Autore
Arima, Serena

Titolo
A Bayesian hierarchical model for identifying epitopes in peptide microarray data
Periodico
Università degli Studi di Roma "La Sapienza" - Dipartimento di Studi Geoeconomici, Linguistici, Statistici e Storici per l'Analisi regionale. Working papers
Anno: 2010 - Fascicolo: 71 - Pagina iniziale: 1 - Pagina finale: 23

Peptide microarray immunoassay (MIA) is a novel technology that enables researchers to map a large number of proteomic measurements at a peptide level, providing information regarding the relationship between antibody response and clinical sensitivity. Similarly to genomic sequences proteomic sequences are made of consecutive amino acids which in turn are functionally grouped into larger units called peptides. A peptide microarray immunoassay is designed to contain in each spot few consecutive overlapping peptides covering the whole protein sequence. Epitope regions are formed by one or more subsets of spots corresponding to consecutive peptides. The microarray technology aims at detecting specific antibody binding to epitope regions of an antigen. The statistical analysis of MIA data represents a new challenge mainly due to the presence of this structural interior spot dependence. We propose a new flexible Bayesian hierarchical model which allows one to detect recognized peptides and bound epitope regions in a single framework, taking into account the dependence between probes through a suitable latent Markov structure. Model extensions for embedding different patients and experimental conditions have been introduced and criteria to compare binding regions have been proposed. Model criticism via posterior predictive checks is discussed. The proposed model framework is illustrated using peptide microarray data from a recent study aimed to evaluate the modifications of immune responses against ovalbumin in patients affected by IgE and IgG4 mediated egg allergy. A simulation study shows that the proposed model is more powerful and robust in terms of epitope detection than simpler models overlooking some of the dependence structure.




Esportazione dati in Refworks (solo per utenti abilitati)