I have RT-qPCR data obtained from an experiment of gene expression. Now I have four dCTs for each sample (untreated and treated groups( GOI & HK)). of course tests are done in triplicate(n=3). Iv seen both Fold Change and dCTs are used for Statistical analysis. the question is which data and how they should be used?
If we must use dCT, which of them considers both GOI and HK gene?
You received no answer yet, perhaps because few people with a statistics background have understood your question. Perhaps showing a part of your sample data can help. You mentioned that you have "seen both Fold Change and dCTs are used for Statistical analysis". Where ? How was it done ? Good luck !
Take a look at this article that explain how to do the calculation and normalization using dCT or ddCT methods for RT-qPCR.
Ahmad Amiri you had used relative quantification technique in qRT-PCR data analysis. i would suggest you to apply statistical technique for ddCT values
*don't apply stat tool on fold change. since, it varies your stat tool value.
for further, use two-tailed paired T-test for analyzing treated and non-treated pops or groups of genes independently..
Ahmad Amiri
Follow these manual steps in Excel and SPSS
if having one target gene:
1- You should have 1 dCT for each sample. Perform average function for repetitions. (Excel)
2- Calculate -ddCT in which the other delta is the mean dCT of the control group. The mean -ddCT of the control group must become 0. (Excel)
3- Perform independent t test to compare -ddCT of the groups. (SPSS)
4- Design column plot with 95% CI as custom plot. The control group must not have column because its mean was 0. (SPSS)
5- Change y axis labels as fold change. i.e. Change 0 to 1, 1 to 2, 2 to 4, 3 to 8,.... If you cannot do it in SPSS you can change them in paint! (SPSS)
6- Calculate FC for mean, lower limit of CI and upper limit of CI to report in manuscript text or tables. (Excel)
If you have more target genes, statistical analysis is same, however for plotting you can: 1- Perform the above steps and design separate plots for each gene.
2- Or design a single plot of all genes as bellow:
- In excel, put -ddCTs of all genes in one column just for treated group. In the next column add grouping numbers (1: gene 1, 2: gene 2, ...).
- Copy and paste these 2 columns in SPSS.
- Design custom table as columns with 95 CI error bars.
- Change the labels of y axis.
- There is no control group in this plot. Add reference line of y=0 (or FC=1) as representative of the control group.
- You can perform one sample t test instead of independent t test. The reference value should be -ddCT=0.
i hope, it may help you (excel tool for qRT-PCR analysis, condition-relative gene expression)
10.13140/RG.2.2.10557.18405
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