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

TL;DR

This review discusses statistical models and methods for analyzing RNA-seq data across different biological levels, highlighting their assumptions, performance, and practical considerations to guide users and developers.

Contribution

It provides a comprehensive comparison of statistical models used in RNA-seq analysis, aiding method selection and future development.

Findings

Significant advances in statistical methods over the past decade.

Diverse models exhibit different performances depending on scenarios.

Guidance provided for selecting appropriate analysis methods.

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. Conclusion: 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 statical 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.