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My data consists in two samples relative to the expression of a given gene, with 4 replications in both samples.

I am interested in testing whether the two samples come from the same population, taking into account the fact that I have repeated measures by design.

I've been reading about different non-parametric approaches and the Friedman's test seems to be one option. However, given the extremely low sample size, I am not quite sure that this test can be adopted.

Can you suggest if I can proceed this way? Can/should other approaches be adopted?

EDIT

alternatively, would any exact test would be ok for my specific need?

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In my case I analysed the results about the abundance of the genes (qPCR) and when the my dataset was not normal ditribution, I applied the Kruskal-Wallis test and if I saw a significant different apply the pairwise.wilcox.test. I hope to help you.

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  • $\begingroup$ thanks @Giorgia, which sample size would be ok for it (taking into account that I have 4+4 repeated meauses per gene)? $\endgroup$ – Stefano Lombardi Jun 30 '15 at 9:16
  • $\begingroup$ In my case I have 4 replicate for each treatment, for each time point. In total I have 10 treatments and 6 time points. Do you have differente time to sampling or not? $\endgroup$ – Giorgia Jun 30 '15 at 12:47
  • $\begingroup$ I need a little more information about your dataset. $\endgroup$ – Giorgia Jun 30 '15 at 12:53
  • $\begingroup$ thanks, in my case I have two datasets relative to the same set of genes, and 4 replicates for each treatment. So, basically 2 treatments x 4 time points. $\endgroup$ – Stefano Lombardi Jun 30 '15 at 20:51
  • $\begingroup$ You create a table with the treatment, time and abundance of your gene. In this table you put the control and the treatment. You create this same table for each treatment. After you save the table in .csv and you import by R. You calculate the Shapiro test and in function of this you apply the ANOVA or the Kruskal-Wallis test. If the first time you have a non-normality distribution you try to transform the data. $\endgroup$ – Giorgia Jul 1 '15 at 6:44

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