Hotzenplotz

Thanks for your answer. I completely agree that selecting the measure that makes it easiest for the reader to understand what I am trying to show is the best option. One thing that baffles me a bit, however, is that depending on which type of comparisons I chose I get different results in the statistical analysis. So, I might get statistically significant differences in some cases, but not in others. The question remains the same in all cases, i.e. "Are there (most likely) true differences between the various conditions?", but getting different results from the statistical analysis depending on which types of data I use (raw data or converted data) is confusing.

I have data of a cell migration assay that I need to evaluate. I have a number of replicates for each condition and the total number (#) of migrated cells for each replicate and condition (control vs treatment with different substances). Now, there are various ways to do the evaluation - I could do the following comparisons: * total # of migrated cells under the different conditions * % of migrated cells relative to total # of seeded cells * % increase in # of migrated cells vs control mean * fold change of # of migrated cells vs control mean Which type of evaluation would you favor and why?

Will the use of commercially available human serum cause an activation of lymphocytes isolated from human whole blood by Ficoll gradient centrifugation when used for the in vitro cultures? The commercially available serum will not be used during the lymphocyte isolation but only later for the in vitro culture. Here is more info on the human serum that I use for the lymphocyte cultures: http://www.paa.com/cell_culture_products/sera/human_sera/human_serum_offtheclot.html (Cat. No. C15-020) Of course, I can also use the autologous serum obtained from the whole blood but there are experimental reasons why this not possible for all my experimental set-ups.