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) > > > ----------------------------------------------------------- > Il Contenuto del presente messaggio potrebbe contenere informazioni > confidenziali a favore dei > soli destinatari del messaggio stesso. Qualora riceviate per errore questo > messaggio siete pregati > di cancellarlo dalla memoria del computer e di contattare i numeri sopra > indicati. Ogni utilizzo o > ritrasmissione dei contenuti del messaggio da parte di soggetti diversi > dai destinatari ? da > considerarsi vietato ed abusivo. > > The information transmitted is intended only for the p...{{dropped:14}}

Thanks Heidi - that is a comprehensive answer indeed and clears to us any doubts ! Agian, a very happy 2011 from a very satisfied Italian HTqPCR user ;-) Alessandro -- 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) ----------------------------------------------------------- Il Contenuto del presente messaggio potrebbe contenere informazioni confidenziali a favore dei soli destinatari del messaggio stesso. Qualora riceviate per errore questo messaggio siete pregati di cancellarlo dalla memoria del computer e di contattare i numeri sopra indicati. Ogni utilizzo o ritrasmissione dei contenuti del messaggio da parte di soggetti diversi dai destinatari ? da considerarsi vietato ed abusivo. The information transmitted is intended only for the per...{{dropped:8}}

On 1/19/2011 5:40 AM, Heidi Dvinge wrote: > 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. In my experience, it is usually better to use the SDS software to set *manual* baseline and threshold values. Moreover, you will often need to set different baselines for different probes. (For example, if you use ribosomal 18S as one of your control genes, the huge quantities of 18S in the samples require a very early baseline, perhaps as small as 1-3 cycles, and the interesting signal will often come off around 8 or 9 cycles. Many other genes will give better results with a baseline of around 3-12 cycles, with typical signals around 20 or more cycles. Even with the same baselines, you may need to use slightly different thresholds.) The "quality" of the parameter settings, unfortunately, is typically assessed visually. You'd like to see a pretty clear logistic curve, parallel for most samples, with the threshold cutting through the "linear" phase. (I'd be a lot happier if I had some quantitative way to assess the results.) One advantage of setting the parameter values manually instead of using the "automatic" setting in the SDS software is that the actual values used for the baseline and threshold will be available in the resulting data set, just in case you want to refer to them at some point in the analysis. If you instead set it to "automatic", then the software does not report which values it chose for which probes. I also find that you are better off quantifying as many of the plates (or cards if you are using ABI's "low density arrays") at once as the SDS software will allow, since that helps ensure that you use the same settings. > 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? I spent some time playing with the raw data. In part, I wanted to see what happened if you fit actual logistic models that account for the fact that some probe-primer pairs are probe limited and other are primer-limited. I decided that the basic model-fitting was good enough, in that accounting for these extra complications didn't seem to have any real payoff in the kinds of inferences one wanted to draw from the data. None of this was published (since it's rather hard to publish something that says "this more detailed model with more parameters has no advantages over the current model for this kind of data"), and I'm not sure if I can even find the actual computations any more.... > HTH > \Heidi > >> Kind regards, >> >> Alessandro G.