[BioC] Hello Sir/ Madam, a help needed

[Naomi Altman]

Actually, you can do a statistical test if you are willing to assume 
that the variance does not change with treatment (which is the usual 
ANOVA assumption),
because the first set of arrays includes biological replication.

However, I have not offered advice on this design because I am not 
familiar with Illumina arrays.  If they are like 1-channel arrays, 
you can use limma or maanova.
There are 3 treatments, and the technical replicate is a block.  This 
is highly unbalanced, and you analysis would probably be simpler if 
you take the "better" of the 2 technical replicates, where "better" 
refers to array quality.

Then you just have an unbalanced 1-way ANOVA and analysis in limma or 
maanova should be straight-forward.

If they are not like 1-channel arrays, then I defer to those who are 
familiar with this platform.

--Naomi

At 10:45 AM 11/20/2008, Sean Davis wrote:
>On Thu, Nov 20, 2008 at 5:37 AM, John Antonydas Gaspar <gasparj at uni-koeln.de
> > wrote:
>
> > Dear Sir,
> >
> > I am in need of your help to decide which test would be an appropraite in
> > this
> > case.
> >
> > I am involved in the data analysis of the out put from Illumina.
> > I have the following experimental design
> >
> > Array A : TBVII Treat 01
> > Array B : TBVII Treat 02
> > Array c : TBVII Treat 03
> > (they are biological replicates)
> > Array D : SSC15 -01
> > (just a sample without replicate)
> > Array E : TBVII control 01
> > Array F:  TBVII control 02
> > (They are technical replicate)
> >
> > I understand that this is a Bad experimental design but a group wants to do
> > have
> > some preliminary information to have from this.
> >
> > Kindly help me out to decide what sort of statistical test I can apply for
> > the
> > anlaysis. There is sample without any replicate. It would be of great help
> > since I am only a beginner for this type of analysis.
> >
>
>I would simply make a heatmap of the top 200 or so varied genes or those
>with the largest fold changes.  There are not meaningful statistics that can
>be done with an n of 1.
>
>Sean
>
>         [[alternative HTML version deleted]]
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111