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Clustering of Time-course Gene Expression Data Using Functional Data Analysis by Joon Jin Song; Ho-Jin Lee; Jeffrey S. Morris; Sanghoon Kang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Clustering of Time-course Gene Expression Data Using Functional Data Analysis about?

Clustering of gene expression data collected across time is receiving growing attention in the biological literature since time-course experiments allow one to understand dynamic biological processes and identify genes governed by the same processes. It is believed that genes demonstrating similar expression profiles over time might give an informative insight into how underlying biological mechanisms work. In this paper, we propose a method based on functional data analysis (FNDA) to cluster time-dependent gene expression profiles. Consideration of clustering problems using the FNDA setting provides ways to take time dependency into account by using basis function expansion to describe the partially observed curves. We also discuss how to choose the number of bases in the basis function expansion in FNDA. A synthetic cycle data and a real data are used to demonstrate the proposed method and some comparisons between the proposed and existing approaches using the adjusted Rand indices are made.

Who reads Clustering of Time-course Gene Expression Data Using Functional Data Analysis?

Author

Joon Jin Song; Ho-Jin Lee; Jeffrey S. Morris; Sanghoon Kang

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 1476-9271)

Published

2007

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)