{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T15:57:12Z","timestamp":1762444632893},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2016,9,15]],"date-time":"2016-09-15T00:00:00Z","timestamp":1473897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case, the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here, we show that reference point discrepancies compromise the performance of regression models like the LASSO. As an alternative, we suggest zero-sum regression for a reference point insensitive analysis. We show that zero-sum regression is superior to the LASSO in case of a poor choice of reference point both in simulations and in an application that integrates intestinal microbiome analysis with metabolomics. Moreover, we describe a novel coordinate descent based algorithm to fit zero-sum elastic nets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and Implementation<\/jats:title><jats:p>The R-package \u201czeroSum\u201d can be downloaded at https:\/\/github.com\/rehbergT\/zeroSum. Moreover, we provide all R-scripts and data used to produce the results of this manuscript as Supplementary Material.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary material is available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btw598","type":"journal-article","created":{"date-parts":[[2016,9,16]],"date-time":"2016-09-16T00:10:35Z","timestamp":1473984635000},"page":"219-226","source":"Crossref","is-referenced-by-count":27,"title":["Reference point insensitive molecular data analysis"],"prefix":"10.1093","volume":"33","author":[{"given":"M","family":"Altenbuchinger","sequence":"first","affiliation":[{"name":"Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany"}]},{"given":"T","family":"Rehberg","sequence":"additional","affiliation":[{"name":"Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany"}]},{"given":"H U","family":"Zacharias","sequence":"additional","affiliation":[{"name":"Institute of Functional Genomics, University of Regensburg, Regensburg, Germany"}]},{"given":"F","family":"St\u00e4mmler","sequence":"additional","affiliation":[{"name":"Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany"},{"name":"Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany"}]},{"given":"K","family":"Dettmer","sequence":"additional","affiliation":[{"name":"Institute of Functional Genomics, University of Regensburg, Regensburg, Germany"}]},{"given":"D","family":"Weber","sequence":"additional","affiliation":[{"name":"Department of Hematology and Oncology, Internal Medicine III, University Medical Center, Regensburg, Germany"}]},{"given":"A","family":"Hiergeist","sequence":"additional","affiliation":[{"name":"Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany"}]},{"given":"A","family":"Gessner","sequence":"additional","affiliation":[{"name":"Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany"}]},{"given":"E","family":"Holler","sequence":"additional","affiliation":[{"name":"Department of Hematology and Oncology, Internal Medicine III, University Medical Center, Regensburg, Germany"}]},{"given":"P J","family":"Oefner","sequence":"additional","affiliation":[{"name":"Institute of Functional Genomics, University of Regensburg, Regensburg, Germany"}]},{"given":"R","family":"Spang","sequence":"additional","affiliation":[{"name":"Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany"}]}],"member":"286","published-online":{"date-parts":[[2016,9,15]]},"reference":[{"key":"2023020204310669300_btw598-B1","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1073\/pnas.0906112107","article-title":"The bacterial signal indole increases epithelial-cell tight-junction resistance and attenuates indicators of inflammation","volume":"107","author":"Bansal","year":"2010","journal-title":"Proc. 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