Dane

1. Wilcoxon-Mann-Whitney Test

wmct <- WMCT(pcbc, tcga[TCGA$normal,], G='SC', njob=3, adjust='fdr')
signature <- signatureWMCT(wmct,n=7000)
rss$wmct_plot + scale_fill_manual(values=c("#999999", "salmon", "#56B4E9"))

2. Klasyfikacja

learn <- setupLearningSets(pcbc,tcga[TCGA$normal,], G='SC',
                               signature = signature, cutoff=0.7)
rss$balance
## [1] "healthy:SC 168:30" "healthy:SC 73:14"

2a. Regresja Logistyczna

objectiveFun <- c("Class","AUC","Deviance")
models <- lapply(objectiveFun, function(f)
      buildScorer(learn$train$X, learn$train$Y, model="LR",
                  cv.measure=tolower(f), intercept=TRUE, standardize=FALSE,
                  njob = nthreads))
names(models) <- objectiveFun
grid.arrange(rss$glmnet_gg$Class,
             rss$glmnet_gg$AUC + ylab(''),
             rss$glmnet_gg$Deviance + ylab(''), ncol=3)

rss$glmnet_coefs

grid.arrange(rss$glmnet_features)