Dear All,
As an assay output we measure expression of one gene per well in a 96 well cell culture plate at different time points/conditions. Eventually, what we have is a matrix (genes x conditions) of gene expression values and we compute the fold change when compared to the control condition. We have one control per condition.
Are there any other better normalization strategies for such experiments?
Is it valid to visualize this data by computing Z-Scores? I have this question for the following reasons:
- because the Z Score is computed on Fold Change which is essentially a positive value.
- unlike RNA Seq the expression of each gene is measured individually (more like qRT PCR for each gene).
PS: If I complicated the experiment design (above) for , then to simplify, I have qRT PCR measurements for many genes in different conditions. Essentially this is expression values and my question is about how to normalize and visualize this data? Computing Fold Change from Control and then applying Z Score... Is it valid?
Suggestions welcome! Thank you very much!
Are you just visualizing this data or are you going to try to do anything else with it (in its transformed form). BTW, have you considered taking the log2(fold-change) instead? That would be better for visualization (and probably transformation too).
Visualization is not the key point, though that is one of the objective. The key thing is we need to normalize the data to be able to compare them against each other. This is something I feel would be correct to do, before concluding something biologically.
Why don't you just directly compare the fold-changes (or log2 of them)? That would seem simpler. My guess is that you have samples across plates and don't have anything constant on a plate to standardize against, in which case you're pretty much screwed. It's a bit difficult to give you any really good advice without actually knowing how the experiment was done and the exact comparisons you want to make (with information about how things were split over plates included). Don't worry about that being "too complicated", some of us have done enough molecular biology in our lives that that's not a problem.