calcAUCValue {TCC} | R Documentation

## Calculate AUC value from a TCC-class object

### Description

This function calculates AUC (Area under the ROC curve) value from a TCC-class object for simulation study.

### Usage

    calcAUCValue(tcc, t = 1)

### Arguments

`tcc` | TCC-class object having values in both `stat$rank` and `simulation$trueDEG` fields.
`t` | numeric value (between 0 and 1) specifying the FPR (i.e., the x-axis of ROC curve). AUC value is calculated from 0 to `t`. The default is 1.

### Details

This function is generally used after the `estimateDE` function that estimates p-values (and the derivatives such as the q-values and the ranks) for individual genes based on the statistical model for differential expression (DE) analysis. In case of the simulation analysis, we know which genes are DEGs or non-DEGs in advance and the information is stored in the `simulation$trueDEG` field of the `TCC-class` object `tcc` (i.e., `tcc$simulation$trueDEG`). The `calcAUCValue` function calculates the AUC value between the ranked gene list obtained by the `estimateDE` function and the truth obtained by the `simulateReadCounts` function. A well-ranked gene list should have a high AUC value (i.e., high sensitivity and specificity).

### Value

numeric scalar.

### Examples

    # Analyzing a simulation data for comparing two groups
    # (G1 vs. G2) with biological replicates.
    # the first 200 genes are DEGs, where 180 are up-regulated in G1.
    # The DE analysis is performed by an exact test in edgeR coupled
    # with the DEGES/edgeR normalization factors.
    tcc <- simulateReadCounts(Ngene = 1000, PDEG = 0.2,
                              DEG.assign = c(0.9, 0.1),
                              DEG.foldchange = c(4, 4),
                              replicates = c(3, 3))
    tcc <- calcNormFactors(tcc, norm.method = "tmm", test.method = "edger",
                           iteration = 1, FDR = 0.1, floorPDEG = 0.05)
    tcc <- estimateDE(tcc, test.method = "edger", FDR = 0.1)
    calcAUCValue(tcc)

    # Analyzing a simulation data for comparing two groups
    # (G1 vs. G2) without replicates.
    # the levels of DE are 3-fold in G1 and 7-fold in G2
    # The DE analysis is performed by an negative binomial test in
    # DESeq coupled with the DEGES/DESeq normalization factors.
    tcc <- simulateReadCounts(Ngene = 1000, PDEG = 0.2,
                              DEG.assign = c(0.9, 0.1),
                              DEG.foldchange = c(3, 7),
                              replicates = c(1, 1))
    tcc <- calcNormFactors(tcc, norm.method = "deseq", test.method = "deseq",
                           iteration = 1, FDR = 0.1, floorPDEG = 0.05)
    tcc <- estimateDE(tcc, test.method = "deseq", FDR = 0.1)
    calcAUCValue(tcc)

[Package TCC version 1.24.0 Index]