title: Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss).
abstract: Biomedical data obtained during cell experiments, laboratory animal research, or human studies often display a complex distribution. Statistical identification of subgroups in research data poses an analytical challenge. Here were introduce an interactive R-based bioinformatics tool, called "AdaptGauss". It enables a valid identification of a biologically-meaningful multimodal structure in the data by fitting a Gaussian mixture model (GMM) to the data. The interface allows a supervised selection of the number of subgroups. This enables the expectation maximization (EM) algorithm to adapt more complex GMM than usually observed with a noninteractive approach. Interactively fitting a GMM to heat pain threshold data acquired from human volunteers revealed a distribution pattern with four Gaussian modes located at temperatures of 32.3, 37.2, 41.4, and 45.4 °C. Noninteractive fitting was unable to identify a meaningful data structure. Obtained results are compatible with known activity temperatures of different TRP ion channels suggesting the mechanistic contribution of different heat sensors to the perception of thermal pain. Thus, sophisticated analysis of the modal structure of biomedical data provides a basis for the mechanistic interpretation of the observations. As it may reflect the involvement of different TRP thermosensory ion channels, the analysis provides a starting point for hypothesis-driven laboratory experiments.
authors.affiliation: DataBionics Research Group, University of Marburg, Hans-Meerwein-Straße, Marburg 35032, Germany. ultsch@informatik.uni-marburg.de.
authors.forename: Alfred
authors.initials: A
authors.lastname: Ultsch
authors.affiliation: DataBionics Research Group, University of Marburg, Hans-Meerwein-Straße, Marburg 35032, Germany. mthrun@informatik.uni-marburg.de.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Thrun
authors.affiliation: Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, Frankfurt am Main 60590, Germany. Hansen-Goos@med.uni-frankfurt.de.
authors.forename: Onno
authors.initials: O
authors.lastname: Hansen-Goos
authors.affiliation: Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, Frankfurt am Main 60590, Germany. j.loetsch@em.uni-frankfurt.de.|Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Project Group Translational Medicine and Pharmacology TMP, Theodor-Stern-Kai 7, Frankfurt am Main 60590, Germany. j.loetsch@em.uni-frankfurt.de.
authors.forename: Jörn
authors.initials: J
authors.lastname: Lötsch
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D005260:Female; D006358:Hot Temperature; D006801:Humans; D008297:Male; D008959:Models, Neurological; D059226:Nociceptive Pain / Q000378:metabolism* / Q000503:physiopathology; D017288:Pain Threshold; D013697:Thermosensing; D050051:Transient Receptor Potential Channels / Q000378:metabolism
chemical_list: D050051:Transient Receptor Potential Channels
keywords: R software; bioinformatics; data modeling; molecular mechanisms; pain