Abstract
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. Several different 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 while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets. Finally, we provide an R package (tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.
Highlights
Quantification and comparison of isoform- or gene-level expression based on high throughput sequencing reads from cDNA (RNAseq) is arguably among the most common tasks in modern computational molecular biology
The curves trace out the observed false discovery rate (FDR) and TPR for each significance cutoff value
We have shown that when testing for changes in overall gene expression (DGE), traditional gene counting approaches may lead to an inflated false discovery rate compared to methods aggregating transcript-level TPM values or incorporating correction factors derived from these, for genes where the relative isoform usage differs between the compared conditions
Summary
Paragraph 1: Cufflinks, RSEM and Bitseq are grouped with kallisto and Salmon and it is stated that some of these methods bypass read alignment. It would be clearer if this were reworded to avoid the ambiguity as to which methods avoid read alignment. While it is interesting that simple counting performs to transcript-level quantification procedures, it seems more interesting to this reviewer that incorporating transcript-level information improves the accuracy of differential expression testing at the gene level. Perhaps these two concepts can be combined into one more concise point?
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