{ "abstract": "Differential expression/abundance analyses are commonplace in studies employing high-throughput sequencing (HTS); however different tools often fail to return comparable results when applied to the same dataset. Most tools employ normalisations to attempt to correct for technical variation in the count data. Previously, we demonstrated that these normalisations are often inappropriate due to incorrect assumptions regarding the overall scale (i.e., size) of the biological system in question. In this study, we used a combination of binomial thinning and permutation of sample groupings to produce 100 analysis iterations of 11 RNA-seq and other HTS datasets in which ~5% of all features are expected to be significantly different between groups. This enabled calculation of the false discovery rate (FDR) and sensitivity across the iterations. Our simulations showed that scale misspecification results in poor control of the FDR by several commonly used tools and that, counterintuitively, FDRs increased as the modelled difference between groups increased. Implementing a scale model in ALDEx2 or ALDEx3 ameliorated unacceptably high FDRs; however, there was an inherent trade-off between satisfactory FDR control and high sensitivity- no tool offered both. We established that increasing scale uncertainty also increased the minimum difference between groups required for a feature to be reported as differentially expressed. This phenomenon was consistently observed in disparate types of HTS data and was remarkably consistent. Critically, we leveraged a ‘real-world’, non-permuted analysis of an RNA-seq dataset to demonstrate that the latter effect is not a result of our thinning/permutation approach. Overall, our work highlights the potentially unwitting choice between sensitivity and FDR control that all researchers are making when analysing sequencing data and provides guidance on choosing an appropriate amount of scale uncertainty for the analysis of HTS data. Author summary: Despite attempts to define best practices in differential abundance or expression analysis of high-throughput sequencing data, there is no standardised approach, and a plethora of tools are available to use. Each tool applies different assumptions and methods, which can lead to drastically different results when multiple tools are applied to the same dataset. Our work reproduces multiple recent studies showing that several popular tools exaggerate differences in gene expression or abundance between groups. We build on prior work by showing that these excessively high false discovery rates can be mitigated by using a new approach that models some degree of uncertainty around the total size (or scale) of the biological system under study. We find that scale models offer excellent false-discovery rate control, though at a reduced sensitivity. No tool offers both high sensitivity and a low rate of false positives, highlighting a trade-off that researchers must be cognisant of when analysing HTS data. Accordingly, we extend these observations with guidance on selecting an appropriate degree of scale uncertainty so that analysts can make an informed choice about balancing the ability to detect differences in their data with the confidence that any given difference is real.", "abstract_shown": true, "affiliations": [ "Department of Biochemistry, Western University, London, Ontario, Canada", "Program in Bioinformatics and Genomics, Pennsylvania State University, University Park, Pennsylvania, United States of America", "College of Information Science and Technology, Pennsylvania State University, State College, Pennsylvania, United States of America", "Department of Statistics, Pennsylvania State University, State College, Pennsylvania, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 4, "authors": [ { "affs": [ 0 ], "name": "Scott J. Dos Santos" }, { "affs": [ 1 ], "name": "Andreea C. Murariu" }, { "affs": [ 2 ], "name": "Justin D. Silverman" }, { "affs": [ 3 ], "name": "Gregory B. Gloor" } ], "bibtex": "https://research.draconitic.com/paper/85aqexqv3kcfy.bib", "cited_by": 0, "doi": "10.1371/journal.pcbi.1014728", "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014728", "id": "85aqexqv3kcfy", "issns": [ "1553-7358" ], "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014728", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1312|Gene expression and cancer classification" ], [ "plos", "/Biology and life sciences/Biochemistry/Nucleic acids/RNA/Non-coding RNA/Ribosomal RNA" ], [ "plos", "/Biology and life sciences/Biochemistry/Ribosomes/Ribosomal RNA" ], [ "plos", "/Biology and life sciences/Cell biology/Cellular structures and organelles/Ribosomes/Ribosomal RNA" ], [ "plos", "/Biology and life sciences/Computational biology/Genome analysis/Transcriptome analysis" ], [ "plos", "/Biology and life sciences/Genetics/Gene expression" ], [ "plos", "/Biology and life sciences/Genetics/Genomics/Genome analysis/Transcriptome analysis" ], [ "plos", "/Biology and life sciences/Immunology/Clinical immunology/Immunotherapy" ], [ "plos", "/Biology and life sciences/Molecular biology/Molecular biology techniques/Cloning/DNA cloning/Shotgun sequencing" ], [ "plos", "/Biology and life sciences/Molecular biology/Molecular biology techniques/Sequencing techniques/Shotgun sequencing" ], [ "plos", "/Medicine and health sciences/Clinical medicine/Clinical immunology/Immunotherapy" ], [ "plos", "/Medicine and health sciences/Immunology/Clinical immunology/Immunotherapy" ], [ "plos", "/Medicine and health sciences/Oncology/Cancers and neoplasms" ], [ "plos", "/Physical sciences/Mathematics/Algebra/Polynomials/Binomials" ], [ "plos", "/Physical sciences/Mathematics/Discrete mathematics/Combinatorics/Permutation" ], [ "plos", "/Research and analysis methods/Molecular biology techniques/Cloning/DNA cloning/Shotgun sequencing" ], [ "plos", "/Research and analysis methods/Molecular biology techniques/Sequencing techniques/Shotgun sequencing" ] ], "openalex": "W7211966788", "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014728&type=printable", "pmcid": null, "pmid": null, "published": "2026-09-08", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/85aqexqv3kcfy.ris", "sources": [ { "first_seen": "2026-10-30T12:17:56Z", "native_id": "10.1371/journal.pcbi.1014728", "source": "plos", "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014728" } ], "subjects": [ { "field": "biochemistry", "origin": "plos:gene expression", "subfield": "molecular-biology" }, { "field": "biology", "origin": "plos:immunology", "subfield": "immunology" } ], "title": "Not every gene is special: Modelling scale controls the false discovery rate when analysing high-throughput sequencing data", "title_text": "Not every gene is special: Modelling scale controls the false discovery rate when analysing high-throughput sequencing data", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:56Z", "url": "https://research.draconitic.com/paper/85aqexqv3kcfy", "venue": "PLOS Computational Biology", "versions": [] }