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Fay and Wu test is a test that compare expected sequences under a standard coalescent theory model, that is a single panmictic population with non-overlapping generation of constant size $N$ and effective population $Ne=N$, that is the variance in allele frequency at any following generation is $\frac{p(1-p)}{2N}$, where $p$ is the frequency of a given ...


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The different models that can be used to fit the curves do differ. The linked paper and this website outline the differences; the discussion revolves around an R package called DRC but much of the information is generally applicable. Modelling can also be used to estimate ED50. The parameters and models that are being used need to be consistent with the ...


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Preprocessing will and should always depend upon the biology that you try to answer or discover (e.g.: There might be an experimental rationale to believe that some genes behave differently in individual samples - and that different samples could possibly have different distributions.) log-transforming your data by itself is usually no problem, and hugely ...


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From personal experience, nearly all count data whether from microarray or reads from RNAseq of some kind, requires a log transformation of the counts. Usually a small fraction is added to all values before doing so to zero protect. Log2(counts + 0.5) or some such. This is independent of the treatments. If you log transform one sample, you will do the same ...


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I am a bit wobbly on the subject, but I think the most important bit of information is that they are re-parametrising Wright's (1951) hierarchical analysis of variation, "F-statistics," "hierarchical partitioning of variation," or "population parameters," depending on whom you ask. The parameters correspond as follows (on the bottom of p.1358): Fit=F, Fis=f, ...



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