2.7 years ago by
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
We recently did a simulation study with romer() and, unfortunately, both romer() and camera() do need multiple testing adjustments. camera() does this automatically but romer() doesn't. You can easily add an FDR column for romer() yourself.
romer() is designed to be an improved parametric analog of the Broad Institute's GSEA approach, but the approach is neither purely competitive nor self-contained, and personally I prefer the clarity of mroast() or camera().
My favourites are either mroast() with as many rotations as you have time for or camera() with a preset intergene correlation of 0.05 as in:
camera(y, index, design, contrast="whatever", inter.gene.cor=0.05)
Both of these will be more powerful than romer().
If your data has a large value for df.prior, then you can use fry() to approximate the mroast() results without having to run the rotations. That is a recent addition to the package.