How to deal with non-normal heterocedastic data from a factorial experiment? I ran an experiment with two factors each with two levels, 5 replicates each combination and one response variable. My data are non-normal and heterocedastic. Transformations didn't help. I ran a permutation ANOVA (aovp - package lmperm - R), but I am not sure this is the right way to go and I don't know what to do about a post hoc test, because I have a significant interaction. This is what my data look like:  
F1 F2 BIOMASS
A C 0.66
A C 0.31
A C 0.88
A C 0.55
A C 0.81
A D 0.39
A D 0.21
A D 0.17
A D 0.15
A D 0.18
B C 0.00
B C 0.18
B C 0.05
B C 0.00
B C 0.02
B D 0.16
B D 0.04
B D 0.21
B D 0.15
B D 0.06

 A: Your response variable is suspicious.  If it is a continuous proportion, you may want to pursue beta regression (I have an example of a BR in R here: Remove effect of factor on continuous proportion data using regression in R.  If it is the outcome of some number of Bernoulli trials (and you know the number), you should disaggregate and use logistic regression (or use a weighted LR).  The following assumes the data are simply from a continuous variable, and happen to be within the (0, 1) interval. 
I think most of this may be covered by my answer here: Alternatives to one-way ANOVA for heteroscedastic data.  In general, permutation tests are not recommended when you have heteroscedasticity.  In your case, I might suggest using ordinal logistic regression.  Since you have a 2x2, the post-hoc tests are just two 2-group comparisons, which could be done by repeating the original analysis on the stratified data.  Under the assumption that the global test of the initial model is significant, I wouldn't even worry about multiple comparisons.  
