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

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput assays like RNA-sequencing are crucial for detecting differential abundance.
• Inconsistent performance across statistical tests and normalization methods leads to wasted resources and reduced sensitivity.

Purpose of the Study:

• To introduce EDDA, a general design tool for RNA-sequencing, Nanostring, and metagenomic analysis.
• To rationally select statistical tests, predict experimental performance, and minimize resource wastage.

Main Methods:

• EDDA models various high-throughput assays, including single-cell RNA-sequencing.
• It incorporates a novel mode-based normalization for differential abundance detection.
• The tool predicts performance and plans experiments to optimize resource allocation.

Main Results:

• EDDA can reduce sequencing costs up to five-fold for single-cell RNA-sequencing.
• It improves metagenomic biomarker detection through optimized test selection.
• Novel normalization enhances robustness by 10-20% and precision by up to 140%.

Conclusions:

• EDDA offers a first-of-its-kind design tool for optimizing high-throughput omics experiments.
• The tool significantly reduces resource wastage and enhances analytical sensitivity and precision.
• EDDA has broad applicability across RNA-sequencing, Nanostring, and metagenomic studies.