Description Details Author(s) References Examples
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.
Package: | EDDA |
Type: | Package |
Version: | 0.99.2 |
Date: | 2014-02-12 |
License: | GPL (>= 2) |
generateData() testDATs() computeAUC() plotROC() plotPRC()
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>
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.
1 2 3 4 5 | 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)
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