Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling high-dimensional data by mixtures of factor analyzers
Computational Statistics & Data Analysis
Analyzing Gene Expression Time-Courses
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Clustering of time-course gene expression data using functional data analysis
Computational Biology and Chemistry
Clustering of change patterns using Fourier coefficients
Bioinformatics
Clustering of gene expression data based on shape similarity
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Functional Cluster Analysis via Orthonormalized Gaussian Basis Expansions and Its Application
Journal of Classification
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
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Longitudinal data is becoming increasingly common and various methods have been developed to analyze this type of data. Profiles from time-course gene expression studies, where cluster analysis plays an important role to identify groups of co-expressed genes over time, are investigated. A number of procedures have been used to cluster time-course gene expression data, however there are many limitations to the techniques previously described. An alternative approach is proposed, which aims to alleviate some of these limitations. The method exploits the connection between the linear mixed effects model and P-spline smoothing to simultaneously smooth the gene expression data to remove any measurement error/noise and cluster the expression profiles using finite mixtures of mixed effects models. This approach has a number of advantages, including decreased computation time and ease of implementation in standard software packages.