Title

Preface

Keywords

Adjust rand index; Infinite mixture model; Inverse gamma distribution; Mcmc chain; Temporal expression profile

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.

Publication Date

1-1-2012

Publication Title

Multiscale Hydrologic Remote Sensing: Perspectives and Applications

Number of Pages

ix-x

Document Type

Editorial Material

Personal Identifier

scopus

DOI Link

https://doi.org/10.1201/b11279

Socpus ID

85060407361 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85060407361

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