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Querying Co-regulated Genes on Diverse Gene Expression Datasets Via Biclustering

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Microarray Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1375))

Abstract

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.

“What we call chaos is just patterns we haven’t recognized. What we call random is just patterns we can’t decipher.”

— Chuck Palahniuk, Survivor

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Authors and Affiliations

  1. Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Mehmet Deveci, Onur Küçüktunç & Kemal Eren

  2. Biomedical Informatics, The Ohio State University, Columbus, OH, USA

    Doruk Bozdağ

  3. Computer Science and Engineering, Sabancı University, Istanbul, Turkey

    Kamer Kaya

  4. Biomedical Informatics, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA

    Ümit V. Çatalyürek

Authors
  1. Mehmet Deveci
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  2. Onur Küçüktunç
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  3. Kemal Eren
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  4. Doruk Bozdağ
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  5. Kamer Kaya
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  6. Ümit V. Çatalyürek
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Correspondence to Ümit V. Çatalyürek .

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Editors and Affiliations

  1. Department of Surgical and Medical Sciences, University “Magna Græcia” of Catanzaro, Catanzaro, Italy

    Pietro Hiram Guzzi

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Deveci, M., Küçüktunç, O., Eren, K., Bozdağ, D., Kaya, K., Çatalyürek, Ü.V. (2015). Querying Co-regulated Genes on Diverse Gene Expression Datasets Via Biclustering. In: Guzzi, P. (eds) Microarray Data Analysis. Methods in Molecular Biology, vol 1375. Humana Press, New York, NY. https://doi.org/10.1007/7651_2015_246

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  • DOI: https://doi.org/10.1007/7651_2015_246

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3172-9

  • Online ISBN: 978-1-4939-3173-6

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