{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-reproducible-effect-size-is-more-useful","title":"A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets","arxiv_id":"1809.02623","date":"2019-05-13","proceeding":null,"authors":[],"abstract":"Motivation: P values derived from the null hypothesis significance testing\nframework are strongly affected by sample size, and are known to be\nirreproducible in underpowered studies, yet no suitable replacement has been\nproposed. Results: Here we present implementations of non-parametric\nstandardized median effect size estimates, dNEF, for high-throughput sequencing\ndatasets. Case studies are shown for transcriptome and tag-sequencing datasets.\nThe dNEF measure is shown to be more reproducible and robust than P values and\nrequires sample sizes as small as 3 to reproducibly identify differentially\nabundant features. Availability: Source code and binaries freely available at:\nhttps://bioconductor.org/packages/ALDEx2.html , omicplotR, and\nhttps://github.com/ggloor/CoDaSeq .","url_abs":"http://arxiv.org/abs/1809.02623v2","url_pdf":"http://arxiv.org/pdf/1809.02623v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-reproducible-effect-size-is-more-useful","repo_url":"https://github.com/ggloor/CoDaSeq","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"tag","task_name":"TAG"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}