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

PeerJ

article

Diem-Trang Tran¹  Matthew Might² 

[1] School of Computing, University of Utah;Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham

关键词: RNA-seq;    Benchmarking;    Assessment;    Transcriptomics;    Transcriptomic profiling;    Normalization;    SVD;    Spike-ins;    Performance measure;    Public datasets;   

DOI  :  10.7717/peerj.12233

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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 ad hoc 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 cdev, to quantify normalization success. cdev measures how much an expression matrix differs from another. If a ground truth normalization is given, cdev 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 cdev, provides a valuable toolset for benchmarking new and existing normalization methods.

【 授权许可】

CC BY