Delta delta Ct method vs anova in RT-PCR data analysis

Question

[krishnapashu912]

I am new to RT-PCR data analysis and I hope someone can clarify my confusions as follows,

Following the literature, delta delta Ct method seems to be a common practice. For reference, the delta delta Ct steps are explained here - Computing fold change values for RT-PCR

Given that background, here is my question,

I have RT-PCR data with ~800 miRNAs. I want to do comparison between two groups (disease and control). Each group has 30 samples. I did global mean normalisation. That is, I normalised each Ct values from a sample to mean of all miRNAs in that sample. Boxplot of the data showed that distribution of Ct values across samples were different. I did between sample quantile normalisation. Now, after that I am planning to perform either limma based moderated t-scores or Mann-Whitney U test.

What is the difference between the approach I described above and delta delta Ct method? The approach I described seems simple and more intuitive to me, but I am worried whether I am missing some important point why delta delta Ct method is preferred.

I will appreciate for any clarification !

Answer 1

[Kevin Blighe]

There is nothing overtly incorrect about your approach, but can you elaborate on the exact formula that you used? - something like (for each mir):

Mean CT (across all samples) - CT (Sample1)
Mean CT (across all samples) - CT (Sample2)
Mean CT (across all samples) - CT (Sample3)
...
et cetera

I would not expect the distribution of these values on box-and-whisker plots to look good; nevertheless, you can still use these values for statistical comparisons but always use non-parametric. So, you should be employing Mann-Whitney t tests and a Kruskal-Wallis ANOVA..

The main article that you should be reading in relation to this was A novel and universal method for microRNA RT-qPCR data normalization.

-------------------------------------

There is neither anything wrong with the ΔΔCT method. It provides an intuitive metric (i.e. relative abundance as 2^(-ΔΔCT) ) for small panels of markers, or even when you just have one marker. For large panels, like you have, a different approach is needed in order to conduct statistical comparisons.

Kevin

[krishnapashu912]

Thank you Kevin!

I calculated ΔCT as follows, CT (Sample1) - Meant CT (across all mir in Sample1)

In words: from each mir in a sample, I substract mean across all mir in that sample.