R/eic.qual.R

In yufree/apLCMS: Adaptive processing of LC-MS data

eic.qual <-
function(eic.rec, known.mz, mass.matched=NA, match.tol.ppm=5, do.plot=FALSE, pos.confidence=0.99, neg.confidence=0.99)
{
    
    if(is.na(mass.matched[1]))
    {
        y<-mass.match(x=eic.rec[,2], known.mz=known.mz, match.tol.ppm=match.tol.ppm)
    }else{
        y<-mass.matched
    }
    
    rec.def<-rec.unc<-new("list")
    vus.fcauc<-matrix(nrow=ncol(eic.rec)-2, ncol=4)
    colnames(vus.fcauc)<-c("vus_1","fcauc_1","vus_uncertainty","fcauc_uncertainty")
    rownames(vus.fcauc)<-colnames(eic.rec)[-1:-2]
    
    for(i in 3:ncol(eic.rec))
    {
        x<-eic.rec[,i]
        r<-rocs.x(x[y==0], x[y==1], rep(1, sum(y==0)), rep(1, sum(y==1)), n.perm=1, FDR.cut=1, do.plot=FALSE)
        rec.def[[i-2]]<-r
        vus.fcauc[i-2, 1:2]<-unlist(r[1:2])
        
        r<-rocs.x(x[y==0], x[y==1], rep(0.99, sum(y==0)), rep(0.99, sum(y==1)), n.perm=1, FDR.cut=1, do.plot=FALSE)
        rec.unc[[i-2]]<-r
        vus.fcauc[i-2, 3:4]<-unlist(r[1:2])
    }
    
    if(do.plot)
    {
        par(mfrow=c(3,3))
        for(i in 1:min(9, length(rec.unc)))
        {
            plot(rec.def[[i]]$fp, rec.def[[i]]$tp, type="l", col="blue",xlab="FPR", ylab="TPR", main=rownames(vus.fcauc)[i])
            lines(rec.unc[[i]]$fp, rec.unc[[i]]$tp, col="red")
            abline(0,1,col="grey", lty=2)
        }
    }
    vus.fcauc
}