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

By Charlotte Soneson, Michael I. Love, Mark D. Robinson. 2015

3,582 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

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Citations in the corpus

SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data (2015, methodology): “For instance, the new tximport R package [19] available on Bioconductor allows to import in R transcript-level quantification results from softwares like salmon [20] and pass them to DESeq2.”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “For example, transcriptlevel abundance estimates are considerably more variable than genelevel counts due to ambiguous assignment of fragments to isoforms [73] .”
Splatter: simulation of single-cell RNA sequencing data (2017, methodology): “ch:3838/conquer/) and converted to a gene by cell matrix 1 using the tximport [44] package.”
Tximeta: Reference sequence checksums for provenance identification in RNA-seq (2019, methodology): “tximport [29] RNA-seq import Arkas [16] RNA-seq analysis ✓ ✓ ARMOR[17] RNA-seq analysis ✓ ✓ ✓”
CDK4/6 Inhibition Augments Anti-Tumor Immunity by Enhancing T Cell Activation (2017, methodology): “The digested NFATc3 and His-GB1 were separated and further purified using size exclusion chromatography (GE Healthcare Life Sciences “Superdex 75 10/300 GL”).”
Differential analysis of RNA-seq incorporating quantification uncertainty (2016, methodology): “In addition, we tested BitSeq 10 (Supplementary Figures 15 and 16) on a single sample as its run-time was prohibitive on the entire simulation set.”
variancePartition: interpreting drivers of variation in complex gene expression studies (2016, positive): “The software can easily be applied to RNA-seq quan-tifications from featureCounts [46], HTSeq [47], kallisto [48], sailfish [49], salmon [50], RSEM [51] and cufflinks [52] which have been processed in R with limma/voom [15], DESeq2 [16], tximport [53] and ballgown [54].”