Hey everyone,
I have the following situation:
Two groups of mice wildtype (WT) and knockout (KO) in each group I have lets say 10 mice. I isolate cells and treat the cells from each mouse with a basal or insulin condition (these treatments are done on the same cell suspension) and I measure glucose uptake as an outcome.
From previous data, I know that with increasing glucose uptake, which is induced by the insulin treatment the mean of glucose uptake increases and with increase in mean my variance increases. I choose to log-transform the data and I get approximately normal distribution and equality of variances. Is this reasonable to do?
Then I would like to carry out a statistical analysis. From my n=10 sample I can say very little about the distribution and assumptions of the model I want to use and I would therefore base my assumption about this sample on my previously observed findings of about 200 observations. Is this okay to do? Assuming the assumptions of a parametric test are fulfilled, how would you analyse this data?
As basal and insulin are from the same cell suspion, I assumed these to be paired/dependent. While the other measurement, so between WT and KO should be independent. In each group, so WT Bas, WT Ins, KO Bas, KO Ins I have 10 biological replicates. I was thinking about a repeated measures ANOVA. Could someone help here?
Is this reasonable to use log-transformed values?
Yes.
Is this okay to base assumption on previous experience?
Yes.
How would you analyse this data?
The values under the two treatment condisions (basal / insulin) are paired. You can calculate the differences of the (log)uptakes and analyze the mean difference of these between the genotypes (WT / KO) using a simple t-test.
Jochen Wilhelm Thanks for the reply. If I would want to also analyze the effect of insulin here (which is not interesting here, but assuming insulin would be some other interesting treatment) would it be reasonable to use a repeated measures ANOVA here, analyzing both the effect of insulin and the differences between genotypes?
Sure. But the effect of insulin is already encoded in the differences of the log(uptakes). You can test these values against zero if you like (e.g. in a model without intercept term).