber@embl.de>
> >
> >Cc: "bioconductor@r-project.org" <bioconductor@r-project.org>
> >Sent: Saturday, December 15, 2012 10:02 AM
> >Subject: Re: [BioC] linear models for microarray and RNA-seq
> >
> >Dear Xin
> >
> >the accompanying papers and package vignettes might be a good place to
> start.
> >The authors of these packages have put a lot of effort in these, you
> could check them out.
> >
> >    Best wishes
> >    Wolfgang
> >
> >
> >ha scritto:
> >
> >> Limma and EdgeR both used linear models to fit the data. Anyone could
> explain the differences between these two linear models? I noticed Limma
> requires normal data and edgeR needs negative binomial distribution.
> >>
> >> Thanks.
> >>
> >> Xin
> >>     [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
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> >> Search the archives:
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> >        [[alternative HTML version deleted]]
> >
> >
>
> --
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
>  | Memorial Sloan-Kettering Cancer Center
>  | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact
>         [[alternative HTML version deleted]]
>
>
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>

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