The importance of study design for detecting differentially abundant features in high-throughput experiments
Huaien Luo, Juntao Li, Kuan Hui Burton Chia, Paul Robson, Niranjan Nagarajan
This paper introduces EDDA, a tool for designing high-throughput experiments to detect differentially abundant features, showing it improves precision and reduces resource use in single-cell RNA-seq, Nanostring, and Metagenomics studies.
Genome Biology · Dec 3, 2014Peer-reviewedBioinformatics