ROCcurve: ROCs
In emodoro/dEMO: Differential Expression and confidencial estimation for FC

Description Usage Arguments Value Author(s) Examples

ROCcurve is a functions which allow plotting ROCs for several hypothesis testing methods, shuch a several Differential Expression analysis.

1	ROCcurve(dataRoc, main)

`dataRoc`	a data.frame where each column stores p values from each of the hypothesis test methods which are wanted to compare. The last column store the information related to real differential expression, where "0" means no differential expression and "1" means differential expression.
`main`	the title of the plot

a plot with the ROC for every and each one of hypothesis test methods

Enrique Perez_Riesgo

library(compcodeR)
library(edgeR)
set.seed(123456987)
datasCI<-generateSyntheticData(dataset = EMOresults, n.vars = 15000,samples.per.cond = 6, n.diffexp = 1500,repl.id = 1, seqdepth = 1e7,fraction.upregulated = 0.5,between.group.diffdisp = FALSE,filter.threshold.total = 1,filter.threshold.mediancpm = 0,fraction.non.overdispersed = 0)
expressiondata<-datasCI@count.matrix
TMMfac<-calcNormFactors.default(expressiondata,method = "TMM")
exprT<-t(t(expressiondata)*TMMfac)
conditions<-(datasCI@sample.annotations$condition-1)
testEMOTMM<-EMObuODlmTest(expr=exprT, condition = conditions,original = exprT,originalm=exprT)
edgeR.dgelist = DGEList(counts = expr, group = factor(conditions))
edgeR.dgelist = calcNormFactors(edgeR.dgelist, method = "TMM")
edgeR.dgelist = estimateCommonDisp(edgeR.dgelist)
edgeR.dgelist = estimateTagwiseDisp(edgeR.dgelist, trend = "movingave")
edgeR.test = exactTest(edgeR.dgelist)
edgeR.pvalues = edgeR.test$table$PValue
edgeR.adjpvalues = p.adjust(edgeR.pvalues, method = "BH")
testedgeRTMM<-data.frame(FC=edgeR.test$table$logFC, PVALUE=edgeR.test$table$PValue, ADJ.PVAL=edgeR.adjpvalues)
dataRoc<-data.frame(EMO=testEMOTMM$ADJ.PVAL,edgeR=testedgeRTMM$ADJ.PVAL, DE=expr3@variable.annotations$differential.expression)
ROCcurve(dataRoc = dataRoc, main="ROC")