[BioC] (control vs treatment) in (wildtype vs mutant) microarray analysis
Saroj K Mohapatra
saroj at vt.edu
Mon May 25 17:40:22 CEST 2009
In section "8.7 Factorial Designs" of Limma Users Guide, there is a
1. which genes respond to stimulation in wild-type cells,
2. which genes respond to stimulation in mutant cells, and
3. which genes respond differently in mutant compared to wild-type cells.
The contrast matrix is set up accordingly.
Following the same logic, in the current situation, how about such a
> cont.matrix <- makeContrasts(
The first one (TOXinWT) is for the effect of toxin in wild-type, while
the latter (Diff) is for the genes that show different effect of toxin
in mutant (compared to that in wild-type)?
Cheng-Yuan Kao wrote:
> We have done affy microarrays for wildtype-control treatment,
> wildtype-toxin treatment, mutant-control treatment and mutant-toxin
> The goal is to find diffferentially expressed genes regulated by toxin
> in wildtype and then find out which of these regulation are mutant
> The first goal is typical. So we did R/ bioconductor - SAM and limma.
> Both could give us a bunch of DEGs. However, I am lost about getting
> the second aim done.
> With limma and 2x2 factorial analysis, we could find the DEGs from all
> kind of pairs, such as wildtype -Toxin/control (this answers the first
> goal) or mutant/wildtype in control treatment (this tells us how the
> mutant gene is affecting the basal expression without toxin).
> But I don't know how to find the wildtype DEGs which have regulation
> depending on the mutant gene. Say one gene is up-regulated 100 folds
> by toxin (i.e. toxin treatment/control treatment) in wildtype. Then if
> this gene is up-regulated 3 folds (toxin treatment/control treatment)
> in mutant, it will be apparently mutant gene dependent (from 100 folds
> in wildtype to 3 folds in mutant). However, this gene will be shown as
> DEG in the mutant analysis as well since it is more than the 2-fold
> cutoff. Then if I only compared the DEGs in different pair of analysis
> from limma, I will miss these kind of genes. There must be some
> dedicated way to analyze this but I could not find it. Any suggestion
> will be appreciated. Thanks a lot.
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