DUGi

Application of Compositional Models for Glycan HILIC Data

http://dugi.udg.edu/item/http:@@@@hdl.handle.net@@2072@@273645

Glycoconjugates constitute a major class of biomolecules which include glycoproteins, glycosphingolipidsand proteoglycans. The enzymatic process in which glycans (sugar chains) are linked toproteins or lipids is called glycosylation. Glycosylation is involved in many biological processes, bothphysiological and pathological, inlcuding host-pathogen interactions, tumour invasion, cell traffickingand signalling. Changes in glycan structure are thought be be at least partly responsible for the developmentof inflammation, infection, arteriosclerosis, immune defects and autoimmunity. Such changeshave been observed in human diseases such as diabetes mellitus, rheumatoid arthritis and Alzheimer’sDisease. Aberrant patterns of glycosylation are also a universal feature of cancer cells. The field ofglycobiology thus shows great potential for the discovery of glycan biomarkers for disease diagnosisand prognosis.Here we focus specifically on N-glycans, that is, glycans attached to protein molecules via anitrogen atom. This class of glycans is the best characterized. High-throughput HILIC analysis isa well-established technique for the separation and quantification of N-linked glycans released fromglycoproteins. HILIC analysis quantifies the N-glycan structures in serum via a chromatogram, whichis subsequently standardized and integrated. The generated data for each sample is a set of relativeHILIC peak areas and as a result, the data is compositional. To-date, most statistical analyses of theseglycan data fail to account for their compositional nature.We compare and contrast three compositional data models for the glycan HILIC data: the Dirichlet,Nested Dirichlet and Logistic Normal models, with the intention of providing tools for the statisticalanalysis of compositional data analysis in the glycobiology field. We use these three models forclassification of disease/control cases in ovarian and lung cancer diagnosis applications. We discussand compare these models in terms of their classification performance and goodness-of-fit

Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada