Paper Info
Title
Gene Expression Clustering with Functional Mixture Models
Abstract
We propose a functional mixture model for simultaneous clustering and alignment of sets of curves measured on a discrete time grid. The model is specifically tailored to gene expression time course data. Each functional cluster center is a nonlinear combination of solutions of a simple linear differential equation that describes the change of individual mRNA levels when the synthesis and decay rates are constant. The mixture of continuous time parametric functional forms allows one to (a) account for the heterogeneity in the observed profiles, (b) align the profiles in time by estimating real-valued time shifts, (c) capture the synthesis and decay of mRNA in the course of an experiment, and (d) regularize noisy profiles by enforcing smoothness in the mean curves. We derive an EM algorithm for estimating the parameters of the model, and apply the proposed approach to the set of cycling genes in yeast. The experiments show consistent improvement in predictive power and within cluster variance compared to regular Gaussian mixtures.
Year
Venue
Keywords
2003
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16
linear differential equation,mixture model,discrete time,gene expression,decay rate
Field
DocType
Volume
Mathematical optimization,Nonlinear system,Expectation–maximization algorithm,Linear differential equation,Gaussian,Parametric statistics,Discrete time and continuous time,Cluster analysis,Mathematics,Mixture model
Conference
16
ISSN
Citations 
PageRank 
1049-5258
4
0.52
References 
Authors
4
4
Name
Order
Citations
PageRank
Darya Chudova111110.99
Christopher Hart2162.19
Eric Mjolsness31058140.00
Padhraic Smyth471481451.38