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  3. Tree-based Differential Testing Using Inferential Uncertainty For Rna-seq.
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  3. Tree-based Differential Testing Using Inferential Uncertainty For Rna-seq.

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Tree-based differential testing using inferential uncertainty for RNA-seq.

Noor P Singh1, Euphy Wu2, Jason Fan1

  • 1Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA.

Genome Research
|August 21, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Identifying differential gene expression is challenging due to transcript abundance uncertainty. Our new method, mehenDi, uses a tree structure to find significant expression changes, including those missed by other approaches.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Differential expression analysis of RNA-sequencing data is critical for biological insights.
  • Transcript abundance estimation uncertainties can lead to false positives or reduced statistical power.
  • Existing methods often struggle to effectively incorporate this uncertainty into differential testing.

Purpose of the Study:

  • To introduce mehenDi, a novel method for differential transcript expression analysis.
  • To leverage a hierarchical tree structure (TreeTerminus) to manage uncertainty in transcript abundance.
  • To identify differentially expressed transcripts and transcript groups, including those at inner nodes of the tree.

Main Methods:

  • Utilized the TreeTerminus hierarchical structure to represent transcript relationships and uncertainty.
  • Developed mehenDi for data-driven differential testing on the tree structure.
  • Selected nodes (transcripts or inner nodes) to maximize signal while controlling for abundance uncertainty.
  • Compared mehenDi against existing tree-based and uncertainty-aware differential expression methods.

    • Main Results:

    • mehenDi successfully identified differentially expressed inner nodes, revealing signals missed by transcript-only analysis.
    • The method demonstrated robust performance on both simulated and experimental RNA-seq datasets.
    • mehenDi effectively balances the detection of differential expression with the control of uncertainty.

    Conclusions:

    • mehenDi offers an advanced approach to differential expression analysis by incorporating transcriptomic hierarchy and uncertainty.
    • The method enhances the discovery of biologically relevant differential expression signals.
    • mehenDi provides a powerful tool for transcriptomic data interpretation.