M Altenbuchinger1, T Rehberg1, H U Zacharias2, F Stämmler1,3, K Dettmer2, D Weber4, A Hiergeist3, A Gessner3, E Holler4, P J Oefner2, R Spang1. 1. Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany. 2. Institute of Functional Genomics, University of Regensburg, Regensburg, Germany. 3. Institute of Clinical Microbiology and Hygiene, University Medical Center, Regensburg, Germany. 4. Department of Hematology and Oncology, Internal Medicine III, University Medical Center, Regensburg, Germany.
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: 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.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: 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: Michael Altenbuchinger; Antoine Weihs; John Quackenbush; Hans Jörgen Grabe; Helena U Zacharias Journal: Biochim Biophys Acta Gene Regul Mech Date: 2019-10-19 Impact factor: 4.490
Authors: Michael Altenbuchinger; Helena U Zacharias; Stefan Solbrig; Andreas Schäfer; Mustafa Büyüközkan; Ulla T Schultheiß; Fruzsina Kotsis; Anna Köttgen; Rainer Spang; Peter J Oefner; Jan Krumsiek; Wolfram Gronwald Journal: Sci Rep Date: 2019-09-27 Impact factor: 4.379
Authors: Kevin Y X Wang; Gulietta M Pupo; Varsha Tembe; Ellis Patrick; Dario Strbenac; Sarah-Jane Schramm; John F Thompson; Richard A Scolyer; Samuel Muller; Garth Tarr; Graham J Mann; Jean Y H Yang Journal: NPJ Digit Med Date: 2022-07-04
Authors: M Altenbuchinger; P Schwarzfischer; T Rehberg; J Reinders; Ch W Kohler; W Gronwald; J Richter; M Szczepanowski; N Masqué-Soler; W Klapper; P J Oefner; R Spang Journal: Bioinformatics Date: 2017-07-15 Impact factor: 6.937
Authors: Jörg Reinders; Michael Altenbuchinger; Katharina Limm; Philipp Schwarzfischer; Tamara Scheidt; Lisa Strasser; Julia Richter; Monika Szczepanowski; Christian G Huber; Wolfram Klapper; Rainer Spang; Peter J Oefner Journal: Sci Rep Date: 2020-05-12 Impact factor: 4.379