Using Dynamic Template Based Clustering to Analyze Time Series Microarray Data
Date Issued
2008
Date
2008
Author(s)
Kuo, Yu-Ho
Abstract
Microarray is a high-throughput technology for investigating gene expression. There are two major kinds of experiment designs in Microarray, one is case control study and another is time series study. Clustering methods are developed in order to analyze microarray data. Clustering can help to discover similar samples or co-related genes according to expression profiles of samples or genes. Traditional clustering methods are not designed for analyzing time series therefore are easy to miss information or misclassify. Although there exist several clustering method for time series, these clustering methods is not suitable for all the condition. We create a new time series clustering Gap statistic and Template based clustering (GT-clustering) for analyzing time series microarray data in all condition (not matter long time series or short time series). GT-clustering designs templates for clustering by using Gap statistic. Besides, binomial test is applied to identify the significant clusters. In this study, the algorithm is tested in simulation data and published data and compared the result with a published algorithm.
Subjects
Microarray
gene expression
time series
clustering
gap statistic
binomial test
Type
thesis
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