I’m experimenting with the ability of fry/mroast functions to include gene weights. I have two use cases:
1) Comparing the result of a previous experiment in mouse, with a current experiment in human: Basically I want to see if a previous observed response in mouse is observed in a similar human experiment. I can use Ensembl homologs to match results from the mouse data to the human data. There are multiple ways of setting the weights though, and I’m not sure which is best:
- Set gene.weights to be the observed logFCs in mouse. This will result in most genes having a weight set.
- Only set weights for genes that were DE (FDR < 0.05) in mouse.
- Similar to above, but only setting basic directional weights (-1,1) instead of using the actual logFCs
- Using predictive logFCs from predFCm, again most genes will end having weights set.
2) Enrichment of a gene set with gradual membership:
Let’s say I’m able to score each gene based on whether it belongs to some geneset. So basically I have a weight for each gene on some (more or less arbitrary) scale. How important is the absolute values of these weight to how mroast/fry uses them? Should they be scaled to the 0-1 interval, or can they go from say 1-1000?
I might also be interested in competitive version of the same test. Neither CAMERA nor ROMER accepts weights, leaving only barcodeplot. While this visualisation is nice, it’s not really useful for testing a large number sets. Is a possible to do a weighted competitive test in limma?