By Susmita Datta, Bart J. A. Mertens
This booklet provides an summary of computational and statistical layout and research of mass spectrometry-based proteomics, metabolomics, and lipidomics info. This contributed quantity offers an creation to the detailed features of statistical layout and research with mass spectrometry information for the hot omic sciences. The textual content discusses universal points of layout and research among and throughout all (or so much) sorts of mass spectrometry, whereas additionally delivering specific examples of software with the most typical different types of mass spectrometry. additionally lined are purposes of computational mass spectrometry not just in medical research but in addition within the interpretation of omics information in plant biology studies.
Omics examine fields are anticipated to revolutionize biomolecular learn via the power to concurrently profile many compounds inside both sufferer blood, urine, tissue, or different organic samples. Mass spectrometry is likely one of the key analytical recommendations utilized in those new omic sciences. Liquid chromatography mass spectrometry, time-of-flight information, and Fourier remodel mass spectrometry are yet a variety of the dimension structures on hand to the fashionable analyst. therefore in sensible proteomics or metabolomics, researchers won't basically be faced with new excessive dimensional facts types—as against the frequent information constructions in additional classical genomics—but additionally with nice edition among specified different types of mass spectral measurements derived from diversified systems, which could complicate analyses, comparability, and interpretation of results.
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Extra resources for Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry
Due to the norm-preserving nature of warping in this representation, the pinching effect is completely avoided. 2 Pairwise Alignment Procedure With this mathematical foundation, the pairwise alignment of chromatograms can be accomplished as follows. Let f1 , f2 by functional forms of the two given spectra and let q1 , q2 be the corresponding SRSFs. q1 ; /k2 : (5) This minimization is performed in practice using a numerical approach called the dynamic programming algorithm . We have already mentioned (in Lemma 2) that the use of SRSFs and L2 norm satisfies the invariance property from Sect.
The problem of functional data alignment has been studied by several authors, including [4, 6, 9, 14]. In our approach, the actual alignment becomes an optimization problem with a novel objective function which, in turn, is derived using ideas from functional information geometry. Any two chromatograms are aligned by minimizing a distance between them; this distance can be seen as an extension of the classical nonparametric Fisher-Rao distance, derived originally for comparing probability density functions, to more general class of functions.
Another experiment involves a set of eight chromatograms shown in the top panel in Fig. 11. In this data, some of the major peaks are not aligned, especially in the domain range Œ15; 25. The outcome of Algorithm 1 on this data is shown in the second row where the peaks appear to be sharply aligned throughout the spectrum. To emphasize the quality of alignment we look at a couple of smaller intervals in the spectrum more carefully. The zoom-ins of these smaller regions are shown in the last two row of the figure.
Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry by Susmita Datta, Bart J. A. Mertens