title: Reference point insensitive molecular data analysis.
abstract: 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.
  
  
  
  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.
  
  
  
  The R-package "zeroSum" 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 CONTACT: Michael.Altenbuchinger@ukr.de, Thorsten.Rehberg@ukr.de and Rainer.Spang@ukr.deSupplementary information: Supplementary material is available at Bioinformatics online.
authors.affiliation: Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Altenbuchinger
authors.affiliation: Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
authors.forename: T
authors.initials: T
authors.lastname: Rehberg
authors.affiliation: Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
authors.forename: H U
authors.initials: HU
authors.lastname: Zacharias
authors.affiliation: Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany.
authors.forename: F
authors.initials: F
authors.lastname: Stämmler
authors.affiliation: Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
authors.forename: K
authors.initials: K
authors.lastname: Dettmer
authors.affiliation: Department of Hematology and Oncology, Internal Medicine III, University Medical Center, Regensburg, Germany.
authors.forename: D
authors.initials: D
authors.lastname: Weber
authors.affiliation: Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany.
authors.forename: A
authors.initials: A
authors.lastname: Hiergeist
authors.affiliation: Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany.
authors.forename: A
authors.initials: A
authors.lastname: Gessner
authors.affiliation: Department of Hematology and Oncology, Internal Medicine III, University Medical Center, Regensburg, Germany.
authors.forename: E
authors.initials: E
authors.lastname: Holler
authors.affiliation: Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
authors.forename: P J
authors.initials: PJ
authors.lastname: Oefner
authors.affiliation: Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
authors.forename: R
authors.initials: R
authors.lastname: Spang
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000235:genetics / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D000069196:Gastrointestinal Microbiome / Q000235:genetics; D015964:Gene Expression Regulation, Bacterial; D006801:Humans; D055432:Metabolomics; D012984:Software