TCC (version 1.12.1)

calcAUCValue: 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.

Value

numeric scalar.

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).

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)