Hi,
This seems a bit unusual to me, since I could not find any related paper. This is my situation:
We have CT values (3 replicates each) for following conditions for gene A:
- Wild type (WT) cells, treated.
- WT cells, untreated,
- Mutated (MUT) cells, treated,
- MUT cells, untreated.
We are interested to study the effect of a certain mutation (MUT) on the expression pattern of gene A in treated vs. untreated conditions. To do so, I simply calculated ddCT_WT as: dCT_WT_treated - dCT_WT_untreated, and similarly for MUT: ddCT_MUT: dCT_MUT_treated - dCT_MUT_untreated. Finally log2 fold change expression (FC) was calculated as: log2(ddCT_MUT/ddCT_WT). I am not sure if this approach makes sense or not; so, I appreciate if you can help me to better interpret/represent/analyse my results.
Any reference to similar conditions is highly appreciated!
Thanks!
You have two independent factors:
(1) GENOTYPE with levels "wild-type" and "mutated"
(2) TREATMENT with levels "no" and "yes" (or "untreated" and "treated")
Usually, such experiments are conducted to see if the presence of the mutation interferes with the response to the treatment. This question is adressed statistically by the interaction of the two factors in a two-factorial model.
Fisher RA. 1960. The design of experiments. New York: Hafner Publishing Company, Inc.
Box, G. E.; Hunter, W. G.; Hunter, J. S. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN 978-0-471-71813-0.
http://users.stat.umn.edu/~gary/book/fcdae.pdf
Hi there,
In principle your calculation will tell you if the fold change in the mutant is higher or lower than those in the WT. You may loose important information about significant of such a change though. If you have a bit of programming knowledge this is what I would do. You have 4 experiments and 3 replicates for each. Calculate your log2(ddCT_MUT/ddCT_WT) as you did and then for 1000 times randomly shuffle the values of the expression of A among all the 12 groups. Each time calculate the log2(ddCT_MUT/ddCT_WT) and store the result. You should get a distribution of values that you can use to compare with your initial one. If your initial value is higher (or lower) than 95% of the random values then there is probably a signal there. ........ just an idea!
Hi, You may perform geo2R for your relative gene expression in different condition
Jochen Wilhelm Thanks for your response!
I found my approach peculiar, but the rationale behind this was mainly due to the fact that we want to compare our data (in vitro) to differential gene expression analysis of RNA-Seq (i.e. FC) data from human samples (in vivo). So, we were trying to compare the differences in expression levels of some key genes (e.g. A, B,...) between in vitro and in vivo conditions. MUT in in vivo is patients carrying a certain mutation, and WT denotes the control patients. However, both WT and MUT in human samples share the same "disease". To resemble this criteria in vitro, I tried to first compare treated vs untreated (to simulate the "disease" condition) and then compare MUT vs. WT.
So, my questions are:
1- Do you think I can follow this approach but supplement it with statistical analysis (e.g. two-sample t-test or two-way ANOVA) to examine the difference between WT and MUT (to resemble the differential expression analysis coming from RAN-Seq)?
2- Or, should I change my approach (if two-factorial justifies my criteria)?
Thanks for your patience!
What I would do in this case: analyse the qPCR data and the NGS data just in the same way, that is, detemine the coefficient for the genotype:treatment interaction (and its confidence interval, ideally), and show the correlation between the data across the genes (scatterplot!).
The problem: the method (qPCR and NGS) and the assay (in vitro and in vivo) are perfectly confounded. Strictly speaking, this means that you are unable to attribute any difference you may find to the method or to the assay. In the worst case you simply don't see much of a correlation, and then you are left with nothing, as you don't know if this difference is due to the method or due to the assay. If you have genes that are known to have the same regulation (more or less) in both assays, you can use thier data to see if both methods will give comperabel data. You can use the regulation by the mutation as well as by the treatment, and you can estimate a lower bound for the error in the interaction.
If all that sounds a bit too complicated, I suggest discussing this on-site with a local statistician.
Jochen Wilhelm Thanks for providing details on this case! Actually, it is of much help.
Many thanks!
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