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Genome Biol. 2014 Dec 3;15(12):527. doi: 10.1186/s13059-014-0527-7.

The importance of study design for detecting differentially abundant features in high-throughput experiments.

Abstract

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%.

PMID:
25517037
PMCID:
PMC4253014
DOI:
10.1186/s13059-014-0527-7
[Indexed for MEDLINE]
Free PMC Article

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