Reference point insensitive molecular data analysis
| Autor: | Michael Altenbuchinger, Daniela Weber, Frank Stämmler, Katja Dettmer, Thorsten Rehberg, Peter J. Oefner, Rainer Spang, Ernst Holler, Andreas Hiergeist, André Gessner, Helena U. Zacharias |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Statistics and Probability Computer science 610 Medizin 01 natural sciences Biochemistry Measure (mathematics) 010104 statistics & probability 03 medical and health sciences Metabolomics Lasso (statistics) Statistics Humans Computer Simulation 0101 mathematics Coordinate descent Molecular Biology VERSUS-HOST-DISEASE STEM-CELL TRANSPLANTATION LOGISTIC-REGRESSION VARIABLE SELECTION C-MYC REGULARIZATION MICROBIOME LASSO Biomedicine ddc:610 Bacteria business.industry Computational Biology Regression analysis Observable Pattern recognition Gene Expression Regulation Bacterial Regression Computer Science Applications Gastrointestinal Microbiome Computational Mathematics 030104 developmental biology Computational Theory and Mathematics Artificial intelligence business Algorithms Software |
| Zdroj: | Bioinformatics (Oxford, England). 33(2) |
| ISSN: | 1367-4811 |
| Popis: | 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. Supplementary information Supplementary material is available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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