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Related Concept Videos

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Ribosome Profiling

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Updated: Jun 2, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Complementary ROC-Derived Indices for Screening Improper Expression Profiles in RNA-Seq Differential Expression

Merve Başol Göksülük1, Ebru Öztürk2, Ünal Erkorkmaz1

  • 1Department of Biostatistics, Sakarya University Faculty of Medicine, Sakarya, Türkiye.

Balkan Medical Journal
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

New receiver operating characteristic (ROC)-based indices, generalized area under the curve (gAUC) and length of the ROC curve (LROC), effectively identify improper expression profiles in RNA sequencing (RNA-Seq) data. These ROC indices complement standard differential expression methods for prioritizing potential disease-associated genes and microRNAs.

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Last Updated: Jun 2, 2026

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Differential expression (DE) analysis of RNA sequencing (RNA-Seq) data is crucial for transcriptomic research.
  • Standard DE methods primarily detect monotonic mean shifts, potentially missing genes with non-monotonic expression patterns (improper expression profiles) linked to disease.
  • Identifying these improper expression profiles is challenging with current tools.

Purpose of the Study:

  • To evaluate receiver operating characteristic (ROC)-based indices, specifically generalized AUC (gAUC) and length of ROC curve (LROC), for screening and prioritizing improper expression profiles in RNA-Seq data.
  • To assess the utility of ROC indices as a complement to conventional DE methods for discovering novel disease-associated transcript features.

Main Methods:

  • Comparison of DESeq2, classical AUC (cAUC), gAUC, and LROC using simulated negative binomial count data.
  • Performance evaluation based on true positive rate and positive predictive value under ranking-based feature selection.
  • Application of ROC-derived indices to a public CC miRNA dataset with heuristic screening rules.

Main Results:

  • Classical AUC (cAUC) was insensitive to improper expression patterns.
  • DESeq2 effectively identified conventionally differentially expressed features but recovered fewer simulated improper profiles.
  • gAUC demonstrated superior and stable recovery of improper expression profiles across various simulation scenarios.
  • LROC offered complementary insights under low-to-moderate dispersion but was less effective under high dispersion.
  • ROC indices identified candidate microRNAs in the CC dataset not prioritized by DESeq2, with some showing biological plausibility.

Conclusions:

  • gAUC, with LROC as a supplementary index, offers a practical ROC-based screening extension for RNA-Seq workflows.
  • These ROC-derived indices serve as exploratory tools for prioritizing candidate improper expression profiles.
  • Independent validation is essential for candidates identified using these heuristic thresholds without controlled error rates.