Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR

Abstract

A major challenge in the analysis of RNA-seq data at the transcript-level is accounting for the variability introduced during quantification of RNA sequencing reads. This variability is due to the high levels of sequence similarity among transcripts annotated to the same genomic locus and the mapping ambiguity resulting from the assignment of sequence reads to such transcripts. The quantification uncertainty associated with transcript-level estimated counts is intractable to measure analytically but represents an extra source of variation that seriously compromises differential transcript expression (DTE) analyses if standard statistical methods developed for gene-level analyses are used. Bootstrap counts, as provided by popular RNA-seq quantification tools, allow one to estimate the quantification uncertainty and account for such an effect in DTE analyses. We present catchSalmon and catchKallisto, two functions included in the R/Bioconductor package edgeR, that estimate the transcript-level quantification uncertainty, here termed mapping ambiguity overdispersion, using bootstrap counts. We discuss how the mapping ambiguity overdispersion can be effectively removed from the data in transcript-level analyses via count scaling, an approach that reduces the size of the estimated counts obtained from quantification tools to effective count sizes that reflect their true precision. The presented count scaling approach allows users to perform efficient DTE analyses within the efficient edgeR framework. A comprehensive simulation study and a DTE analysis of human lung adenocarcinoma cell lines are presented to illustrate the benefits of accounting for the mapping ambiguity overdispersion in transcript-level RNA-seq data analyses.

Population and Transcriptomic Changes of the Tropical Fish Parasite Caligus confusus (Copepoda: Caligidae) with Seasonal Variations in Sea Temperature

“…For the differential expression analysis, first, the RNA-seq reads were mapped to the assembled transcriptome using the package Kallisto 0.42.4 [36], to obtain a matrix of transcript counts and abundances. The matrix of transcript abundances was used as an input in the Bioconductor package edgeR 3.40.2 [37] to identify differentially expressed transcripts (DETs) by comparing the transcripts obtained from the cold group against those from the warm. In this analysis, the transcripts with expression levels below 4 counts per million reads (cpm) were removed and transcript counts were normalized using the "calcNormFactors" function.…”

Tree-based differential testing using inferential uncertainty for RNA-Seq

“…The robustness and accuracy of any downstream analysis, such as differential testing, is directly impacted by the quality of the abundance estimates. The methods designed for differential transcript expression testing that utilize inferential replicates report a more robust performance than the methods that do not include them [Seesi et al, 2014, Mandric et al, 2017, Pimentel et al, 2017, Zhu et al, 2019, Baldoni et al, 2023. However, if inferential replicates are incorporated, then we might observe a reduced power for the transcripts that exhibit high uncertainty since we might not be confident of the observed differential signal.…”