Pattern Discovery in Gene Expression Data

Pattern Discovery in Gene Expression Data

Gráinne Kerr (Dublin City University, Ireland), Heather Ruskin (Dublin City University, Ireland) and Martin Crane (Dublin City University, Ireland)
DOI: 10.4018/978-1-59904-982-3.ch003
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Abstract

Microarray technology1 provides an opportunity to monitor mRNA levels of expression of thousands of genes simultaneously in a single experiment. The enormous amount of data produced by this high throughput approach presents a challenge for data analysis: to extract meaningful patterns, to evaluate its quality, and to interpret the results. The most commonly used method of identifying such patterns is cluster analysis. Common and sufficient approaches to many data-mining problems, for example, Hierarchical, K-means, do not address well the properties of “typical” gene expression data and fail, in significant ways, to account for its profile. This chapter clarifies some of the issues and provides a framework to evaluate clustering in gene expression analysis. Methods are categorised explicitly in the context of application to data of this type, providing a basis for reverse engineering of gene regulation networks. Finally, areas for possible future development are highlighted.
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Introduction

A fundamental factor of function in a living cell is the abundance of proteins present at a molecular level, that is, its proteome. The variation between proteomes of different cells is often used to explain differences in phenotype and cell function. Crucially, gene expression is the set of reactions that controls the level of messenger RNA (mRNA) in the transcriptome, which in turn maintains the proteome of a given cell. The transcriptome is never synthesized de novo; instead, it is maintained by gene expression replacing mRNAs that have been degraded, with changes in composition brought about by switching different sets of genes on and off. To understand the mechanisms of cells, involved in a given biological process, it is necessary to measure and compare gene expression levels in different biological phases, body tissues, clinical conditions, and organisms. Information on the set of genes expressed, in a particular biological process, can be used to characterise unknown gene function, identify targets for drug treatments, determine effects of treatment on cell function, and understand molecular mechanisms involved.

DNA microarray technology has advanced rapidly over the past decade, although the concept itself is not new (Friemert, Erfle, & Strauss, 1989; Gress, Hoheisel, Sehetner, & Leahrach 1992). It is now possible to measure the expression of an entire genome simultaneously, (equivalent to the collection and examination of data from thousands of single gene experiments). Components of the system technology can be divided into: (1) Sample preparation, (2) Array generation and sample analysis, and (3) Data handling and interpretation. The focus of this chapter is on the third of these.

Microarray technology utilises base-pairing hybridisation properties of nucleic acids, whereby one of the four base nucleotides (A, T, G, C) will bind with only one of the four base ribonucleotides (A, U, G, C: pairing = A – U, T – A, C – G, G - C). Thus, a unique sequence of DNA that characterises a gene will bind to a unique mRNA sequence. Synthesized DNA molecules, complementary to known mRNA, are attached to a solid surface, referred to as probes. These are used to measure the quantity of specific mRNA of interest that is present in a sample (the target). The molecules in the target are labelled, and a specialised scanner is used to measure the amount of hybridisation (intensity) of the target at each probe. Gene intensity values are recorded for a number of microarray experiments typically carried out for targets derived under various experimental conditions (Figure 1). Secondary variables (covariates) that affect the relationship between the dependent variable (experimental condition) and independent variables of primary interest (gene expression) include, for example, age, disease, and geography among others, and can also be measured.

Figure 1.

mRNA is extracted from a transcriptome of interest, (derived from cells grown under precise experimental conditions). Each mRNA sample is hybridised to a reference microarray. The gene intensity values for each experiment are then recorded.

An initial cluster analysis step is applied to gene expression data to search for meaningful informative patterns and dependencies among genes. These provide a basis for hypothesis testing--the basic assumption is that genes, showing similar patterns of expression across experimental conditions, may be involved in the same underlying cellular mechanism. For example, Alizadeh, Eisen, Davis, Ma, Lossos, Rosenwald, Boldrick, Sabet, Tran, Yu, Powell, Yang, Marti, Moore, Hudson Jr, Lu, Lewis, Tibshirani, Sherlock, Chan, Greiner, Weisenburger, Armitage, Warnke, Levy, Wilson, Grever, Byrd, Botstein, Brown, and Staudt (2000) used a hierarchical clustering technique, applied to gene expression data derived from diffuse large B-cell lymphomas (DLBCL), to identify two molecularly distinct subtypes. These had gene expression patterns, indicative of different stages of B-cell differentiation--germinal centre B-like DLBCL and activated B-like DLBCL. Findings suggested that patients, with germinal centre B-like DLBCL, had a significantly better overall survival rate than those with activated B-like DLBCL. This work indicated a significant methodology shift towards characterisation of cancers based on gene expression, rather than morphological, clinical and molecular variables.

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