Im working on a project involving adipose tissue from 10 different locations from 30 individuals, which equals 300 biopsies.

The biopsies has been analysed in regard to "adipocyte count" and "area pr adipocyte" using stereology and digital image processing. The minimum count of adipocyte pr biopsy is 154 and the maximum count is 852 adipocytes, hence the 300 biopsies do not have the same adipocyte count - each adipocyte has an area and diameter meaning that the mean of each biopsy do not build on the same number of observations as the mean of another biopsy.

The data does not fit a normal distribution and transformation is not possible (still not a normal distribution), the data is right skewed.

We wish to compare the adipocytes of the different locations and see if there is a difference in adipocyte size (area).

We are disgussing how to handle the data and how to make the comparison and we can't seem to find an agreement.

Should the data be compared using Mann-Whitney (non-parametric test for unpaired data) or Wilcoxon (non-parametric test for paired data)? (some say the data is paired and some say it is not)

Another approach suggested is: Should the means of the biopsies be compared using a student t-test? (comparing the means does not seem like the right option when the mean does not represent non-parametric data so well)

Hoping for some input!

Thanks.