title: Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features.
abstract: Mass spectrometry is an important analytical technology in metabolomics. After the initial feature detection and alignment steps, the raw data processing results in a high-dimensional data matrix of mass spectral features, which is then subjected to further statistical analysis. Univariate tests like Student's t-test and Analysis of Variances (ANOVA) are hypothesis tests, which aim to detect differences between two or more sample classes, e.g., wildtype-mutant or between different doses of treatments. In both cases, one of the underlying assumptions is the independence between metabolic features. However, in mass spectrometry, a single metabolite usually gives rise to several mass spectral features, which are observed together and show a common behavior. This paper suggests to group the related features of metabolites with CAMERA into compound spectra, and then to use a multivariate statistical method to test whether a compound spectrum (and thus the actual metabolite) is differential between two sample classes. The multivariate method is first demonstrated with an analysis between wild-type and an over-expression line of the model plant Arabidopsis thaliana. For a quantitative evaluation data sets with a simulated known effect between two sample classes were analyzed. The spectra-wise analysis showed better detection results for all simulated effects.
authors.affiliation: Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry , Halle , Germany ; Institute of Computer Science, Martin Luther University Halle-Wittenberg , Halle , Germany.
authors.forename: Diana
authors.initials: D
authors.lastname: Trutschel
authors.affiliation: Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry , Halle , Germany.
authors.forename: Stephan
authors.initials: S
authors.lastname: Schmidt
authors.affiliation: Institute of Computer Science, Martin Luther University Halle-Wittenberg , Halle , Germany ; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , Leipzig , Germany.
authors.forename: Ivo
authors.initials: I
authors.lastname: Grosse
authors.affiliation: Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry , Halle , Germany.
authors.forename: Steffen
authors.initials: S
authors.lastname: Neumann
keywords: hypothesis tests; mass spectrometry; metabolomics; multivariate analysis; statistics