Hello,
I have three different groups (Static/A/B) and I want to use delta delta Ct method to show my results in a log form. Previously I always had an untreated control to do the delta delta method but in this case I only have three cases with no other control and I have to compare these three in a graph.
In few papers I have seen that they show as" fold increase relative to control".. I don't know what exactly that means. Lets suppose if I use my static as a control (untreated, which it really is) then all my static genes would be zero if I show it on the graph. right? How can I show the static gene variation as well?
What's the best way to do the analysis knowing that I have ct valves for all the three groups.
Any help/guidance would be greatly appreciated.
Dear Sana, The book "Real-time PCR" by M. Tevfik Dorak (Ed.) can help you to analyse your data for relative quantification.
The another way to use software of your Real-Time PCR equipment especially LightCycler 480 software can calculate raw data for both basic relative quantification (delta delata Cp) and advanced relative quantification based on E2 method.
Your control group (untreated) used as a calibrator for both housekeeping gene and target gene. After normalization of data by software it can show X times of increase or decrease of target genes with respect to housekeeping gene. It indicates relative quantification.
Best regrads
You have three groups, S, A, B and you want make all possible pairwise comparisons, which are A-S, B-S and B-A. Consider the letters S, A and B denote the mean dCt values for each of the three groups, then A-S, B-S and B-A denote the ddCt values. So this is exacltly what you can do: calculate these ddCt values and show them in a diagram (ideally together with their condifence intervals).
There is another option: you can show the dCt values of the three groups, so the reader can compare their values between the groups visually. You can then show all the individual dCt (in a scatterplot; see linked image for an example) values so that the reader can see the numer of samples and the distribution of the values in each group.
Note: I strongly suggest to calculate dCt as Ct[reference gene] - Ct[target gene] so that higher dCt values indicate a higher expression (Livak and Schmittgen did this the other way around, what was a mistake, as it normalized the logs of the reciprocal concentrations; this mistake was corrected only in the final calculation of the fold-change, where they had to change the sign of the exponent again; my advice is to not repeat this mistake and do the calculation right from the beginning).
http://physiolgenomics.physiology.org/content/physiolgenomics/43/11/685/F6.large.jpg
Dear Sana Nasim,
you can read this very helpful guidebook. 4 and 5 sections of this book can help you.
Best regards,
Maxim
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