EDDA – Experimental Design in Differential Abundance Analysis

High-throughput assays, such as RNA-seq, to detect differential abundance are widely used. Variable performance across statistical tests, normalizations, and conditions leads to resource wastage and reduced sensitivity. EDDA represents a first, general design tool for RNA-seq, Nanostring, and metagenomic analysis, that rationally selects tests, predicts performance, and plans experiments to minimize resource wastage. Case studies highlight EDDA’s ability to model single-cell RNA-seq, suggesting ways to reduce sequencing costs up to five-fold and improving metagenomic biomarker detection through improved test selection. EDDA’s novel mode-based normalization for detecting differential abundance improves robustness by 10% to 20% and precision by up to 140%.


Availability – EDDA is available at: http://www.bioconductor.org/packages/release/bioc/html/EDDA.html

Luo H, Li J, Chia BK, Robson P, Nagarajan N. (2014) The importance of study design for detecting differentially abundant features in high-throughput experiments. Genome Biol 15(12):527. [abstract]

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