--- a
+++ b/codes.R
@@ -0,0 +1,36 @@
+#####################################
+
+library(MLSeq)
+data(cervical)
+
+set.seed(12349)
+ratio=0.7
+conditions = factor(rep(c("N","T"), c(29,29)))
+ind = sample(58, ceiling(58*ratio), FALSE)
+
+train = cervical[,ind]
+test = cervical[,-ind]
+
+tr.cond = conditions[ind]
+ts.cond = conditions[-ind]
+
+tmmT = voomDDA.train(counts = train, conditions = tr.cond, normalization = "TMM", TRUE)
+tmmF = voomDDA.train(counts = train, conditions = tr.cond, normalization = "TMM", FALSE)
+
+quanT = voomDDA.train(counts = train, conditions = tr.cond, normalization = "quan", TRUE)
+quanF = voomDDA.train(counts = train, conditions = tr.cond, normalization = "quan", FALSE)
+
+noneT = voomDDA.train(counts = train, conditions = tr.cond, normalization = "none", TRUE)
+noneF = voomDDA.train(counts = train, conditions = tr.cond, normalization = "none", FALSE)
+
+tmmNSC = voomNSC.train(counts = train, conditions = tr.cond, normalization = "TMM")
+quanNSC = voomNSC.train(counts = train, conditions = tr.cond, normalization = "quan")
+  
+table(ts.cond, predict.voomDDA(tmmT, test))
+table(ts.cond, predict.voomDDA(tmmF, test))
+table(ts.cond, predict.voomDDA(quanT, test))
+table(ts.cond, predict.voomDDA(quanF, test))
+table(ts.cond, predict.voomDDA(noneT, test))
+table(ts.cond, predict.voomDDA(noneF, test))
+table(ts.cond, predict.voomNSC(tmmNSC, test))
+table(ts.cond, predict.voomNSC(quanNSC, test))