Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

TLDR

RNA‑seq is widely used to profile cellular transcriptomes, and studies compare abundance or composition across conditions, requiring read‑based quantification ranging from simple read counting to sophisticated transcript abundance estimation. The authors aim to demonstrate that gene‑level abundance estimates and statistical inference outperform transcript‑level analyses in performance and interpretability. They provide the R package tximport, which integrates transcript‑level abundance estimates from common pipelines into count‑based statistical inference engines. They find that gene‑level estimates reduce false discovery rates inflated by differential isoform usage, and that this issue is minor in several real datasets, confirming the superiority of gene‑level inference over transcript‑level approaches.

Abstract

<ns4:p>High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Various quantification approaches have been proposed, ranging from simple counting of reads that overlap given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability. We also illustrate that the presence of differential isoform usage can lead to inflated false discovery rates in differential gene expression analyses on simple count matrices but that this can be addressed by incorporating offsets derived from transcript-level abundance estimates. We also show that the problem is relatively minor in several real data sets. Finally, we provide an R package (<ns4:italic>tximport</ns4:italic>) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.</ns4:p>