I am working with culture cells where one dish has been transfected with a scrambled knockdown clone and two dishes which have been transfected with two knockdown clones each knocking down the expression of a single gene.

An example of an experiment I have performed is to measure the mitochondrial membrane potential (using a fluorescent dye) in these cells using a confocal microscope.

This experiment was repeated on three independent occasions.

On each experimental day, the intensity of the laser which I used (the laser "gain") varies therefore I cannot combine all experimental days without expressing the dye intensity of each knockdown clone as a percent of the control "scrambled" clone (e.g. control = 100% mean intensity; knockdown clone 1 = 50% mean intensity).

Therefore, **I need to test for a difference in means between my control scrambled clone and each of the knockdown clones**, where my control scrambled clone is set to 100% dye intensity on each experimental day and my knockdown clones are normalised to this control. Therefore, my control has no variance (100% for all three experimental days) while my knockdown clones do have variance.

I know an ANOVA would not be feasible given the difference in variance. I will look into the procedure suggested by Michael Lew, but **would a t-test be unacceptable as well?** (I have seen papers using ANOVA and t-tests in these circumstances, but in spite of this I am assuming these should not be used). Thanks in advance.

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#### Best Answer

You are correct in assuming that you can't (shouldn't, really) analyse the data with the controls having zero variance.

It sounds like you should consider using a two-way ANOVA on the raw data with the within day variance accounted for in the manner of a paired test. I wrote about the approach in this paper that is intended for pharmacologists with little statistical background: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2042947/

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