Can limma/other DE algorithms be used for differential analysis of chemical descriptors?

Question

[Alonzo]

If I have two groups of small molecules with different properties, e.g. one can penetrate membranes and the other cannot, and I have an m × n matrix of m small molecules and n descriptors (e.g. polar surface area, molecular weight), can I use, say, limma to identify descriptors that differ between these two groups? So basically, I would just be using limma on molecular descriptors instead of gene expression data. If it makes any difference, some of these descriptors are discrete (e.g. number of rotatable bonds) and some are continuous (e.g. weight.)

If so, would I have to prepare the data in any special way? If not, what algorithms would be best for this sort of task?

Answer 1

[Devon Ryan]

While a regression-based strategy is fine, you're going to run into problems if you try to use something like limma, which is very fancy linear regression, for this. Firstly the aggregation of variance information across rows (you'd need to transform your matrix to n x m rather than m x n) would be nonsense. Further, it doesn't make sense to use linear regression on binary or multinomial data. I would suggest you just use the appropriate logistic regression or equivalent with each descriptor.