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May 20, 2026BioinformaticsOpen Access

Clarifying the scope and capabilities of ROTS in differential expression analysis

Tomi Suomi Jalmari Kettunen Taneli Pusa Laura L. Elo

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Key Points

This research aims to clarify the methodological aspects of using ROTS in differential expression analysis.
Reanalyses were performed on spike-in datasets to evaluate the ROTS method.
The studies utilized the original ROTS code with minimal modifications.
Reanalyses did not support the conclusions of previous spike-in case studies.
Results from the clinical Alzheimer's disease case study could not be reproduced.
Findings stress the need for transparent benchmarking practices in statistical analysis.

Implication

Study clarifies differential expression analysis in spike-in datasets, highlighting methodological challenges.

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Cite This Study

Suomi et al. (2026) studied this question.

synapsesocial.com/papers/6a1689a80c924ddd1bd5854f https://doi.org/10.1093/bioinformatics/btag335

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1LimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Differential Expression Analysis2025 · 2 citations
2Relative Abundance of Transcripts (RATs): Identifying differential isoform abundance from RNA-seqOriginal Research · 2019 · 44 citations
3ROOFS: RObust biOmarker Feature SelectionClinical Trial · 2026 · 1 citations
4Revisiting differential expression analysis: An updated six-dimensional comparative studyOriginal Research · 2026
5Critical Differential Expression Assessment for Individual Bulk RNA-Seq Projects2024 · 2 citations

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