I've been using DESeq2 for differential expression analysis of microbial (meta)transcriptomic datasets and have been very happy with its performance. I've started to overlay pathway analyses onto these differential expression results to identify functional groupings of genes (via KEGG or SEED) that are over- or under-represented in these DE gene sets. In parallel, I'd also like to be able to take a dataset, order the genes from most- to least-expressed, and look for enrichment of certain functional groupings in the most highly-expressed genes in a given dataset. My question is whether it makes sense to normalize, specifically via a DESeq2-performed size factor, rlog, or vst normalization, prior to ordering the genes from greatest to least expression?

I'm aware of the value of these normalization strategies for preparing datasets for differential expression analyses but would greatly appreciate an opinion on whether these are also appropriate methods for preparing a transcriptional dataset for the types of analysis I described.

The gene counts don't tell you about the order of expression across genes. For that you need to estimate a quantity like TPM, where ideally there is normalization for transcript length, fragment length distribution, and various other sample-specific biases. You can estimate TPMs very quickly with software like Salmon, Sailfish, or kallisto, and then import these into R with the tximport package. These are also my preferred way to generate count matrices for DESeq2, as we mention in the current version of the vignette and workflow.