Clustering of Multiple Microarray Experiments Using Information Integration

In this article, we study two microarray data integration techniques and describe how they can be applied and validated on a set of independent, but biologically related, microarray data sets in order to derive consistent and relevant clustering results. First, we present a cluster integration approach, which combines the information containing in multiple data sets at the level of expression or similarity matrices, and then applies a clustering algorithm on the combined matrix for subsequent analysis. Second, we propose a technique for the integration of multiple partitioning results. The performance of the proposed cluster integration algorithms is evaluated on time series expression data using two clustering algorithms and three cluster validation measures. We also propose a modified version of the Figure of Merit (FOM) algorithm, which is suitable for estimating the predictive power of clustering algorithms when they are applied to multiple expression data sets. In addition, an improved version of the well-known connectivity measure is introduced to achieve a more objective evaluation of the connectivity performance of clustering algorithms.