EDDA (version 1.10.0)

plotPRC: plot precision-recall curves

Description

plot precision-recall curves for each test.

Usage

plotPRC(obj,DE.methods=c("Cuffdiff","DESeq","baySeq","edgeR","MetaStats","NOISeq"), 
		nor.methods=c("default","Mode","UQN","NDE"),
		plot_type = "o",plot_pch = 20,plot_lwd = 1.75,plot_cex = 1)

Arguments

obj

Object from testDATs().

DE.methods

Method list for differential expression tests. Methods currently available include "Cuffdiff","DESeq","baySeq","edgeR","MetaStats","NOISeq".

nor.methods

Normalization method list. Methods currently available include "default"(default normalization for each DE method),"Mode"(Mode normalization),"UQN"(Upper quartile normalization),"NDE"(non-differential expression normalization).

plot_type

type option in plot.

plot_pch

pch option in plot.

plot_lwd

lwd option in plot.

plot_cex

cex option in plot.

References

Luo Huaien, Li Juntao,Chia Kuan Hui Burton, Shyam Prabhakar, Paul Robson, Niranjan Nagarajan, The importance of study design for detecting differentially abundant features in high-throughput experiments, under review.

Examples

data <- generateData(EntityCount=500)
test.obj <- testDATs(data,DE.methods=c("DESeq","edgeR"),nor.methods="default")
auc.obj  <- computeAUC(test.obj)
plotPRC(auc.obj)