Experimental Design in Differential Abundance analysis

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Description

EDDA aids in the design of a range of common experiments including RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance.

Details

Package: EDDA
Type: Package
Version: 0.99.2
Date: 2014-02-12
License: GPL (>= 2)

generateData() testDATs() computeAUC() plotROC() plotPRC()

Author(s)

Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan

Maintainer: Li Juntao<lij9@gis.a-star.edu.sg>, Luo Huaien<luoh2@gis.a-star.edu.sg>, Niranjan Nagarajan <nagarajann@gis.a-star.edu.sg>

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

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