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

Identifying differentially expressed transcripts poses a crucial yet challenging problem in transcriptomics. Substantial uncertainty is associated with the abundance estimates of certain transcripts which, if ignored, can lead to the exaggeration of false positives and, if included, may lead to reduced power. For a given set of RNA-Seq samples, TreeTerminus arranges transcripts in a hierarchical tree structure that encodes different layers of resolution for interpretation of the abundance of transcriptional groups, with uncertainty generally decreasing as one ascends the tree from the leaves. We introduce trenDi, which utilizes the tree structure from TreeTerminus for differential testing. The candidate nodes are determined in a data-driven manner to maximize the signal that can be extracted from the data while controlling for the uncertainty associated with estimating the transcript abundances. The identified candidate nodes can include transcripts and inner nodes, with no two nodes having an ancestor/descendant relationship. We evaluated our method on both simulated and experimental datasets, comparing its performance with other tree-based differential methods as well as with uncertainty-aware differential transcript/gene expression methods. Our method detects inner nodes that show a strong signal for differential expression, which would have been overlooked when analyzing the transcripts alone.

SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty.

Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.

Aided recall of The Real Cost e-cigarette prevention advertisements among a nationally representative sample of adolescents.

E-cigarette use among youth remains a significant public health concern. In 2018, The Real Cost campaign began disseminating messages about the harms of vaping, primarily using digital media. We sought to determine the prevalence of aided recall of The Real Cost e-cigarette prevention ads and identify potential differences by participant characteristics. Participants were a nationally representative sample of adolescents living in United States (US) households recruited by the National Opinion Research Center (NORC) at the University of Chicago's AmeriSpeak panel in September and October of 2020. A total of 623 adolescents completed the survey. Analyses were weighted to represent the distribution of youth in the US, and effect sizes for individual characteristics were estimated using an adjusted marginalized two-part model. Seventy-one percent of adolescents recalled at least one of the five The Real Cost e-cigarette prevention ads, with individual ad recall ranging from a low of 38.8% (for Magic) to a high of 50.1% (for Narrative). Adjusted estimates of aided recall identified significantly higher recall among Black adolescents and those that used social media at medium or high frequencies (p < 0.05). Results support ongoing efforts by the FDA to reach youth with e-cigarette prevention messages using primarily digital media.