Looked into ROC curves

Receiver operating characteristic curves, or ROC curves, can be used with proteomic data to visualize how sensitive and specific a potential biomarker is to the environment. I wanted to make some ROC curves for peptides that were differentially expressed between Puget Sound sites and Willapa Bay. In addition to this paper on ROC analysis, I found some resources on how to make ROC curves in R. It seems like I have a few options:

  1. pROC
  2. simple.ROC
  3. roc.plot
  4. Base R

I haven’t really looked into the differences between these methods, but what I can tell is that I’ll need to first build a model with my data. The simplest model I can build is something like the following:

protein abundance ~ site

However, I think my model will be more powerful (and the ROC curves more useful) if I include environmental variables and Alex’s data as covariates:

protein abundance ~ site + habitat + pH + temperature + DO + conductivity + chlorophyll + tidal depth + %c + %n + tissue mass + shell strength + shell density

Obviously, I can use covariate deletion to identify the most significant model before I proceed, or I can only include covariates that contribute to the differential protein expression. I think I’ll also need to use a binomial model with a logit link, since all of the resources mention that the ROC analysis is evaluating successes and failures.

Bottom line: I need to wait until I get the environmental data before I can proceed.