**0**wrote:

Dear Bioconductor community,

I have a RNA-seq dataset consisting of 16 samples. Two different genotypes are expected to show differences with respect to a treatment (4x4 samples). Half of the mice have not been treated and half of them treated with an additive to the food. Because of the different amount the individual mice eat during the day the dose of the treatment per day and unit body weight varies widely. I know that it is possible to build a quantitative model so that the treatment is a real value and the treatment effect will be expressed relative to one unit of treatment dose. Then - if I am not mistaken - there cannot be groups treated-untreated anymore and there will be no log2FC between them. Most lab people like to have fold change values. My questions:

1. Is it possible to stick with theĀ treated v. untreated groups here but get a clearer picture of the effect by using the dose as a contiuous covariate? Or phrased somewhat differently: Could the contiuous covariate be introduced only for the treated animals, or for all but equal to 0 for all untreated animals e.g. as a difference to the mean dose within the group (0 for all untreated and a real number for all the treated animals)?

2. Is there a standard way to decide whether to use a contiuous covariate or binning to generate a factor or no covariate at all? What measure could be used to justify this choice and the number of bins when bins are preferable?

Any comments on the approach in general and how to actually analyze this design in DESeq2 or edgeR are most welcome. Thanks for your support and keep up the fantastic work,

Jacob.