Hi,

This might be a quite basic question, but has been troubling me for a while. How is fitting a negative binomial glm in R different from edgeR DE analysis. I think edgeR allows one to model a different variance for all genes, which is not feasible in the standard glm.nb function in R. Other than this, do they both use the same model? I have drug response data for cases and controls and would like to estimate the effect of drug as well as the disease. With glm.nb I can analyze all the samples together using drug and disease as two factors (0/1). However, edgeR only allows pairwise comparisons, such as between cases with no drug vs controls with no drug OR cases with drug vs controls with drug to look at the effect of disease, and cases with no drug vs cases with drug OR controls with no drug vs controls with drug to look at the effect of drug. This tremendously reduces the power of the data I have. I just want to confirm if I am correct in understanding this. Any comments will be highly appreciated.

Thanks

AD

This is also a basic question but I am having a hard time finding it in the documentation.

If y is a DGE list object which contains norm.factors and lib.size, are these already included in the GLM regression model or must one explicitly add them via the lib.size parameter and/or design matrix.

Put another way, if I call res = glmFit(y, design), is the model for each gene y_i ~ design + lib.size*norm.factors?

You do not have to (ie.

you should not) explicitly add themAlso, the correct set-up for each gene looks more like this:

where

`coef`

is a vector of estimated coefficients,`mu`

is a vector of fitted values across libraries (an estimate of`E(y)`

for a vector of counts`y`

), and`lib.size`

and`norm.factors`

are vectors of their respective values across libraries.