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Typically a statistical normalization removes unwanted technical variation in the data or the parameters of the model to enable differential abundance analyses. We recently showed that all normalizations make implicit assumptions about the unmeasured system scale and that errors in these assumptions can dramatically increase false positive and false negative rates. We demonstrated that these errors can be mitigated by accounting for uncertainty using a scale model, which we integrated into the ALDEx2 R package. This article provides new insights focusing on the application to transcriptomic analysis. We provide transcriptomic case studies demonstrating how scale models, rather than traditional normalizations, can reduce false positive and false negative rates in practice while enhancing the transparency and reproducibility of analyses. These scale models replace the need for dual cutoff approaches often used to address the disconnect between practical and statistical significance. We demonstrate the utility of scale models built based on known housekeeping genes in complex metatranscriptomic datasets. Thus this work provides guidance on how to incorporate scale into transcriptomic data sets.<\/jats:p>","DOI":"10.1093\/nargab\/lqaf108","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T10:52:40Z","timestamp":1755600760000},"source":"Crossref","is-referenced-by-count":10,"title":["Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5803-3380","authenticated-orcid":false,"given":"Gregory\u00a0B","family":"Gloor","sequence":"first","affiliation":[{"name":"University of Western Ontario Department of Biochemistry, , London \u00a0ON, N6A 5C1,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle\u00a0Pistner","family":"Nixon","sequence":"additional","affiliation":[{"name":"Geisinger Department of Population Health Sciences,\u00a0 , Danville, PA\u00a017822 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin\u00a0D","family":"Silverman","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, Pennsylvania State University , University Park, PA\u00a016802 ,","place":["United States"]},{"name":"Pennsylvania State University Department of Statistics, , University Park, PA\u00a016802 ,","place":["United States"]},{"name":"Pennsylvania State University Department of Medicine, , Hershey, PA\u00a017033 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"2025081906523089800_B1","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1002\/9781119976462.ch14","article-title":"Proportions, percentages, ppm: Do the molecular biosciences treat compositional data right?","volume-title":"Compositional Data Analysis: Theory and Applications","author":"Lovell","year":"2011"},{"key":"2025081906523089800_B2","doi-asserted-by":"crossref","unstructured":"Quinn TP, Erb I, Gloor G \u00a0et al. \u00a0A field guide for the compositional analysis of any-omics data. 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