plotPRC: plot precision-recall curves

In EDDA: Experimental Design in Differential Abundance analysis

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.

Author(s)

Li Juntao and Chia Kuan Hui Burton

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)

EDDA documentation built on Nov. 8, 2020, 5:44 p.m.