[BioC] LIMMA package differentially expressed genes

Gordon K Smyth smyth at wehi.EDU.AU
Thu Feb 24 23:16:49 CET 2011


Dear Prasad,

You don't need to worry about setting up the contrast matrix, as limma 
will do this for you.  You can use makeContrasts() and tell it directly 
what comparisons you want to do.  For example:

   cont.matrix <- makeContrasts(inf1-inf2,inf2-inf3,inf1-control1,levels=design)

You get the idea.  You can ask for any number of comparisons.  There are 
many examples of this in the limma User's Guide.

With respect to your first question, see Section 7.3 "Common reference 
designs".  The easiest way to is to created an identifier for each unique 
treatment/time point combination in your experiments.  Use modelMatrix() 
with ref="ref", and then proceed as above.

Best wishes
Gordon

> Date: Tue, 22 Feb 2011 19:35:46 +0000
> From: Prasad Siddavatam <siddavatam at gmail.com>
> To: <bioconductor at stat.math.ethz.ch>
> Subject: [BioC] LIMMA package differentially expressed genes
> Message-ID: <loom.20110222T201744-87 at post.gmane.org>
> Content-Type: text/plain; charset="us-ascii"
>
> I am kind of new to Gene expression studies.
>
> I have two questions.
> 1. What would be the design matrix for reference design with dye-swap time
> course experiment. I have 6 time points and at each time point, I have dye-swaps
> and 3 replicates for infection and control samples (A total of 12 samples at
> every time point). What I want is the differentially expressed between control
> and infection.
>
>
> 2. In other set, I have to make multiple comparisons (infection1, infection2,
> infection3). This is also a reference design with two-colors.
> sample    cy3    cy5
> array1      ref    inf.1
> array2      ref    inf.1
> array3      ref    inf.1
> array4      ref    inf.2
> array5      ref    inf.2
> array6      ref    inf.2
> array7      ref    inf.3
> array8      ref    inf.3
> array9      ref    inf.3
> array10     ref    control1
> array11     ref    control2
> array12     ref    control3
> what I want is differentially expressed genes between every pair of comparisons
> (ex. inf1-inf2).
> my design looks like this
>            inf1    inf2   inf3    control
> array1      1        0       0       0
> array2      1        0       0       0
> array3      1        0       0       0
> array4      0        1       0       0
> array5      0        1       0       0
> array6      0        1       0       0
> array7      0        0       1       0
> array8      0        0       1       0
> array9      0        0       1       0
> array10     0        0       0       1
> array11     0        0       0       1
> array12     0        0       0       1
>
> what would be my contrast matrix would be?
> and how to get the genes between inf2 and inf3?
>
> Prasad siddavatam

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