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Ct variation/Ct limit/rē values/efficiency errors


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#1 wozzels

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Posted 26 February 2010 - 04:45 AM

Hey guys, I have some important questions in order to accurately evaluate some real-time data from students in my lab. Here they go:

If analyzing real-time pcr data, I was wondering about these important points:

1) Suppose you have triplicates for each sample/standard: the Ct-values of these three replicates are never exactly the same, but which degree of variation is allowed and should you remove wells of which the Ct-value is very different from the other 2? Is the cut-off around Ct +/- 0.15? And are data with Ct +/- 1.0 reliable or not at all?

2) If you read data or have wells which have Ct-values of more than 35, are these data or wells usable or is 35 the maximum allowed Ct-value? (I thought more than 35 was comparable to the baseline threshold)

3)When real-time data have correlation coefficients (rē) of 0.91-0.95, are these data reliable? (I thought data should have at least 0.98 rē)

4) If you have data with efficiencies of more than 100 % (standard curve slope > -3.33, for example -2.90), are these data reliable? Is the > 100 % efficiency due to improper settings of the threshold/baseline or due to pipetting mistakes?

If you have answers to any of these questions, that would be extremely helpful!

Thank you in advance and greetings!!!
Wozzels

#2 Vini

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Posted 26 February 2010 - 07:04 AM

Hey guys, I have some important questions in order to accurately evaluate some real-time data from students in my lab. Here they go:

If analyzing real-time pcr data, I was wondering about these important points:

1) Suppose you have triplicates for each sample/standard: the Ct-values of these three replicates are never exactly the same, but which degree of variation is allowed and should you remove wells of which the Ct-value is very different from the other 2? Is the cut-off around Ct +/- 0.15? And are data with Ct +/- 1.0 reliable or not at all?

2) If you read data or have wells which have Ct-values of more than 35, are these data or wells usable or is 35 the maximum allowed Ct-value? (I thought more than 35 was comparable to the baseline threshold)

3)When real-time data have correlation coefficients (rē) of 0.91-0.95, are these data reliable? (I thought data should have at least 0.98 rē)

4) If you have data with efficiencies of more than 100 % (standard curve slope > -3.33, for example -2.90), are these data reliable? Is the > 100 % efficiency due to improper settings of the threshold/baseline or due to pipetting mistakes?


If you have answers to any of these questions, that would be extremely helpful!

Thank you in advance and greetings!!!
Wozzels



Hi Wozzels,

I might be able to answer ur first 2 questions.....

1) triplicates shpuld ideally be the almost same........with Taqman, a variation of +/- 0.5 is considered goos, whereas for Sybr green...+/- 0.75. 1.0 is too high..........if its a triplicate set frm the same naster mix, then, of course, u should suspect pipetting errors, or primer dimer formation.....

2) n i think u shud discard the data with Ct values higher than 35...


3) about r value, i would myself like to have an answer... :)

#3 ivanbio

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Posted 27 February 2010 - 12:18 AM

Here are my two cents:

1. You should never remove replicates unless it is clear that they are outliers. A Ct difference of 0.15 is not an outlier, while a difference of >1.0 could be an outlier depending on how tights your other readings are.

2. It is perfectly fine to use a Ct value of 35 if you run 45 to 50 PCR cycles. The only reason why you would not use a Ct value of 35 is if you only run 40 PCR cycles. This is because you cannot really tell if the amplification is real when you only have 5 PCR cycles beyond the point where the Ct is calculated.

3. Any qPCR run with an r2 lower than 0.98 is for all intended purposes useless (not reproducible/tight enough). In my experience I've never used any qPCR assay that gave me an r2 lower than 0.99, and I have always strived for an r2 higher than 0.995. In short, an r2 higher than 0.98 is ok, although sloppy, an r2 of 0.99 is better, and an r2 >0.995 is good technique.

4. Data with efficiencies higher than 100% are rare, but possible, and they are likely not due to threshold/baseline settings (although if done incorrectly, settings the threshold/baseline manually can give you all sorts of weird results). There are many ways you can get efficiencies higher than 100%, but the most common is the presence of an inhibitor in your sample.

My recommendation: test different qPCR assays, for the same gene, to find the one that gives you the best results (highest r2, closest to 100% efficiency, etc), and only then use it to analyze your samples.

Ivan
Carlsbad, CA




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