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2^-ddCT method for multiple genes


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#1 Joe77

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Posted 02 November 2009 - 08:55 AM

Hi guys,

I am new at ddCT calculation method.

I want to compare the fold expression change in 4 genes I am interested in. I have 2 time-points at which the RNA was extracted and mRNA of these genes were measured plus an endogenous mRNA. I have the CT numbers of each gene. I would like to compare each gene at different time points to gene#1 at time point#1. How to do the calculation.

I am using the following formula:

2^-ddCT = 2^-((CT_target - CT_endo)_time2 - (CT_target - CT_endo)_time1)

Any help?

Thanks

#2 Trof

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Posted 03 November 2009 - 09:24 AM

I don't think you can compare one gene to a different one, you only can compare to first timepoint of each gene to tell the relative fold change in expression.

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#3 anfernee

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Posted 17 November 2009 - 04:29 AM

Hi check this paper if you haven't read it.

"Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 22DDCT Method"

As long as your PCR efficiency is similar between one gene and another,I can not see the point why you are not able to compare them. Or you can do absolute quantification to compare rather than delta delta Ct method.

Good luck!:huh:








Hi guys,

I am new at ddCT calculation method.

I want to compare the fold expression change in 4 genes I am interested in. I have 2 time-points at which the RNA was extracted and mRNA of these genes were measured plus an endogenous mRNA. I have the CT numbers of each gene. I would like to compare each gene at different time points to gene#1 at time point#1. How to do the calculation.

I am using the following formula:

2^-ddCT = 2^-((CT_target - CT_endo)_time2 - (CT_target - CT_endo)_time1)

Any help?

Thanks



#4 fishdoc

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Posted 17 November 2009 - 07:18 AM

I am not sure of the validity of this method of calculation, but I have seen it done in a paper before. With the ddCt method, you typically have an endogenous gene and a calibrator, which is normally a null treatment. In that method of calculation, you really can't compare the expression of one gene to another, because it's fold expression that's calculate, not overall expression. You may be able to say that geneA has a greater increase in expression than geneB, but you cannot compare the level of expression to say geneA > geneB or vice versa.

The method I saw will allow you to compare the expression of genes within ONE treatment to other genes in the same treatment. However, it cannot be used to compare gene expression between treatments, i.e., you cannot compare geneB at time 2 to geneA or geneB at time 1. What you do is instead of setting up your calibrator as a treatment, you set it up as the lowest expressing measurable gene in that treatment. So if you had 5 genes that had Ct values of 15, 20, 22, 25, and 30, you would set the gene with the Ct of 30 as the calibrator. What you then get is the relative expression of each gene in that treatment to one other gene in that treatment, but only that treatment. Because it's possible for that gene to vary in expression in other conditions, you cannot compare across treatments. It's not an absolute amount of expression, but you're able to say geneA has X fold expression over geneZ in this treatment, whereas geneB has Y fold expression over geneZ.

As I said, I've seen this method in a paper, but I am not sure of it's validity for use, as in whether or not it's a proper way to measure expression. Some here may have input towards that.

#5 Christo K

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Posted 17 September 2012 - 05:55 AM

Hi,
as far I can see your formula it is ok; anyway following you have step by step what you can do

first you calculate the

ΔCt= Ct (gene)- Ct (refference) where the refference it is the endojenous control, gene that you use for normalization in each sample.
the SD of this it is calculated by SD=(s1^2+s2^2)^0.5 where s1 the sd of gene and s2 the sd of reference gene

here you can normalize the expression if the gene1 in the A cells or tissu with the expression of the same gene in cell B or tissue B
by which the cellB or the tissue B it is your refference

ΔΔCt=ΔCt1-ΔCt2
the SD of ΔΔCt it is the same of the previous

then all the data can be shown as Log. with the formula 2^-ΔΔCt




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