BioNet seems to require p-values to generate its scores for each node:
library(BioNet) library(DLBCL) data(dataLym) pvals <- cbind(t=dataLym$t.pval, s=dataLym$s.pval) rownames(pvals) <- dataLym$label pval <- aggrPvals(pvals, order=2, plot=FALSE) fb <- fitBumModel(pval, plot=FALSE) scores <- scoreNodes(subnet, fb, fdr=0.001)
module <- runFastHeinz(subnet, scores)
I'm building a data integration pipeline where p-values and other factors will be integrated into a score that goes from -1 to +1.
Can I use these custom scores directly as argument to
runFastHeinz()? Or do I need to convert them to BioNet scores, somehow? The distribution of BioNet scores in this example seems to be much wider than [-1, 1]:
summary(scores) Min. 1st Qu. Median Mean 3rd Qu. Max. -7.265 -6.946 -6.417 -5.610 -5.152 8.986
I'm also not entirely sure what the + and - sign mean in the BioNet scores: does the sign represents the directionality of the effect (I.e.: positive=activated, negative=inhibited)?