ArticleF1000Research2015
Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.
Article in F1000Research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3,085 papers, 2 of them syntheses that pooled it.
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Who cites it
3,085 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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3,025 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
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. 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 ( tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.
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