plotPRC {EDDA} | R Documentation

## 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.

### 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)

[Package EDDA version 1.26.0 Index]