Clustering of time-course gene expression data using functional data analysis

Authors:
Joon Jin Song;Ho-Jin Lee;Jeffrey S. Morris;Sanghoon Kang
Affiliations:
Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR 72701, USA;Schering-Plough Research Institute, 2015 Galloping Hill Road, K-15-2-2125, Kenilworth, NJ 07033-1300, USA;Department of Biostatistics and Applied Mathematics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA;Institute for Environmental Genomics, The University of Oklahoma, Norman, OK 73019, USA
Venue:
Computational Biology and Chemistry
Year:
2007

Citing 1
Cited 7

A fully Bayesian model to cluster gene-expression profiles

Bioinformatics

Research article: Optimal classification for time-course gene expression data using functional data analysis

Computational Biology and Chemistry
Brief communication: Classification for high-throughput data with an optimal subset of principal components

Computational Biology and Chemistry
Clustering of temporal gene expression data by regularized spline regression and an energy based similarity measure

Pattern Recognition
Temporal gene expression profiles reconstruction by support vector regression and framelet kernel

ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

Computer Methods and Programs in Biomedicine
Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data

Computational Statistics & Data Analysis
Enhanced classification for high-throughput data with an optimal projection and hybrid classifier

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.