[BioC] Taqman array analysis

[James Perkins]

Thanks everyone for your replies.

I was indeed inadvertently calculating deltaCt, but its great to know 
that what I was doing was correct!

I get differences in values for deltaCt using different control genes, 
but the trend is always the same and these changes are usually quite 
small, except for a few occasions where there seems to have been 
problems determining the value for Ct in some of the technical 
replicates. (so if GAPDH had a Ct value of ~24 cycles for 2 technical 
replicates, but was undetermined for 2 others). However I guess this is 
more of a problem with the technology and possibly the person running 
the card than with the choice of genes.

Regards,

Jim

Mark Cowley wrote:
> 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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>