title: 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%.
authors.forename: Huaien
authors.initials: H
authors.lastname: Luo
authors.forename: Juntao
authors.initials: J
authors.lastname: Li
authors.forename: Burton Kuan Hui
authors.initials: BK
authors.lastname: Chia
authors.forename: Paul
authors.initials: P
authors.lastname: Robson
authors.forename: Niranjan
authors.initials: N
authors.lastname: Nagarajan
mesh_terms: D019295:Computational Biology / Q000191:economics / Q000379:methods / Q000592:standards; D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing / Q000191:economics / Q000379:methods* / Q000592:standards; D006801:Humans; D020014:K562 Cells; D056186:Metagenomics; D017423:Sequence Analysis, RNA / Q000191:economics / Q000379:methods* / Q000592:standards; D059010:Single-Cell Analysis