I did an experiment where I administrated LPS in WT and KO animals. I wanted to know if the KOs have a different immune response than WTs. As targets I used several cytokines and complement system genes. I wanted to assess if the expression of certain cytokines correlates with the expression of the complement genes in each group. What value would be appropriate to use from my qPCR analysis to do this correlation? dCT? ddCT? I thought about using the dCT since the ddCT is "normalized" to the control group.
For the data presentation is recomended to use ddCT, using the wt as control, the result if is different to 1, means that something change in the ko. But for statistic analysis use the dCT
You have a 2x2 design, trying to evaluate the difference in the LPS-effect between KO and WT by measuring dCt values. The LPS effect in either genotype would be a ddCt value (difference of dCt values). The differential effect between KO and WT would be a dddCt value (the coefficient of the interaction of genotype and treatment; the difference of differences of dCt values). Hence, I'd look for the correlation of dddCt values. This should give you the correlation of the genotype-specific differences in the LPS effect.
You could alos correlate the ddCt values as the LPS-effects. However, these effects may or may not be genotype-specific (and this may not be what you are interested in).
Hi Danielle Santana Coelho
Yes, it is possible to correlate the expression of different targets in the same sample quantified by qPCR.
To perform correlation analysis, you can use the relative quantification method and calculate the delta-delta CT (ddCT) values for each target gene in your samples. ddCT compares the expression levels of each target gene to the expression levels of a reference gene, usually a housekeeping gene, and to a control group. The ddCT value can then be used to calculate the fold change in expression of the target gene between experimental groups.
To assess correlation between different targets, you can use the ddCT values and perform a correlation analysis such as Pearson's correlation coefficient or Spearman's rank correlation coefficient. These methods will allow you to determine if there is a significant correlation between the expression of different cytokines and complement system genes in each group.
It is also important to note that the choice of reference gene is critical for accurate relative quantification analysis. Make sure to use a reference gene that is stably expressed across all samples and that is appropriate for your specific experimental conditions.
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