CLIC: clustering analysis of large microarray datasets with individual dimension-based clustering

Abstract

Large microarray data sets have recently become common. However, most available clustering methods do not easily handle large microarray data sets due to their very large computational complexity and memory requirements. Furthermore, typical clustering methods construct oversimplified clusters that ignore subtle but meaningful changes in the expression patterns present in large microarray data sets. It is necessary to develop an efficient clustering method that identifies both absolute expression differences and expression profile patterns in different expression levels for large microarray data sets. This study presents CLIC, which meets the requirements of clustering analysis particularly but not limited to large microarray data sets. CLIC is based on a novel concept in which genes are clustered in individual dimensions first and in which the ordinal labels of clusters in each dimension are then used for further full dimension-wide clustering. CLIC enables iterative sub-clustering into more homogeneous groups and the identification of common expression patterns among the genes separated in different groups due to the large difference in the expression levels. In addition, the computation of clustering is parallelized, the number of clusters is automatically detected, and the functional enrichment for each cluster and pattern is provided. CLIC is freely available at http://gexp2.kaist.ac.kr/clic.

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

“…In this paper, the biclustering algorithm uses a single column vector clustering to generate the bicluster seeds; the bicluster seeds are extended through GRASP. The single column vector clustering is similar to individual dimension clustering (CLIC) [17] method. CLIC [17] is an effective clustering method that has been tested on larger microarray datasets.…”

“…The single column vector clustering is similar to individual dimension clustering (CLIC) [17] method. CLIC [17] is an effective clustering method that has been tested on larger microarray datasets. CLIC [17] clustering is more effective than traditional K-means clustering methods.…”

“…CLIC [17] is an effective clustering method that has been tested on larger microarray datasets. CLIC [17] clustering is more effective than traditional K-means clustering methods. The single column vector clustering is a class of genes with similar expressions under each condition, and more bicluster seeds can be found, each run found that the bicluster seeds are the same.…”

Biclustering for the comprehensive search of correlated gene expression patterns using clustered seed expansion

“…Individual dimension-based clustering is employed to collect genes with similar expression levels in each decomposed vectors. It is an approach that is similar to that used in the Clustering analysis of Large microarray datasets with Individual dimension-based Clustering (CLIC) algorithm [29]. CLIC uses individual dimension-based clustering method to cluster larger microarray datasets efficiently.…”

Estimating the number of data clusters via the contrast statistic

“…Considerable interest is studies in genes cauterization [11,12]. Thus, study [13] presents method of clustering analysis of large micro array datasets with individual dimension-based clustering (CLIC), which meets the requirements of clustering analysis particularly but not limited to large micro-array data sets. CLIC is based on a novel concept in which genes are clustered in individual dimensions first and in which the ordinal labels of clusters in each dimension are then used for further full dimension-wide clustering.…”