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.
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?
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.
If we look at the section 3.5 of edgeR manual, on this thread (question about the EdgeR package: additive models and blocking) you suggested subsetting the data and performing separate analyses on "without hormone" and the "with hormone" samples to find DE genes for diseased patients vs healthy patients. Is there any way in edgeR that I include all samples for computing DE genes between diseased patients vs healthy patients while adjusting for hormone differences.
Direct comparisons between diseased and healthy patients of the same hormone status are not possible if you block for the patient effect. The patient blocking factor will absorb differences between samples from different patients, making the disease effect impossible to interpret. On the other hand, you can't just discard the patient blocking factor from your design, as you would end up with hidden correlations between hormone-treated and untreated samples from the same patient. This underlies Gordon's suggestion; to analyse hormone-treated samples separately from untreated samples, such that there is only one sample from each patient in either analysis (thus avoiding correlations between samples); or to analyse all samples with voom and duplicateCorrelation, in order to adjust for the known correlations between samples from the same patient.
This followup question is unrelated to the topic of edgeR vs glm.nb. If you have a new question, please start a new question rather than posting it as an answer to your previous question.
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 them
Also, the correct set-up for each gene looks more like this:
where
coefis a vector of estimated coefficients,muis a vector of fitted values across libraries (an estimate ofE(y)for a vector of countsy), andlib.sizeandnorm.factorsare vectors of their respective values across libraries.