Observer-Biased Analysis of Gene Expression Profiles

Observer-Biased Analysis of Gene Expression Profiles

Paulo Fazendeiro (Instituto de Telecomunicações (IT), Portugal) and José Valente de Oliveira (University of Algarve, Portugal)
Copyright: © 2015 |Pages: 21
DOI: 10.4018/978-1-4666-6611-5.ch006
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Microarray generated gene expression data are characterized by their volume and by the intrinsic background noise. The main task of revealing patterns in gene expression data is typically carried out using clustering analysis, with soft clustering leading the more promising candidate methods. In this chapter, Fuzzy C-Means with a variable Focal Point (FCMFP) is exploited as the first stage in gene expression data analysis. FCMFP is inspired by the observation that the visual perception of a group of similar objects is (highly) dependent on the observer position. This metaphor is used to provide a new analysis insight, with different levels of granularity, over a gene expression dataset.
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A gene usually corresponds to a sequence used in the production of a specific protein or ribonucleic acid (RNA) molecule. It is a region of deoxyribonucleic acid (DNA) that controls a hereditary characteristic. A gene carries biological information in a form that must be copied and transmitted from each cell to all its progeny. Each gene has a fixed location on its chromosome and helps to specify a trait. Defective genes may cause diseases hence they need to be identified. Despite some evidences pointing that microarray technology is slowly being phased out in favor of several next-generation sequencing methods (Ozsolak & Milos, 2011; Wang, Gerstein, & Snyder, 2009) DNA microarrays are commonly being used in first-tier clinical testing (Riggs, 2014) and still are essential tools for various genomic studies, e.g. (Belfield, 2014; Sanmann, 2013; Nylund, 2013). This technique is providing a wealth of data on global patterns of gene expression. Currently, efforts are being made to describe and understand the global view of these patterns, i.e., trying to uncover the hidden structures in gene expression data.

Gene expression refers to transcription levels of genes. The expression level refers to the amount of messenger RNA (mRNA) in a gene, which is the transcription of an activated gene that is later translated into a protein. A wide range of approaches are being use to measure gene expression levels. These methods, which fall under the category of microarrays technology, include cDNA microarray (Schena et al., 1996a; Schena et al., 1996b) and oligonucleotide microarray (Fodor et al., 1993; Lipshutz et al., 2000). Gene expression profiling can also be performed using serial analysis of gene expression (SAGE) (Velculescu et al., 1997) and reverse transcription-polymerase chain reaction (RT-PCR) (Somogyi et al., 1995).

The analysis of microarrays generated data remains a quite challenging task. According to (Simon, 2008) gene expression profiling offers both a great opportunity for new kinds of investigation and great risk of error because it provides a high-dimensional read-out for each specimen assayed. The datasets are typically large with large background noise, cf. (Chu et al., 1998). The yeast cell cycle dataset analysed in this chapter is one relevant example of such datasets.

Clustering is usually the first step in gene expression data analysis (Jiang, Tang & Zhang, 2004). Apart from gene expression, clustering plays a major role in data mining applications such as information retrieval and text mining, web analysis, scientific data exploration, spatial database applications, CRM and marketing, image processing and recognition systems, medical diagnostics and computational biology, just to mention a few (de Oliveira & Pedrycz, 2007; Soowhan, Lee & Pedrycz, 2009; Ming, Kiong & Soong, 2011; Zhang & Lu, 2010; Chaira, 2011).

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