Description Usage Arguments Details Value Examples
View source: R/TCC.simulation.R
This function calculates AUC (Area under the ROC curve) value from a TCCclass object for simulation study.
1  calcAUCValue(tcc, t = 1)

tcc 
TCCclass object having values in both 
t 
numeric value (between 0 and 1) specifying the FPR (i.e., the
xaxis of ROC curve). AUC value is calculated from 0 to

This function is generally used after the estimateDE
function
that estimates pvalues (and the derivatives such as the qvalues
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 nonDEGs in advance and the information is stored in
the simulation$trueDEG
field of the TCCclass
object tcc
(i.e., tcc$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 wellranked gene list should have a high AUC value
(i.e., high sensitivity and specificity).
numeric scalar.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  # Analyzing a simulation data for comparing two groups
# (G1 vs. G2) with biological replicates.
# the first 200 genes are DEGs, where 180 are upregulated 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 3fold in G1 and 7fold 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)

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