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The use and analysis of microarray data

Key Points

  • Functional genomics is the study of gene function through the parallel expression measurements of genomes. The tools used to carry out these measurements most commonly include complementary DNA microarrays, oligonucleotide microarrays or serial analysis of gene expression (SAGE). Regardless of the specific technique, with the end result is 4,000–50,000 measurements of gene expression per sample. As a complete experiment might involve up to hundreds of microarrays, the resultant RNA expression data sets can vary greatly in size.

  • In addition to their use in basic research and target discovery, there are many other uses of functional genomics in drug discovery, including biomarker determination, pharmacology, toxicogenomics, target selectivity, development of prognostic tests and disease subclass determination.

  • Current methodologies to analyse RNA expression data sets can be roughly divided into two categories: supervised approaches, or analysis to determine genes that fit a specified pattern; and unsupervised approaches, or analysis looking for characterization of the components of a data set, without the a priori input of a training signal.

  • Hierarchical clustering is particularly advantageous in representing all the expression patterns seen in an experiment in a compact way. Self-organizing maps provide a two-dimensional visual survey of expression patterns with fewer computational requirements compared with hierarchical clustering. Relevance networks provide networks constructed from pairs of genes with strong positive or negative correlation, and can include phenotypic measurements. Principal components are used for visualization, by displaying samples on coordinate axes that capture the most variance in the data.

  • Nearest-neighbour methods find those genes that are most similar to an ideal gene pattern. Support vector machines are used to separate biological samples from differing conditions or diseases, by finding a plane to separate them in a higher-dimensional feature-rich space.

  • Challenges after analysis can include linking probes to genes and other biological knowledge, a process that never ends. Operationally, one is never done analysing a set of microarray data. The analysis of microarray data sets in a setting devoid of biological knowledge will be less rewarding than tapping into that knowledge. Finally, in the application of functional genomics to drug discovery, to extract the most information from microarrays, an open mind is needed with regard to the choices of analytical methods, using supervised methods, unsupervised methods and methods yet to be invented.

Abstract

Functional genomics is the study of gene function through the parallel expression measurements of genomes, most commonly using the technologies of microarrays and serial analysis of gene expression. Microarray usage in drug discovery is expanding, and its applications include basic research and target discovery, biomarker determination, pharmacology, toxicogenomics, target selectivity, development of prognostic tests and disease-subclass determination. This article reviews the different ways to analyse large sets of microarray data, including the questions that can be asked and the challenges in interpreting the measurements.

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Figure 1: Schematized experimental process using a microarray.
Figure 2: Dissimilarity measures.
Figure 3: Clustering and network-determination methods used in microarray analysis.

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Acknowledgements

The author wishes to thank T. Deshpande, A. Kho, M. Ramoni and I. Kohane for critical comments and interesting discussions on the manuscript. During the writing of this work, the author has been funded by and wishes to thank the Endocrine Fellows Foundation, the Genentech Centre for Clinical Research and Education, the Lawson Wilkins Paediatric Endocrinology Society, the Harvard Centre for Neurodegenerative Research and the Merck–Massachusetts Institute of Technology partnership. The author was also supported in part by grants from the National Heart, Lung and Blood Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, and the National Institute of Neurological Disorders and Stroke.

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DATABASES

Cancer.gov

acute lymphocytic leukaemia

acute myelogenous leukaemia

LocusLink

p53

FURTHER INFORMATION

CardioGenomics

Eisen's laboratory

GeneCluster 2.0

National Cancer Institute

RELNET

Glossary

SPLINES

Instead of fitting a complex polynomial curve to data, splines allow the fitting of data by putting together smaller, less complex curves.

NORTHERN BLOT

Different RNA molecules are separated by mass on a gel, then radioactively labelled complementary DNA or RNA molecules are used to quantify specific RNA amounts.

REVERSE TRANSCRIPTION

The synthesis of a strand of DNA from RNA, which is used to make a complementary DNA copy of sample RNA.

BAYESIAN NETWORK

A graphical representation in which variables (that is, genes) are represented as nodes. Arrows between nodes represent conditional dependence, which is interpretable as causal associations.

PEARSON CORRELATION COEFFICIENT

A measurement of the degree of fit of a linear-regression line to data points, calculated as the average distance of points from the regression line normalized to the standard deviations of the individual coordinates.

RANK CORRELATION COEFFICIENT

Points are restated in terms of their ordinal rank (for example, first, second, third) before calculation of the correlation coefficient.

DENDROGRAM

A visual representation of hierarchical clusters.

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Butte, A. The use and analysis of microarray data. Nat Rev Drug Discov 1, 951–960 (2002). https://doi.org/10.1038/nrd961

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