EDDA: Experimental Design in Differential Abundance analysis

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).

Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("EDDA")
AuthorLi Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan
Bioconductor views ChIPSeq ExperimentalDesign Normalization RNASeq Sequencing
Date of publicationNone
MaintainerChia Kuan Hui Burton <chiakhb@gis.a-star.edu.sg>, Niranjan Nagarajan <nagarajann@gis.a-star.edu.sg>
LicenseGPL (>= 2)
Version1.14.0
http://edda.gis.a-star.edu.sg/, http://genomebiology.com/2014/15/12/527

View on Bioconductor

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