A t-test on the delta-delta-Ct values is reasonable.  However if you have
many more genes than samples, you might instead want to use the modified
t-test implemented in the limma Bioconductor package. Follow its
documentation for "single channel designs" and note that lmFit() will also
accept matrix objects if you prefer not to create an ExpressionSet object.

-Levi



On Tue, Nov 12, 2013 at 1:10 PM, alessandro.guffanti@genomnia.com <
alessandro.guffanti@genomnia.com> wrote:

>  Thanks a lot Levi! this is enlightening and we will use your function
> on our datasets, we will let you know how this goes.
>
> Just a final question, excuse me the sheer ignorance: if I want to
> associate
> a statistics to each final value (which is indeed the big added vakue of
> HTqPCR),
> of course in the case when I am a comparing a first group of samples vs a
> second
> group of samples, which could be a reasonable approach ? a simple rowwise
> t-test ?
>
> Keep in touch
>
> Alessandro
>
>
> On 11/12/2013 7:04 PM, Levi Waldron wrote:
>
> Dear Alessandro & Elena,
>
> I have not used this package but have used the kinds of qPCR
> normalization methods you are talking about and can offer some
> comment, below.
>
> On Mon, Nov 11, 2013 at 8:17 AM, alessandro.guffanti@genomnia.com<alessandro.guffanti@genomnia.com> <alessandro.guffanti@genomnia.com> wrote:
>
>  Now, this was our initial doubt and still persists: why a normalization
> method which should operate an average Ct value for each sample, and
> scales all Ct values according to the ratio of these mean Ct values
> across samples, should take as a reference one of the samples (the first
> in the data matrix by default) and leave its values untouched ?
>
>  I think that using average Ct value and a reference sample for HT
> qRT-PCR should work as follows, if you assume 100% efficiency for all
> primers:
>
> 1) for each sample, subtract the mean Ct value from the Ct value of each gene.
> 2) for each gene, subtract the Ct value of that gene in the reference
> sample.  delta-delta-Ct for the reference sample is then zero for all
> genes.
>
> This is the method of Pfaffl 2001 (PMID 11328886) but with mean Ct
> value for the sample replacing use of a housekeeping gene.  I wrote a
> function a few years ago to do this, with options to specify the
> efficiency of each primer or use one or more housekeeping genes, and
> to specify one or more control samples (in which case the mean delta
> Ct of these samples is used as reference, so that delta-delta-Ct
> values for each case sample can be directly interpreted as log2(FC) ).
>  My function isn't packaged or documented, but if it might be useful
> to you I'd be glad to help.
>
>
>  ==> *As far as scaling ranking is concerned, we noticed that even if we
> specified the reference as pseudo mean or median, apparently nothing was
> changed in term of scaling factor and reference column (always the first
> sample by default). Is it correct? Where the normalization is influenced
> by the reference?
>
> In general, which is the logic of selecting one sample as a 'reference'
> in the normalization step with these methods ?
>
>  I think it is the same logic as described by Pfaffl.  Delta-delta-Ct
> values can be interpreted directly as log2(Fold Change) relative to
> the reference sample.  If you have primers with efficiencies other
> than 100%, then it also corrects for differences in the number of
> amplification cycles for a gene between samples.
>
> Hope this helps,
> Levi
>
>
>
>
> --
>
> Alessandro Guffanti
>
> Head, Bioinformatics
>
> *Genomnia srl*
>
> Via Nerviano, 31/B – 20020 Lainate (MI)
>
> Tel. +39-0293305.702 / Fax +39-0293305.777
>
> www.genomnia.com
>
> alessandro.guffanti@genomnia.com
>
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>
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