[BioC] Taqman array analysis

[Sean Davis]

On Thu, Sep 4, 2008 at 6:05 AM, James Perkins
<jperkins at biochem.ucl.ac.uk> 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.

There are a number of references for this (quick google search will
turn up several).  But here is one that is pretty clear, I think.

Livak, K. J. and Schmittgen, T. D. (2001). Analysis of relative gene
expression data using real-time quantitative PCR and the 2(-Delta
Delta C(T)) Method. Methods 25, 402-8.
http://www.ncbi.nlm.nih.gov/pubmed/11846609

Sean

> 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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