[BioC] Taqman array analysis

[Mark Cowley]

Hi James,
by normalising to housekeeper genes, you have probably inadvertently  
calculated deltaCt.
There are some other great references by Pfaffl and Bustin on the  
subject

cheers,
Mark

-----------------------------------------------------
Mark Cowley, BSc (Bioinformatics)(Hons)

Peter Wills Bioinformatics Centre
Garvan Institute of Medical Research, Sydney, Australia
-----------------------------------------------------

On 04/09/2008, at 8:05 PM, James Perkins wrote:

> 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
>>
>
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