cdev: a ground-truth based measure to evaluate RNA-seq normalization performance

• Published in: PeerJ (San Francisco, CA) Vol. 9; p. e12233
• Main Authors: Tran, Diem-Trang, Might, Matthew
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 04.10.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Analysis; Assessment; Benchmarking; Bioinformatics; Clustering; Computational Biology; Consortia; Data collection; Data Science; Gene expression; Normalization; Principal components analysis; Ribonucleic acid; RNA; RNA-seq; Simulation; Transcriptomic profiling; Transcriptomics; Performance measure; SVD; RNA-seq; Transcriptomics; Assessment; Benchmarking; Public datasets; Normalization; Spike-ins; Transcriptomic profiling
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.12233

Normalization of RNA-seq data has been an active area of research since the problem was first recognized a decade ago. Despite the active development of new normalizers, their performance measures have been given little attention. To evaluate normalizers, researchers have been relying on measures, most of which are either qualitative, potentially biased, or easily confounded by parametric choices of downstream analysis. We propose a metric called condition-number based deviation, or to quantify normalization success. measures how much an expression matrix differs from another. If a ground truth normalization is given, can then be used to evaluate the performance of normalizers. To establish experimental ground truth, we compiled an extensive set of public RNA-seq assays with external spike-ins. This data collection, together with provides a valuable toolset for benchmarking new and existing normalization methods.