I'm trying to generate a ROC curve for different RNA-seq normalisations, using qRT-PCR data for comparison. I want to define True Positives from the RNA-seq data as genes which are measured as significantly differentially expressed (FDR < 0.05) in the same direction (logFC) from the qRT-PCR data.

I am using the following function to generate my ROC curves (from this excellent single-cell RNA-seq tutorial):

DE_Quality_AUC <- function(pVals) {

    # Only use RNA-seq genes which are also in the qRT-PCR dataset
    pVals <- pVals[names(pVals) %in% GroundTruth$DE | 
                   names(pVals) %in% GroundTruth$notDE]

    # Create vector of classifications
    truth <- rep(1, times = length(pVals));

    # Classify true positives
    truth[names(pVals) %in% GroundTruth$DE] = 0;

    # Generate ROC/AUC values based on FDR from differential expression analysis
    pred <- ROCR::prediction(pVals, truth)
    perf <- ROCR::performance(pred, "tpr", "fpr")

    # Plot ROC curve
    ROCR::plot(perf)

    # Return AUC value
    aucObj <- ROCR::performance(pred, "auc")
    return(aucObj@y.values[[1]])

}

However this does not take into account if the fold change between the RNA-seq and qRT-PCR data is in the same direction, and I'm struggling to come up with a way to incorporate this additional classifier, any ideas?