[BioC] HTqPCR to analyze Fluidigm 96.96 Dynamic Array data

V. Oostra v.oostra at biology.leidenuniv.nl
Fri Jun 18 13:21:57 CEST 2010

Hello Heidi,

Thanks a lot for your heplful comments. I followed your suggestions
regarding the re-structuring of the data, and am now doing some basic
data exploration. I will give an update once I know a bit more, but I do
have some questions already in relation to your comments.

> > Is this a good way to structure my data? Or would it be better to
> 9
> > qPCRset objects (1 for each plate)? Before spending more effort
> continuing
> > this approach I'd appreciate your opinion on whether this is the way
> > forward.
> >
> It depends a bit on how clean your data is, and how you want to
> it. If you suspect there are any array-specific effects at all, you'll
> probably want to normalise your 9 plates separately, i.e. have them in
> qPCRset object with 96x96 rows and 9 columns.
> Do you have your data in a single or 9 files? Either way, you can
> such a qPCRset. Or possibly, if you want to use the object you already
> have loaded into R, you can split it up using something like this
> (untested, and unelegant):

I had my original data in 9 files, but I imported them into one qPCRset
object (96 features, 864 samples). Using your code I splitted it up
again and I found the array-specific effects--which I indeed suspected:
I had run the same dilution series on all arrays, so those samples
should have similar Ct values across arrays. However, when I plotted
them (for each gene separately) against array I found some arrays that
were consistently higher or lower than the others. My idea is to use
this dilution series to correct for array-specific effects. Note that
I'm not talking about control genes here, but 'control' samples: the
same samples ran for all genes on all 9 arrays.
It never occurred to me to use different types of data structuring for
different parts of the analysis (data eploration, QC, etc). A very good

> A bit of data exploration is probably required to check whether you
> any particular biases that needs correcting in your data. Based on the
> qPCRset object you have now, you can e.g. try clustering your data
> clusterCt(), and see if the samples, especially the controls, cluster
> together by sample type or based on what array they were run on. Also,
> what's the correlation between samples like (plotCtCor)?

Actually, this didn't work for me, neither in my old or new data
structure. Where can I add information on types of samples? E.g. with in
'Feature type' I can put info on each gene (reference, test). But is
where can I put info on each sample? E.g. positive control sample,
negative control, etc.? In a data frame separate from the qPCRset?


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