# Compare distributions (with replicates) and visualization

The experimental set-up is an almost typical control vs treatment, in which we are counting how many reads map to a particular class of genes in each condition (RNA-seq), done in triplicate. An example table would look like this:

feature  c1  c2  c3  t1  t2  t3
A     10  7   11  5   3   4
B     9  10   15  6   7   8
C     15  6   10  11  5   9
(.. up to several hundreds)


The question we are asking is:

"Is the distribution of counts in the control different from the treatment?".

Which would be the most appropriate test for this? From a previous question, an anova sounds like the way to go. Knowing R I could melt the data.table and run aov(Counts~Conditions, data). Would this be acceptable?

Extra points for suggestions on how to represent the data, including the replicates. Since there is more than one treatment, something like this, or boxplots for each replicate will become too cluttered.

• If you are dealing with count data you should probably use a GLM and not an ANOVA. – Roland May 28 '15 at 10:57
• Yes @Roland, it is count data. – fridaymeetssunday May 28 '15 at 10:59

glm(Counts~Condition+Categ, data,family=poisson)
• I don't understand why you'd use family = binomial. I'd expect that the poisson or quasipoisson families would be more appropriate. – Roland Jun 1 '15 at 13:27