I want to do an intergrated network analysis for my Differential
genes. So could anyone tell me how can I do. Which package should I
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Can you tell more about your project and what exactly you mean
by integrated network analysis. What are you integrating into your DE
genes? what organism are you working with? What are you trying to
the data. Is the data RNA seq or micro array (and which platform).
bioconductor has many packages that you can use to handle networks,
`best` package depends on your goals / aims / resources.
On Tue, May 7, 2013 at 5:57 AM, Abdul Rawoof <email@example.com>
> I want to do an intergrated network analysis for my Differential
> genes. So could anyone tell me how can I do. Which package should I
> Abdul Rawoof
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> Bioconductor mailing list
> Search the archives:
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Your test for the 3-way interaction is correct, although 3-way
interactions are pretty hard to interpret.
However testing for the 2-way interaction in the presence of a 3-way
interaction does not make statistical sense. This is because the
parametrization of the 2-way interaction as a subset of the 3-way is
somewhat arbitrary. Before you can test the 2-way interaction
species*treatment in a meaningful way you would need to accept that
3-way interaction is not necessary and remove it from the model.
In general, I am of the opinion that classical statistical factorial
interation models do not usually provide the most meaningful
parametrizations for genomic experiments. In most cases, I prefer to
the saturated model (a different level for each treatment combination)
make specific contrasts. There is some discussion of this in the
In your case, I guess that you might want to test for
interaction separately at each time point. It is almost impossible to
this within the classical 3-way factorial setup. However it is easy
the one-way approach I just mentioned, or else you could use:
~Age + Age:Species + Age:Treatment + Age:Species:Treatment
> Date: Thu, 9 May 2013 14:55:46 -0400
> From: Hilary Smith <hilary.a.smith.964 at="" nd.edu="">
> To: "bioconductor at r-project.org" <bioconductor at="" r-project.org="">
> Subject: [BioC] Statistics question for multi-factor interaction
> in edgeR
> Hi. I need to generate two GLM tests of a factorial design with RNA-
> count data. I have 3 factors with 2 levels apiece (2 species X 2
> treatments X 2 times), and 4 separate replicates each (i.e., we made
> total of 2*2*2*4 = 32 separate libraries). Our main interest is in
> interaction of species*treatment, as we think species A will alter
> expression in the treatment stress vs. treatment benign, whereas
> species B is expected to show little change. However, we?d like to
> do another test of species*treatment*time, because it is possible
> the ability of species A to alter gene expression in response to the
> stress treatment may differ at the 1st versus 2nd time point.
> I think the way to set this up, is to create a design matrix as
> with the lrt test with coef 5 giving the differentially expressed
> for the species*treatment test, and coef 8 giving the the
> expressed gene for the species*treatment*time test (after calling
> topTags that is). Yet to ensure I have the statistics correct, my
> questions are: (1) is this thinking correct, as I don?t see many 3x2
> factorial models to follow, and (2) do I need to set up a reference
> somehow (which I assume would be the set of four samples with
> TreatmentBenign*SpeciesB*Time2, but I?m not fully sure if that is
> correct or needed).
> Many thanks in advance for your insight!
>> designFF <- model.matrix(~Treatment*Species*Age)
>  "(Intercept)"
>  " TreatmentStress"
>  "SpeciesA "
>  "Time1"
>  "TreatmentStress:SpeciesA"
>  "TreatmentStress:Time1"
>  "SpeciesA:Time1"
>  "TreatmentStress:SpeciesA:Time1"
> And then to run tests with:
>> fit <- glmFit(y, designFF)
>> lrtInteractionStressSpecies <- glmLRT(fitFF, coef=5)
>> lrtInteractionStressSpeciesTime <- glmLRT(fitFF, coef=8)
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