Nearest hyperplane distance neighbor clustering algorithm applied to gene co-expression analysis in Alzheimer's disease

Abstract

Microarray analysis can contribute considerably to the understanding of biologically significant cellular mechanisms that yield novel information regarding co-regulated sets of gene patterns. Clustering is one of the most popular tools for analyzing DNA microarray data. In this paper, we present an unsupervised clustering algorithm based on the K-local hyperplane distance nearest-neighbor classifier (HKNN). We adapted the well-known nearest neighbor clustering algorithm for use with hyperplane distance. The result is a simple and computationally inexpensive unsupervised clustering algorithm that can be applied to high-dimensional data. It has been reported that the NFkB1 gene is progressively over-expressed in moderate-to-severe Alzheimer's disease (AD) cases, and that the NF-kB complex plays a key role in neuroinflammatory responses in AD pathogenesis. In this study, we apply the proposed clustering algorithm to identify co-expression patterns with the NFkB1 in gene expression data from hippocampal tissue samples. Finally, we validate our experiments with biomedical literature search.