• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.623-631
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• 2011

A major goal of time-course microarray data analysis is the detection of groups of genes that manifest similar expression patterns over time. The corresponding numerous cluster algorithms for clustering time-course microarray data have been developed. In this study, we proposed a clustering method based on the primary pharmacokinetic parameters in the pharmacokinetics study for assessment of pharmaceutical equivalents between two drug products. A real data and a simulation data was used to demonstrate the usefulness of the proposed method.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.575-584
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• 2004

We propose a Bayesian model-based approach using a mixture of Dirichlet processes model with discrete wavelet transform, for curve clustering in the microarray data with time-course gene expressions.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.13-32
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• 2006

Biologists are attempting to group genes based on the temporal pattern of gene expression levels. So far, a number of methods have been proposed for clustering microarray data. However, the results of clustering depends on the genes selection, therefore the gene selection with significant expression difference is also very important to cluster for microarray data. Thus, this paper present the results of broad comparative studies to time course microarray data by considering methods of gene selection, clustering and cluster validation.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.415-422
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• 2012

For statistical microarray data analysis, clustering analysis is a useful exploratory technique and offers the promise of simultaneously studying the variation of many genes. However, most of the proposed clustering methods are not rigorously solved for a time-course microarray data cluster and for a fitting time covariate; therefore, a statistical method is needed to form a cluster and represent a linear trend of each cluster for each gene. In this research, we developed a modified hierarchical mixture of an experts model to suggest clustering data and characterize each cluster using a linear mixed effect model. The feasibility of the proposed method is illustrated by an application to the human fibroblast data suggested by Iyer et al. (1999).

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.335-348
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• 2005

The rapid development of microarray technologies enabled the monitoring of expression levels of thousands of genes simultaneously. Recently, the time course gene expression data are often measured to study dynamic biological systems and gene regulatory networks. For the data, biologists are attempting to group genes based on the temporal pattern of their expression levels. We apply the consensus clustering algorithm to a time course gene expression data in order to infer statistically meaningful information from the measurements. We evaluate each of consensus clustering and existing clustering methods with various validation measures. In this paper, we consider hierarchical clustering and Diana of existing methods, and consensus clustering with hierarchical clustering, Diana and mixed hierachical and Diana methods and evaluate their performances on a real micro array data set and two simulated data sets.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.389-399
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• 2013

This article introduces Gaussian process regression and shows its application with time-course microarray gene expression data. Gene screening for yeast cell cycle microarray expression data is accomplished with a ratio of log marginal likelihood that uses Gaussian process regression with a squared exponential covariance kernel function. Gaussian process regression fitting with each gene is done and shown with the nine top ranking genes. With the screened data the Gaussian model-based clustering is done and its silhouette values are calculated for cluster validity.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.39-42
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• 2006

We propose a Bayesian model-based approach using a mixture of Dirichlet processes model with discrete wavelet transform, for curve clustering in the microarray data with time-course gene expressions.

• Molecules and Cells
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• v.26 no.4
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• pp.338-343
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• 2008

An embryonic stem cell is a powerful tool for investigation of early development in vitro. The study of embryonic stem cell mediated neuronal differentiation allows for improved understanding of the mechanisms involved in embryonic neuronal development. We investigated expression profile changes using time course cDNA microarray to identify clues for the signaling network of neuronal differentiation. For the short time course microarray data, pattern analysis based on the quadratic regression method is an effective approach for identification and classification of a variety of expressed genes that have biological relevance. We studied the expression patterns, at each of 5 stages, after neuronal induction at the mRNA level of embryonic stem cells using the quadratic regression method for pattern analysis. As a result, a total of 316 genes (3.1%) including 166 (1.7%) informative genes in 8 possible expression patterns were identified by pattern analysis. Among the selected genes associated with neurological system, all three genes showing linearly increasing pattern over time, and one gene showing decreasing pattern over time, were verified by RT-PCR. Therefore, an increase in gene expression over time, in a linear pattern, may be associated with embryonic development. The genes: Tcfap2c, Ttr, Wnt3a, Btg2 and Foxk1 detected by pattern analysis, and verified by RT-PCR simultaneously, may be candidate markers associated with the development of the nervous system. Our study shows that pattern analysis, using the quadratic regression method, is very useful for investigation of time course cDNA microarray data. The pattern analysis used in this study has biological significance for the study of embryonic stem cells.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.371-379
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• 2005

In this paper, we propose a pattern consistency index for detecting heterogeneous time series that deviate from the representative pattern of each cluster in clustering time course gene expression data using the Pearson correlation coefficient. We examine its usefulness by applying this index to serum time course gene expression data from microarrays.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.275-290
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• 2008

The size of microarray gene expression data is very big and its observation process is also very complex. Thus missing values are frequently occurred. In this paper we propose the sequential partial least squares(SPLS) regression fitting method to estimate missing values for time course gene expression data that has correlations among observations over time points. The SPLS method is to combine the sequential technique with the partial least squares(PLS) regression fitting method. The usefulness of method proposed is evaluated through some simulation study for three yeast time course data.