Modeling and analysis of RNA-seq data: a review from a statistical perspective

Abstract

Background: Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date.

Results: We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations.

Conclusions: The development of statistical and computational methods for analyzing RNA-seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statistical models and exhibit different performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development.

Graphical abstract

Keywords

RNA-seq / statistical modeling / differentially expressed genes / alternatively spliced exons / isoform reconstruction and quantification

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ACKNOWLEDGEMENTS

This work was supported by the following grants: National Science Foundation DMS-1613338, NIH/NIGMS R01GM120507, PhRMA Foundation Research Starter Grant in Informatics, Johnson & Johnson WiSTEM2D Award, and Sloan Research Fellowship (to J.J.L) and the UCLA Dissertation Year Fellowship (to W.V.L). The authors would like to thank the insightful feedbacks from Dr. Lior Pachter at California Institute of Technology and Dr. Michael I. Love at University of North Carolina at Chapel Hill.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Wei Vivian Li and Jingyi Jessica Li declare that they have no conflict of interests.

This article is a review article and does not contain any studies with human or animal subjects performed by any of the authors.

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2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature