Microarray Data Analysis

Mehmet Deveci, Onur Küçüktunç, Kemal Eren, Doruk Bozdağ, Kamer Kaya, Ümit V. Çatalyürek, Deveci, Mehmet, Küçüktunç, Onur, Eren, Kemal, Bozdağ, Doruk, Kaya, Kamer, Çatalyürek, Ümit V.

Rapid development and increasing popularity of gene expression microarrays have resulted in a number of studies on the discovery of co-regulated genes. One important way of discovering such co-regulations is the query-based search since gene co-expressions may indicate a shared role in a biological process. Although there exist promising query-driven search methods adapting clustering, they fail to capture many genes that function in the same biological pathway because microarray datasets are fraught with spurious samples or samples of diverse origin, or the pathways might be regulated under only a subset of samples. On the other hand, a class of clustering algorithms known as biclustering algorithms which simultaneously cluster both the items and their features are useful while analyzing gene expression data, or any data in which items are related in only a subset of their samples. This means that genes need not be related in all samples to be clustered together. Because many genes only interact under specific circumstances, biclustering may recover the relationships that traditional clustering algorithms can easily miss. In this chapter, we briefly summarize the literature using biclustering for querying co-regulated genes. Then we present a novel biclustering approach and evaluate its performance by a thorough experimental analysis.