EDDA can aid in the design of a range of common experiments such as 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. This work was published on 3 December 2014 at Genome Biology under the title "The importance of study design for detecting differentially abundant features in high-throughput experiments" (http://genomebiology.com/2014/15/12/527).
|Author||Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan|
|Bioconductor views||ChIPSeq ExperimentalDesign Normalization RNASeq Sequencing|
|Date of publication||None|
|Maintainer||Chia Kuan Hui Burton <firstname.lastname@example.org>, Niranjan Nagarajan <email@example.com>|
|License||GPL (>= 2)|
BP: BaySeq Profile used in simulations by Hardcastle et al
computeAUC: compute AUC values.
EDDA-package: Experimental Design in Differential Abundance analysis
generateData: generate count data
HBR: Average abundance for RNA-seq data from Human Brain...
plotPRC: plot precision-recall curves
plotROC: plot Receiver Operating Characteristic curve
SingleCell: Single-cell RNA-seq data for model free simulation
testDATs: Run differential abundance testings
Wu: Average abundance for RNA-seq data from schizophrenia.
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