[BioC] Taqman array analysis

[James Perkins]

Hi Bas,

Thanks for your reply. I have built an eset with detector as the rows 
and sample as the columns. However I have not been able to populate it 
with delta Ct since I do not have this data.

How did you calculate deltaCt? Using the proprietary software? I don't 
have access to this I have just been given the Ct and the Ct Avg for 
each detector.

I have been normalising each gene to the houskeeping genes, averaging 
across samples and dividing case by control to get the fold change. I've 
then been comparing the resultant fold changes depending on choice of 
normaliser against each other to see if there is a difference, which 
there is with *some* control genes.

Kind regards,

James

Bas Jansen wrote:
> Hi James:
>
> On Mon, Sep 1, 2008 at 1:25 PM, James Perkins
> <jperkins at biochem.ucl.ac.uk> wrote:
>   
>> Hi,
>>
>>
>> Apologies for the long list of questions, I have searched the mailing list
>> but can't find much info about these arrays.
>>
>>
>> I am looking at low density PCR cards. They measure the expression levels of
>> 96 different transcripts from a very small sample of human or animal tissue.
>> There are actually 384 reactions going on but in our case each is done in
>> quadruplicate (can be through biological or technical repetition).
>>
>> I wondered if there was a favoured way to normalise this data. The most
>> cited paper I have found is the Vandesompele 2002 paper using the geometric
>> mean of a number of control genes, implemented in R in the SLqPCR.
>>
>> Has anything else been developed that could be used with these cards? I
>> guess quantile normalisation is out of the question since this makes some
>> assumption that the majority of genes don't change in expression.
>>     
>
> As far as I know nothing has been developed in Bioconductor for these cards.
> When I analyzed them, I first created an ExpressionSet following the
> (excellent!) directions given in the the Biobase vignette 'An
> introduction to Bioconductor's ExpressionSet class' by Falcon et al.
> Then I processed the normalized data (deltaCt) using the LMGene
> package in order to perform gene-by-gene ANOVA and to identify
> differentially expressed genes. I have repeated the whole procedure
> using different control genes (read: different deltaCt values for the
> same gene), but in my case I got the same results with the different
> controls. Hope this helps.
>
> Kind regards,
> Bas
>