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