TreeTerminus-creating transcript trees using inferential replicate counts

A certain degree of uncertainty is always associated with the transcript abundance estimates. The uncertainty may make many downstream analyses, such as differential testing, difficult for certain transcripts. Conversely, gene-level analysis, though less ambiguous, is often too coarse-grained. We introduce TreeTerminus, a data-driven approach for grouping transcripts into a tree structure where leaves represent individual transcripts and internal nodes represent an aggregation of a transcript set. TreeTerminus constructs trees such that, on average, the inferential uncertainty decreases as we ascend the tree topology. The tree provides the flexibility to analyze data at nodes that are at different levels of resolution in the tree and can be tuned depending on the analysis of interest. We evaluated TreeTerminus on two simulated and two experimental datasets and observed an improved performance compared to transcripts (leaves) and other methods under several different metrics.

Automated summary

Transcript abundance estimates always come with a certain level of uncertainty, which could pose challenges for downstream analyses like differential testing. TreeTerminus is a data-driven approach that groups transcripts into a tree structure, where leaves represent individual transcripts and internal nodes represent an aggregation of transcript sets. By constructing trees that reduce inferential uncertainty as we move up the topology, TreeTerminus enables flexible data analysis at different levels of resolution within the tree. Evaluations on simulated and experimental datasets show improved performance compared to transcripts (leaves) and other methods across multiple metrics.