Relative qPCR dCt analysis - Different ways to analyze data? (Jan/05/2010 )
I've done a lot of qPCR to look at different effects of media conditions on the expression of genes in bacteria. In this analysis, I used 16s as the endogenous gene and standard minimal media as the calibrator for relative quantitation of message. That analysis allowed me to compare the expression of target genes across various media conditions. My PI now wants to try to compare the expression of genes within a particular medium. In other words, I initially compared expression of gene X in condition 1 to gene X in condition 2 to gene X in condition 3 and so on. He now wants to try to compare gene X to gene Y to gene Z all in condition 1. Is there a valid way to use my Ct data to do that, and is it an accepted method of gene expression comparison?
My thoughts were that the dCt values for each gene in the particular media could be compared, but I'm not really sure if that tells me what I think it's telling me. For instance, if gene X has a Ct of 20, gene Y has a Ct of 25, and the 16s has a Ct of 12, then the dCt of gene X is 8 and the dCt of gene Y is 13.
Is it that simple then to say that gene X is expressed at a higher rate in that condition at that time than gene Y?
Obviously if I do each of those analyses in triplicate, I can do statistical analysis and determine if the dCts are significantly different from each other, but is that a valid use of relative qPCR data?
Beyond that, can the 2^ddCt calculation be done, with substitution of dCt for ddCt, to determine how much more gene X is present than gene Y?
I was quite hesitant to even think about doing this sort of analysis of the data, because it seemed like a bastardization of the ddCt method, but I don't have any information yet on why it wouldn't be acceptable, assuming I'm not drawing anything more from it than a "more or less" type of comparison between the two genes.
Any help would be appreciated.
Yuan (2006) has reported different ways to analyze expression. As a linear model for Ct, considering variability among technical replicates, genes and samples and estimating ddCt; as an ancova model for Ct, where includes sample concentration as a covariable, and also estimates ddCt; and the simple t-test or wilcoxon test for comparing dCt, and estimating ddCt (depending if your data seem to come from a normal distribution. The 2008 paper explains it a little bit more.