[BioC] HTqPCR questions

[Heidi Dvinge]

Dear Alessandro,

a long overdue answer...

> Hi Heidi ! My name is Alessandro and i am a bioinformatician in a small
> genome services company in Italy.
>
> We tested HTqpCR in a set of 20 ABI 7900 qPCR experiments with interesting
> results (hence thank you so much for these libraries!!) but I do have a
> couple of
> questions for you:
>
> - the library needs in input SDS output files, which contain the Ct
> values called by the SDS software,

Well, it just needs any sort of Ct values really. Many people get the
output in SDS format, which is why I added an option for inputting these
files directly. Technically, it can be any sort of test file, as long as
it contains one ID-Ct value pair per row.

> Now, it is possible to get these values automatically from SDS or
> manually, trying to consider global
> baseline and threshold settings for all the experiments. In your
> experience, which is the most common
> strategy for such kind of experiments which I would define medium scale ?

I'm afraid I'm not 100% sure what you're asking here. It's been a long
time since I tried playing with the SDS software. As far as I'm aware,
most people tend to use the default settings for calculating the Ct
values, but you can certainly adjust the baseline etc. within SDS if you
ahve any reason for wanting to do so. As long as you keep the settings
consistent across multiple plates you should be fine.

Note though that this is not taken into account within HTqPCR. The package
accepts whatever Ct values are provided as input as being "true". You can
normalise for e.g. plate and batch effects, however there are no functions
that will do any adjustment that depend on the initial parameters used for
obtaining the Ct values. However, some of the normalisation methods are
non-linear, i.e. they don't just subtract a constant from all Ct values,
as the classical deltaCt method does. Hence, if you're not happy with the
SDS software applies global settings to all samples within a plate, the
normalisation can (at least partly) correct for that.

I know that some people generally don't trust commercial software much,
and prefer to get all the raw data, i.e. all the individual fluorescence
measurements, and then fit some sort of sigmoid curve manually. However, I
ahve yet to see any conclusive evidence that this is really necessary, and
especially worth the extra effort. Perhaps other disagree with me here?
>
> - t-test & Mann-Whitney: there is no need to correct for multiple
> comparisons ? i have only tow samples here, though.
>
In principle, it's always good to correct for multiple comparisons, to
limit the number of false hits you get. However, the correction is
typically much less stringent than what you'd see for e.g. microarray
experiments: less genes to begin with means less tests, and hence less
correction.

Depends on what you want. If these are just initial experiments done in
the lab to screen for interesting genes to follow up on (and especially if
you don't have many samples), then you can also choose to just take the
top X most significant, even if they don't have p<0.01. However, if these
are validation studies you'd want to be more stringent about what you
consider significant.

> - I found many more significant variations (miRNAs) with your software
> than with an Integromics trial (I have two
> time points for ten samples). Do you have any experience on such kind of
> comparisons ?
>
I tried Integromics initially, but found it a bit inflexible to work with
(which BTW is why I wrote HTqPCR in the first place). I guess the number
of significant variations depends on your samples. Does it make biological
sense to see large changes? 20 samples is quite a bit for this kind of
qPCR experiments, so you should get some rather robust statistics.

If the fold changes of the results vary a lot between Integromics and
HTqPCR, it could because of different normalisation methods, or depend on
how many potentially unreliable Ct values you filter out.

You could also try to look at the rank correlation between Integromics and
HTqPCR. If you rank all genes based on their p-values, it the order then
roughly the same (high spearman rank correlation)? Or is the order
completely jumbled? For genes with many unreliable Ct values (35-40) there
are likely to be some differences, however for the genes present at higher
levels, it might just be a case of the p-values shifting slightly up or
down.

HTH
\Heidi

> Kind regards,
>
> Alessandro G.
>
>
> --
>
> Alessandro Guffanti - Bioinformatics, Genomnia srl
>   Via Nerviano, 31 - 20020 Lainate, Milano, Italy
>      Ph: +39-0293305.702 Fax: +39-0293305.777
>              http://www.genomnia.com
> "Keep moving forward!" (Wilbur, Meet The Robinsons)
>
>
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