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Erschienen in:
Clustering Large Data Set: An Applied Comparative Study Kapitel lesen Erstes Kapitel lesen

2012 | OriginalPaper | Buchkapitel

Bayesian Methods for Time Course Microarray Analysis: From Genes’ Detection to Clustering

verfasst von : Claudia Angelini, Daniela De Canditiis, Marianna Pensky

Erschienen in: Advanced Statistical Methods for the Analysis of Large Data-Sets

Verlag: Springer Berlin Heidelberg

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Abstract

Time-course microarray experiments are an increasingly popular approach for understanding the dynamical behavior of a wide range of biological systems. In this paper we discuss some recently developed functional Bayesian methods specifically designed for time-course microarray data. The methods allow one to identify differentially expressed genes, to rank them, to estimate their expression profiles and to cluster the genes associated with the treatment according to their behavior across time. The methods successfully deal with various technical difficulties that arise in this type of experiments such as a large number of genes, a small number of observations, non-uniform sampling intervals, missing or multiple data and temporal dependence between observations for each gene. The procedures are illustrated using both simulated and real data.

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Vorheriges Kapitel Joint Clustering and Alignment of Functional Data: An Application to Vascular Geometries
Nächstes Kapitel Longitudinal Analysis of Gene Expression Profiles Using Functional Mixed-Effects Models
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Metadaten
Titel
Bayesian Methods for Time Course Microarray Analysis: From Genes’ Detection to Clustering
verfasst von
Claudia Angelini
Daniela De Canditiis
Marianna Pensky
Copyright-Jahr
2012
Verlag
Springer Berlin Heidelberg
Buch
Advanced Statistical Methods for the Analysis of Large Data-Sets

Print ISBN: 978-3-642-21036-5

Electronic ISBN: 978-3-642-21037-2

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-3-642-21037-2_5