DOI: 10.18129/B9.bioc.EDDA    

Experimental Design in Differential Abundance analysis

Bioconductor version: Release (3.6)

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

Maintainer: Chia Kuan Hui Burton <chiakhb at gis.a-star.edu.sg>, Niranjan Nagarajan <nagarajann at gis.a-star.edu.sg>

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biocViews ChIPSeq, ExperimentalDesign, Normalization, RNASeq, Sequencing, Software
Version 1.16.0
In Bioconductor since BioC 2.14 (R-3.1) (4 years)
License GPL (>= 2)
Depends Rcpp (>= 0.10.4), parallel, methods, ROCR, DESeq, baySeq, snow, edgeR
Imports graphics, stats, utils, parallel, methods, ROCR, DESeq, baySeq, snow, edgeR
LinkingTo Rcpp
URL http://edda.gis.a-star.edu.sg/ http://genomebiology.com/2014/15/12/527
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